Journal of Environmental Management 91 (2010) 563–591
Contents lists available at ScienceDirect
Journal of Environmental Management
journal homepage: www.elsevier.com/locate/jenvman
Review
Environmentally conscious manufacturing and product recovery (ECMPRO):
A review of the state of the art
Mehmet Ali Ilgin a,1, Surendra M. Gupta b, *
a
b
Department of Industrial Engineering, Dokuz Eylul University, Buca 35160, Izmir, Turkey
Laboratory of Responsible Manufacturing, 334 SN Department of MIE, Northeastern University, Boston, MA 02115, USA
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 21 May 2009
Received in revised form
14 September 2009
Accepted 23 September 2009
Available online 22 October 2009
Gungor and Gupta [1999, Issues in environmentally conscious manufacturing and product recovery:
a survey. Computers and Industrial Engineering, 36(4), 811–853] presented an important review of the
development of research in Environmentally Conscious Manufacturing and Product Recovery (ECMPRO)
and provided a state of the art survey of published work. However, that survey covered most papers
published through 1998. Since then, a lot of activity has taken place in EMCPRO and several areas have
become richer. Many new areas also have emerged. In this paper we primarily discuss the evolution of
ECMPRO that has taken place in the last decade and discuss the new areas that have come into focus
during this time. After presenting some background information, the paper systematically investigates
the literature by classifying over 540 published references into four major categories, viz., environmentally conscious product design, reverse and closed-loop supply chains, remanufacturing, and
disassembly. Finally, we conclude by summarizing the evolution of ECMPRO over the past decade
together with the avenues for future research.
Ó 2009 Elsevier Ltd. All rights reserved.
Keywords:
Closed-loop supply chains
Disassembly
Environmentally conscious manufacturing
Environmentally conscious product design
Product recovery
Remanufacturing
Reverse logistics
1. Introduction
Gungor and Gupta (1999) presented a review addressing the
issues in Environmentally Conscious Manufacturing and Product
Recovery (ECMPRO). Since then, the area of ECMPRO has witnessed
a surge in research activity due to growing awareness in protecting
the environment (Gupta and Lambert, 2008). Although several
reviews have appeared since the publication of Gungor and Gupta’s
(1999) review, none of them considers all aspects of ECMPRO. This
paper presents a comprehensive summary of the ECMPRO literature that has been added since the publication of Gungor and
Gupta’s review.
Environmentally Conscious Manufacturing (ECM) deals with
green principles that are concerned with developing methods for
manufacturing products from conceptual design to final delivery to
consumers, and ultimately to the End of Life (EOL) disposal, that
satisfy environmental standards and requirements.
Recent discussions about global warming in various media
outlets have started to send warning signals to the masses about its
* Corresponding author. Tel.: þ1 617 3734846.
E-mail addresses: [email protected] (M.A. Ilgin), [email protected]
(S.M. Gupta).
1
Tel.: þ90 232 4127601.
0301-4797/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jenvman.2009.09.037
seriousness. The former U.S. Vice President Al Gore has been on
a crusade to make the general public aware of the importance of
protecting the environment. Because of this, the Norwegian Nobel
Committee recognized him with the 2007 Nobel Peace Prize for his
efforts to build up and disseminate greater knowledge about manmade climate change, and to lay the foundations for the measures
that are needed to counteract such change.
Environmental awareness and recycling regulations have been
putting pressure on many manufacturers and consumers, forcing
them to produce and dispose of products in an environmentally
responsible manner. These have created a need to develop algorithms, models, heuristics, and software for addressing designing,
recycling, and other issues (such as the economic viability, logistics,
disassembly, recycling, and remanufacturing) for an everincreasing number of products produced and discarded.
The purpose of this paper is to give an overview of the recent
literature on ECMPRO. As can be seen in Table 1, the scope of every
previous review is limited to a specific area in ECMPRO. In this
paper, we present a holistic view of ECMPRO by covering a wide
range of published work. The literature is organized into four main
areas, namely, product design, reverse and closed-loop supply
chains, remanufacturing and disassembly (see Fig. 1). In each main
area, papers are classified into appropriate subcategories. In Section
2, environmental practices and tools employed in product design
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Table 1
Previous reviews.
Review
Scope
Fleischmann et al., 1997
Moyer and Gupta, 1997
Reverse logistics
Recycling/disassembly efforts in the
electronics industry
Environmentally conscious design and
manufacturing
Reverse logistics
Disassembly planning
Remanufacturing
Production planning and control for
remanufacturing
Environmentally conscious manufacturing
and product recovery
Reverse logistics
Production planning and control, inventory
management and control, disassembly,
reverse logistics
Product recovery and reverse logistics
Disassembly planning and scheduling
Disassembly modelling, planning and
application
Disassembly algorithms and DFD guidelines
Disassembly sequence generation and
computer-aided design for disassembly
Disassembly sequencing
Inventory management in reverse logistics
Reverse logistics
Zhang et al., 1997
Carter and Ellram, 1998
O’Shea et al., 1998
Bras and McIntosh, 1999
Guide et al., 1999a
Gungor and Gupta, 1999
Fleischmann et al., 2000
Guide, 2000
Ferguson and Browne, 2001
Lee et al., 2001a
Tang et al., 2002
Desai and Mital, 2003b
Dong and Arndt, 2003
Lambert, 2003
Dong et al., 2005
Prahinski and
Kocabasoglu, 2006
Williams, 2006
Kim et al., 2007b
Sbihi and Eglese, 2007
Srivastava, 2007
Williams, 2007
Rubio et al., 2008
Sasikumar and Kannan,
2008a,b, 2009
Akcali et al., 2009
Chanintrakul et al., 2009
Pokharel and Mutha, 2009
Subramoniam et al., 2009
Electronics demanufacturing processes
Disassembly scheduling
Green logistics
Green supply chain management
Computer integrated demanufacturing,
recycling, disassembly
Reverse logistics
Reverse logistics
Network design for reverse and closed-loop
supply chains
Reverse logistics network design
Reverse logistics
Strategic planning factors for automotive
aftermarket remanufacturing
are discussed. The issues related to the design and management of
reverse and closed-loop supply chains are presented in Section 3.
Section 4 analyzes the operations management issues peculiar to
the remanufacturing facilities. A detailed analysis of EOL product
disassembly for remanufacturing and recycling is discussed in
Section 5. Finally, Section 6 presents some concluding remarks.
2. Environmentally conscious product design
Traditional product development aims at achieving improvements in design with respect to cost, functionality and manufacturability. However, increasing importance of the environmental
issues forces product designers to consider certain environmental
criteria in the design process. In order to help product designers
make environmentally friendly design choices, a number of
methodologies have been developed. This section presents an
overview of these methodologies by organizing them into three
categories, viz., Design for X, life cycle analysis and material
selection.
2.1. Design for X
Design for X (DFX) involves different design specialties such as
design for manufacture, design for quality etc. Meerkamm (1994),
Veerakamolmal and Gupta (2000) and Kuo et al. (2001) give good
overviews of the concepts, applications, and perspectives of various
DFX methods. For detailed information on DFX, we refer the reader
to the book by Huang (1996). Since this review’s emphasis is on the
environmental issues, we only consider the ECM and product
recovery related DFX tools, namely, Design for Environment, Design
for Disassembly and Design for Recycling.
2.1.1. Design for environment
Design for environment (DFE) is the design of products in a way
that the potential environmental impact throughout the life cycle is
minimized (Fiksel, 1996; Billatos and Basaly, 1997; Giudice et al.,
2006; Bevilacqua et al., 2007). Some researchers develop Quality
Function Deployment (QFD)-based DFE methodologies for the
simultaneous consideration of environmental criteria and
customer requirements. Cristofari et al. (1996) introduce a methodology called green QFD which considers quality requirements,
environmental impact, and production costs at the design phase.
Zhang et al. (1999) improve the green QFD by developing a new
methodology called green QFD II which combines Life Cycle Analysis (LCA) and Life Cycle Costing (LCC) into QFD matrices and
provides a mechanism to deploy customer, environmental and
costing requirements throughout the entire product development
process. Green QFD III proposed by Mehta and Wang (2001)
simplifies the detailed LCA and complex product comparison
algorithm of Green QFD II. Santos-Reyes and Lawlor-Wright (2001)
develop a four-phase methodology. In phase 1 and phase 2, eco
profile strategies are defined and prioritized using Analytic Hierarchy Process (AHP). In phase 3 and phase 4, eco-performance
strategies are identified and associated with eco profile strategies
using QFD. Madu et al. (2002) present a step by step approach for
environmentally conscious design. First, AHP is used to prioritize
customer requirements. Then, QFD is used to match design
requirements to customer requirements. A cost-effective design
plan is finally developed by applying Taguchi experimental design
and Taguchi loss function. Kuo and Wu (2003) first apply QFD to
translate customer needs into the six categories of environmentally
technical measures. Then Grey Relational Analysis (GRA) is
employed in this QFD matrix to determine the best design alternative based on the product’s life cycle, i.e. raw material,
manufacturing, assembly, disassembly, transportation, customer
usage, and disposal. Bovea and Wang (2003) introduce an approach
for identifying environmental improvement options by taking
customer preferences into account. The LCA methodology is
applied to evaluate the environmental profile of a product while
a fuzzy approach based on the House of Quality in the QFD methodology provides a more quantitative method for evaluating the
imprecision of the customer preferences. Masui et al. (2003)
develop a QFD-based four-phase methodology for DFE in the early
stage of new product development process. The most important
components in product design are identified in phase 1 and phase
2. Phase 3 and phase 4 involve the determination of the most
suitable design changes among various candidates with respect to
environmental improvement. Sakao (2007) extends the Masui et al.
(2003) by integrating LCA and TRIZ (Theory of Inventive Problem
Solving) into QFD. Bovea and Wang (2007) propose a DFE methodology which integrates QFD, LCA, LCC and contingent valuation
techniques for the evaluation of the customer, environmental, cost
criteria and customer willingness-to-pay, respectively.
The most commonly used technique in DFE methodologies to
assess the environmental impact is Life Cycle Analysis (LCA)
(Veerakamolmal and Gupta, 2000). Grote et al. (2007) integrate
TRIZ, DFX tools and LCA to develop a DFE methodology. Bevilacqua
et al. (2007) present a product design methodology by integrating
DFE and LCA. LCA is also an integral part of various QFD-based DFE
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565
Fig. 1. Classification of issues in environmentally concious manufacturing and product recovery.
methodologies (Zhang et al., 1999; Mehta and Wang, 2001; Sakao,
2007; Bovea and Wang, 2003, 2007).
There are also studies in literature that use fuzzy logic (FL) to
handle imprecise and vague information in the early design stage.
Kuo et al. (2006) use AHP to construct the hierarchical structure of
the environmentally conscious design indices. Then, fuzzy multiattribute decision-making model is used to select the best design
alternative. Li et al. (2008b) employ a fuzzy connected graph to
represent the product structure while AHP is used to convert life
cycle environmental objectives along with other functional and
production concerns into fuzzy relationship values. Using the fuzzy
graph a graph-based clustering algorithm recommends a modular
design based on minimization of the difficulty involved in disassembly and recycling of the EOL products. Qian and Zhang (2009)
develop a methodology for environmentally conscious modularity
assessment of electromechanical products by using fuzzy AHP.
A number of tools were developed for the evaluation of product
designs with respect to environmental criteria. Green Design
Advisor (GDA) proposed by Feldmann et al. (1999) considers
metrics related to product information (e.g. number of materials),
materials used in the product (e.g., toxicity), disassembly and
recyclability of the product (e.g., time for disassembly). Then, an
overall score for environmental impact is obtained by combining all
metrics using multi-attribute value theory. Lye et al. (2002) develop
a computer-based design evaluation tool, called ECoDE, to assess
the environmental impact of components in a product. ECoDE
employs AHP for the comparison and ranking of each criterion. The
scores against each of the criteria are calculated by using a multi
criteria rating technique for both components and the overall
product. A product or component with a large score has a less
severe impact on the environment. Knight and Curtis (2002)
describe a software tool which quantifies the economic and environmental effects of the disassembly by simulating the disassembly
process. Hopkinson et al. (2006) present the application of a DFE
software in a rapid manufacturing environment. Mathieux et al.
(2008) develop a recovery-conscious design method for the multi
criteria and quantitative analysis of the recoverability of complex
products. For an overview of DFE tools and strategies we refer the
reader to Kalisvaart and van der Horst (1995) and Bhamra (2004).
Park and Tahara (2008) simultaneously consider the quality,
environmental and customer satisfaction related aspects of products by using Producer-Based Eco Efficiency (PBEE) and ConsumerBased Eco Efficiency (CBEE). PBEE is used to identify the key issues
of a product as related to product quality and environmental impact
whereas consumer satisfaction and environmental impact related
product characteristics are identified by using the CBEE. Data
Envelopment Analysis (DEA) is used to quantify product values to
the producers and consumers into one single value and the LCA was
used to quantify the environmental impact of a product. Mascle and
Zhao (2008) use FL and feature modeling to evaluate parts together
with the disassembly efficiency and liability. Platcheck et al. (2008)
define a new product development methodology comprising of
four phases: briefing phase, development phase, projectation phase
and communication phase. They insert environmental criteria into
the different phases of the methodology.
Boks and Stevels (2007) emphasize the fact that dissemination
of DFE information requires the consideration of the intended
audience and relevant contexts. Johansson et al. (2007) investigate
the different mechanisms for their potential to support integration
between product designers and environmental specialists.
2.1.2. Design for disassembly
Design for disassembly (DFD) can be defined as the consideration of the ease of disassembly in design process (Veerakamolmal
and Gupta, 2000). Kroll and Hanft (1998) present a method for the
evaluation of the ease of disassembly by using a spreadsheet- like
chart and a catalog of task difficulty scores. The scores are determined based on the work-measurement analyses of standard
disassembly tasks. Veerakamolmal and Gupta (1999) introduce
Design for Disassembly Index (DfDI) to measure the design
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efficiency. DfDI is calculated by using a disassembly tree which
allows the identification of precedence relationships that define the
structural constraints in terms of the order in which components
can be retrieved. Kroll and Carver (1999) try to develop time-based
DFD metrics to be used for comparing alternative designs of the
same product. Das et al. (2000) estimate disassembly cost and effort
by calculating a disassembly effort index comprising of seven
factors: time, tools, fixture, access, instruct, hazard, and force
requirements. Chen (2001) uses axiomatic design to develop integrated design guidelines and an evaluation score for the ease of
disassembly and recycling called Integrated Disassembly and
Recycling Score (IDRS). Ferrer (2001) proposes a framework for the
determination of the disassembly and recovery process of a product
by developing economic measures of recyclability, disassemblability and reusability. Desai and Mital (2003a) and Mital and Desai
(2007) develop a methodology to enhance the disassemblability of
products. They define disassemblability in terms of several factors
such as exertion of manual force for disassembly, degree of precision required for effective tool placement, weight, size, material
and shape of components being disassembled, use of hand tools,
etc. Time-based numeric indices are assigned to each design factor.
A higher score indicates anomalies in product design from the
disassembly perspective. Desai and Mital (2005) propose a quantitative DFD methodology by considering numerous ergonomic and
conventional design attributes. Villalba et al. (2004) use a recyclability index of materials to determine if it is economically feasible to
disassemble a product. Banda and Zeid (2006) present a computational methodology that enables designers to perform disassembly
cost analysis in the design phase of a product. Gungor (2006) uses
Analytic Network Process (ANP) to evaluate alternative connection
types from a DFD perspective. Giudice and Kassem (2009) propose
a DFD methodology for characterizing the disassembly depths of
product components with respect to their need for removal and
recovery at EOL.
As an alternative to the index-based approaches to DFD, Viswanathan and Allada (2001) emphasize the importance of product
configuration in DFD. They propose a formal model, called the
Configuration-Value (CV) model, to evaluate and analyze the effect
of configuration on disassembly. In a follow-up paper, Viswanathan
and Allada (2006) develop a model for the combinatorial configuration design optimization problem. Design solutions proposed by
the model are tested by using a hierarchical evolutionary
programming-based algorithm. Kwak et al. (2009) develop a novel
concept, called ‘‘eco-architecture analysis’’ in which a product is
represented as an assembly of EOL modules. Optimal EOL strategy
is developed by determining the most desirable eco-architecture.
Chu et al. (2009) propose a CAD-based approach that can automatically generate a variant of 3D product structure by modifying
the combination of parts, assembly method and assembly
sequence. A Genetic Algorithm (GA)-based computing scheme is
employed to determine an optimal product structure from the
design alternatives generated by the approach.
2.1.3. Design for recycling
Design for recycling (DFR) focuses on the design attributes
which support the cost-effective recycle and disaggregation of the
materials embodied in the product (Masanet and Horvath, 2007).
Coulter et al. (1998) present a simple metric in order to determine
the appropriate separation process in the early design phase.
Knight and Sodhi (2000) develop mathematical models which
allow the evaluation of products for bulk recycling by determining
the cumulative net profit/cost as materials separation proceeds. Liu
et al. (2002) integrate AHP and Neural Network (NN) to develop
a procedure for recyclability assessment. Boon et al. (2002) explore
the electronic goods recycling infrastructure to identify the
conditions required for profitable recycling of PCs. Ardente et al.
(2003) present a computer-based tool called ENDLESS to calculate
a ‘‘Global Recycling Index’’ based on energy, environmental, technical and economic indicators. Wright et al. (2005) try to improve
the recyclability of a fiber optic cable design using a methodology
called CHAMP (Chain Management of Materials and Products).
CHAMP involves the evaluation of a design for both economic and
environmental impacts on a life cycle basis. Pento (1999) shows
that improved recyclability of paper reduces the amount of waste
generated. The DFR methodology proposed by Ferrao and Amaral
(2006) allows for the identification of economically optimum
recycling strategies while satisfying given recycling and reuse rates.
Masanet and Horvath (2007) develop an analytical framework to
quantify the economic and environmental benefits of DFR practices
for plastic computer enclosures during the design process. Houe
and Grabot (2007) develop a prototype system for the conversion of
the recyclability norms in textual form into constraints which can
be propagated through the product structure in order to identify
the inconsistencies between the present design and a given norm.
2.2. Life cycle analysis
Life cycle analysis (LCA) is a method used to evaluate the environmental impact of a product through its life cycle encompassing
extraction and processing of the raw materials, manufacturing,
distribution, use, recycling, and final disposal. Majority of the
researchers use LCA within a DFE methodology as a tool to measure
the environmental impact of a product’s design (Zhang et al., 1999;
Mehta and Wang, 2001; Bovea and Wang, 2003, 2007; Bevilacqua
et al., 2007; Boks and Stevels, 2007; Sakao, 2007; Grote et al., 2007).
Researchers used LCA as a part of different methodologies
besides DFE. Kuo (2006) analyzes disassembly bills of material
using LCA to evaluate the disassemblability and recyclability in
design phase. Zhu and Deshmukh (2003) apply Bayesian decision
networks to determine the effect of design decisions on the life
cycle performance of a product, including the environmental
friendliness. Arena et al. (2003) use LCA to evaluate alternative solid
waste management options. Nakamura and Kondo (2006) present
a new methodology of LCC which gives the cost and price counterpart of the hybrid LCA tool proposed by Nakamura and Kondo
(2002). Li et al. (2008a) combine NN with LCA to forecast the
environmental impact of a product.
Another LCA-assisted methodology is Life Cycle Engineering
(LCE) which can be defined as the design of product life cycle by
making choices on product concept, structure, materials and
processes. The environmental and resource consequences of these
choices are evaluated using LCA (Giudice et al., 2006). For more
information on LCE, we refer the reader to the books by Hundal
(2001) and Giudice et al. (2006).
2.3. Material selection
Selection of materials for a particular application is affected
mainly by mechanical factors including weight, processability. Cost
is also an important criterion. However, in recent years, environmental factors have also been considered in material selection at an
increasing rate. In order to deal with the environmental criteria in
design process, a number of tools and methodologies were
presented by researchers. Holloway (1998) extends a conventional
material selection technique, material selection charts, by integrating environmental concerns into it. Giudice et al. (2005)
develop a systematic method which minimizes the environmental
impact of the selected materials while satisfying the functional and
performance requirements. Tseng et al. (2008) carry out a green
material cost analysis to recommend materials that cause less
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pollution. Chan and Tong (2007) develop a GRA-based Multi
Criteria Decision Making (MCDM) approach which considers EOL
product strategy together with economical and technical characteristics of the materials. Zhou et al. (2009) integrate NN and GAs
for material selection based on multiple factors including the
weight, cost and environmental impact of products. The method
developed by Huang et al. (2009a) considers traditional design
factors together with life cycle environmental impact.
Recyclability of selected materials is an important factor
considered by various researchers. Sodhi et al. (1999) and Knight
and Sodhi (2000) develop mathematical models that can be used
for the recycling-based evaluation of material content of product
designs in the early design phase. Villalba et al. (2002, 2004)
propose a recyclability index as a tool for the evaluation of
a product design for material recovery.
Some studies investigate the recycling infrastructure to help
designers in selecting the materials suitable for recycling. Isaacs
and Gupta (1997) use Goal Programming (GP) to investigate the
effect of lighter materials on the profitability of the recycling
infrastructure. In a follow-up study, Boon et al. (2003) expand
Isaacs and Gupta’s (1997) mathematical formulation for the recycling Infrastructure to assess the materials streams and process
profitabilities of several different clean vehicles with different
material content. Boon et al. (2001) present a similar analysis for
aluminum-intensive vehicles. Bandivadekar et al. (2004) investigate the material flows and economic exchanges within the automotive recycling infrastructure. Williams et al. (2007) analyze the
sensitivity of a recycler’s profit to reprocessing and shipment
decisions taken under different ferrous and nonferrous metal prices
and different EOL vehicle compositions. Kumar and Sutherland
(2008) give a good overview of studies on automotive recovery
infrastructure and identify future challenges.
3. Reverse and closed-loop supply chains
Reverse Logistics (RL) involves all the activities associated with
the collection and either recovery or disposal of used products.
Strict environmental regulations and diminishing raw material
resources have intensified the importance of RL at an increasing
rate. In addition to being environment friendly, effective management of RL operations leads to higher profitability by reducing
transportation, inventory and warehousing costs. Moreover, RL
operations have a strong impact on the operations of forward
logistics such as the occupancy of the storage spaces and transportation capacity. Due to this high level interdependence, in
recent years, the closed-loop supply chains, which involve the
simultaneous consideration of forward and reverse flows, have
become an attractive alternative for the cost-effective management
of RL operations. Note that in this paper we use the terms reverse
logistics and reverse supply chains interchangibly. A vast majority
of studies on reverse and closed-loop supply chains involve the
network design problem. The literature also involves studies on
transportation problems, selection of used products, selection and
evaluation of suppliers, performance measurement, marketingrelated issues, EOL alternative selection and product acquisition
management.
3.1. Network design
In general, network design models can be classified into two
categories: deterministic and stochastic. While deterministic
models ignore uncertainty associated with RL and closed-loop
networks in model building, stochastic models integrate the
uncertain characteristics of RL and closed-loop networks into
modeling process.
567
3.1.1. Deterministic models
Most of the deterministic models consider only reverse flows.
Mixed Integer Linear Programming (MILP) is the most commonly
used modeling technique. Barros et al. (1998) propose a two-level
location model for recycling sand from construction waste. A
heuristic procedure based on a linear relaxation strengthened by
valid inequalities to generate a lower bound is proposed. Louwers
et al. (1999) use a non-linear programming model and a linear
approximation solution procedure to determine the location and
size of regional recycling centers for a carpet waste management
network. Realff et al. (1999, 2000a) propose MILP models for the RL
network design for the carpet recycling. Krikke et al. (1999b)
develop an MILP model for the design of a RL network for a copier
manufacturer. Jayaraman et al. (1999) propose a binary mixed
integer programming model to determine the location of remanufacturing/distribution facilities and to find the optimum quantities of transshipment, production and stocking for cores and
remanufactured products. Fleischmann et al. (2000) investigate the
general characteristics of product recovery network design
problem based on the case studies from different industries. Shih
(2001) develops a mixed integer programming (MIP) model to
design a RL system for recycling computers and home appliances.
Hu et al. (2002) develop a discrete-time linear analytical model for
a multi-time-step, multi-type hazardous-waste RL system based on
the minimization of total RL operating costs. Schultmann et al.
(2003) combine facility location planning and flow-sheeting-based
process simulation for the design of a spent-battery RL network.
Jayaraman et al. (2003) propose models and solution procedures to
develop an efficient strategy for a RL network designed for
hazardous products. The aim of the study is to determine the
number and location of the collection centers and refurbishing sites
and the corresponding flow of hazardous products. The proposed
heuristic concentration procedures combined with heuristic
expansion components have the ability of solving relatively large
problems with up to 40 collection sites and 30 refurbishment sites.
However, their model and solution procedures ignore the multiple
period problems, freight rate discounts and inventory cost savings
resulting from consolidation of returned products. Wang and Yang
(2007) try to develop better heuristics for solving the locationallocation problem of recycling e-waste by comparing their
heuristics with the heuristics proposed by Jayaraman et al. (2003).
Their MIP model is based on the model proposed by Shih (2001).
They also exploit the real – world parameters of Taiwan e-waste
recycling industry used by Shih (2001). Amini et al. (2005) use
binary integer programming to design the RL network for the repair
operations of a major international medical diagnostics manufacturer. Du and Evans (2008) setup an MIP optimization model for the
design of the RL network for a third party logistics company
providing logistics service for the post sale service operations of
a manufacturing company. A solution approach is proposed by
combining three search algorithms: scatter search, the dual
simplex method and the constraint method. Pati et al. (2008)
formulate a mixed integer GP model to determine the facility
location, route and flow of different varieties of recyclable wastepaper in the multi-item, multi echelon and multi-facility decisionmaking framework. Srivastava (2008a,b) present a multi period
two-level hierarchical optimization model for RL network design
problem. The first MILP optimization model determines the
opening decision for collection centers based on the minimization
of investment (fixed and running costs of facilities and transportation costs). Disposition decisions, location and capacity addition decisions for rework sites at different time periods together
with the flows to them from collection centers are determined by
the second MILP optimization model based on maximization of
profit. Min and Ko (2008) develop an MIP model together with a GA
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in order to solve the location-allocation problem related to the
repair facilities of a third party logistics service provider. Lee et al.
(2009) develop a novel GA to solve the mathematical model associated with a RL network. Dehghanian and Mansour (2009) propose
a multi objective GA to design a product recovery network. Pishvaee et al. (in press) use Simulated Annealing (SA) to solve the MILP
model associated with a RL network design problem.
In recent years, simultaneous consideration of reverse and
forward flows has become a popular approach to the deterministic
network design problem. Fleischmann et al. (2001) propose an
MILP-based generic Recovery Network Model (RNM) to compare
the traditional logistic design with the simultaneous design of
forward and reverse network. After applying their model to two
case studies from the literature, they state that for many cases
product recovery can be integrated with existing logistic structure
in an efficient way while other cases require the integrated design
of reverse and forward logistic networks. Salema et al. (2007) point
out that Fleischmann et al. (2001) do not take into consideration
some important problems of real RL networks such as capacity
limits on production/storage, multi product production, and
uncertainty in demand/return flows. In order to deal with these
shortcomings, they extend the RNM and develop an MILP formulation for a capacitated multi product RL model by considering
forward flows. In another paper, Salema et al. (2005) present a twolevel approach for the same problem. Beamon and Fernandes
(2004) propose a multi period MILP model for a closed-loop supply
chain to determine the location and sorting capabilities of warehouses together with the amount of material to be transported
between each pair of sites. Sim et al. (2004) use a Linear
Programming (LP)-based GA for the design of a closed-loop supply
chain. Sheu et al. (2005) propose a linear optimization model that
considers forward and reverse logistics flows. Zhou et al. (2005)
integrate a Mixed Integer Non-Linear Programming (MINLP) model
and GA in order to solve a distribution problem with forward and
reverse flows. Lu and Bostel (2007) use a lagrangian heuristic
approach to solve a location problem of a remanufacturing RL
network considering forward flows. Ko and Evans (2007) construct
an MINLP model and propose a GA-based heuristic procedure for
the forward/reverse network design problem of a third party
logistics provider. Uster et al. (2007) use Benders decomposition
with alternative multiple cuts to propose an exact solution for the
large scale MILP model associated with a closed-loop supply chain
network. Wang et al. (2007) present a bi-level programming model
for a supply chain in order to jointly determine location and
inventory policies by considering product returns. Lee et al. (2007a)
propose a closed-loop supply chain network design model for
a Third Party Logistics Provider (3PL) by considering two objectives:
maximization of the returned products shipped from customers
back to the collection facilities and minimization of the total costs
associated with the forward and reverse logistics operations. After
developing a compromise solution using fuzzy GP, a GA is
employed to solve the problem. Lee et al. (2007b) develop a GA to
solve the MILP model associated with the closed-loop supply chain
design of a 3PL. Lee and Dong (2008) integrate Tabu Search (TS) and
network simplex algorithm to solve the location-allocation
problem of an end-of-lease computer recovery network. Demirel
and Gökçen (2008) develop an MIP model to determine the optimal
manufacturing, remanufacturing, and transportation quantities
together with the optimal locations of dissassembly, collection and
distribution facilities. Mutha and Pokharel (2009) propose a mathematical model for the design of a multi echelon closed-loop supply
chain involving five retailers, four warehouses, three reprocessing
centers, five spare part markets, three factories, one recycling
center, one disposal site, six new module suppliers and six distribution centers. Kannan et al. (2009a) use GA and particle swarm
optimization in order to design a multi echelon closed-loop supply
chain in a build to order environment. Wang and Hsu (2010)
employ a spaning-tree-based GA to solve an IP model associated
with the design of a closed-loop supply chain. Salema et al. (in
press) use a graph approach based on the conventional concepts of
nodes and arcs to develop a generic model for the simultaneous
design and planning of supply chains with reverse flows. Kannan
et al. (in press) propose an MILP model for a closed-loop supply
chain network design problem and then develop a GA-based
heuristic as a solution methodology. Yang et al. (2009) use theory of
variational inequalities to formulate and optimize the equilibrium
state of a closed-loop supply chain network.
Some researchers focus only on the collection of used products
from the consumers. Bautista and Pereira (2006) formulate the
collection point location problem as a set covering and MAX-SAT
problem. They develop GA and GRASP (Greedy Randomized
Adaptive Search Procedure) methodologies to solve the set
covering and MAX-SAT formulation, respectively. Min et al. (2006a)
present a non-linear integer program for solving the multi echelon
RL problem involving product returns without considering
temporal consolidation issues in a multiple planning horizon. Min
et al. (2006b) present a mixed integer non-linear model to determine the number and location of initial collection points and
centralized return centers. The model involves the determination of
the exact length of holding time for consolidation at the initial
collection points and total RL costs associated with product returns
in a multiple planning horizon. A solution procedure based on GA is
proposed to solve the model. Wojanowski et al. (2007) develop an
analytical model for the collection facility network design and
pricing policy by considering the impact of the deposit refund on
the sales rate and return rate. Aras and Aksen (2008) propose an
MINLP model for collection center location problem with distance
and incentive-dependent returns under a drop-off policy. Aras et al.
(2008) extend Aras and Aksen’s (2008) model by considering
a pickup policy with capacitated vehicles. Cruz-Rivera and Ertel
(2009) construct an uncapacitated facility location model in order
to design a collection network for EOL vehicles in Mexico. de Figueiredo and Mayerle (2008) develop an analytical model with the
associated algorithmic solution procedure to design minimum-cost
recycling collection networks with required throughput.
Instead of determining the location of collection centers in an
optimal way, Tuzkaya and Gülsün (2008) propose an integrated
ANP-fuzzy technique for the evaluation of potential collection
center locations. Bian and Yu (2006) use AHP in order to evaluate
the alternative RL operation locations for an international electrical
manufacturer. Pochampally and Gupta (2008) integrate AHP and
fuzzy set theory to determine potential facilities from a set of
candidate recovery facilities. Kannan et al. (2008) investigate the
use of AHP and fuzzy AHP for selecting the collection center location in a RL network. Pochampally and Gupta (2009) employ a fourphase approach to evaluate the efficiencies of collection and
recovery facilities, viz. (1) identification of criteria for evaluation of
the facilities of interest, (2) use of fuzzy ratings of existing facilities
to construct a neural network that gives the importance value for
each criterion, (3) employment of a fuzzy TOPSIS (Technique for
Order Preference by Similarity to Ideal Solution) approach to obtain
the overall ratings of the facilities of interest, and (4) employment
of Borda’s choice rule to calculate the maximized consensus ratings
of the facilities of interest.
Determination of the appropriate reverse channel structure for
the collection of used products from customers is an important
issue in RL network design. Savaskan et al. (2004) investigate the
reverse channel structure selection problem for a single manufacturer single retailer case. Savaskan and Van Wassenhove (2006)
extend Savaskan et al. (2004) by considering two retailers in
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a competitive retailing environment. Bhattacharya et al. (2006)
analyze the impact of centralized and decentralized channel
structures in the optimal order quantity of a retailer from a manufacturer which makes new products and orders remanufactured
products from a remanufacturer. Karakayali et al. (2007) analyze
centralized as well as remanufacturer- and collector-driven
decentralized channels. Walther et al. (2008) develop a negotiation-based coordination mechanism which allows for the decentralized assignment of recycling tasks to the companies of
a recycling network. Hong et al. (2008) compare centralized and
decentralized decision making in a RL system. In centralized model,
a decision maker gives decisions for the entire system while the
decentralized model involves several independent entities individually operated by self-interested parties.
3.1.2. Stochastic models
Design of reverse and closed-loop supply chain networks
involves a high degree of uncertainty associated with quality and
quantity of returns. In order to deal with this uncertainty,
researchers developed various stochastic models. Robust optimization is a commonly used technique to deal with the uncertainty
in RL network design. Realff et al. (2000b, 2004) propose a robust
MILP model to support decision-making for RL network design. The
model can search for solutions close to the mathematically optimal
solutions for a set of alternative scenarios identified by a decision
maker. Hong et al. (2006) also develop a robust MILP model based
on the maximization of the system net profit for specified deterministic parameter values in each scenario. A min–max robust
optimization methodology is employed to find a robust solution for
all of the scenarios.
Stochastic programming is a popular alternative to robust
optimization. Listes and Dekker (2005) propose a stochastic
programming-based approach for the sand recycling case study
given in Barros et al. (1998). A generic stochastic model is developed by Listes (2007) for closed-loop supply chains. Lee et al.
(2007c) develop a stochastic programming-based approach for
a product recovery network design problem. Chouinard et al.
(2008) consider the randomness related with recovery, processing
and demand volumes in a closed-loop supply chain design problem
by developing a stochastic programming model. They use a sample
average approximation-based heuristic to solve the problem. Lee
and Dong (2009) propose a stochastic programming model to deal
with the location and allocation decisions of a RL network under
uncertainty. They integrate sample average approximation and SA
in order to solve this model.
Lieckens and Vandaele (2007) propose an MINLP model by
integrating a conventional RL MILP model with a queueing model
to deal with the dynamic and stochastic aspects of RL networks. A
GA-based technique, Differential Evolution, is used to solve the
MINLP model.
Qin and Ji (in press) use fuzzy programming to deal with the
uncertainty associated with the design of RL networks. They integrate fuzzy simulation and GA for the design of a RL network.
3.2. Simultaneous consideration of network and product design
issues
Some researchers develop reverse or closed-loop supply chain
network design models by explicitly considering the issues related
with product design. Krikke et al. (2003) develop an MILP model to
support decisions on both design structure of a product, i.e.
modularity, reparability and recyclability, and the design structure
of the logistic network. Fernandez and Kekale (2005) investigate
the impact of the product modularity on RL strategy. The role of
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product modularity on closed-loop supply chain design is studied
by Krikke et al. (2004).
3.3. Optimization of transportation of goods
Efficient and effective planning of transportation activities is
a crucial factor in the cost-effective management of RL networks. A
majority of the studies in literature try to determine the vehicle
routes in RL networks using different versions of well-known
Vehicle Routing Problem (VRP). Some researchers consider only
return flows. Mourao and Almeida (2000) and Mourao and Amado
(2005) provide heuristic methods in order to solve the mixed
capacitated arc routing problem of a refuse collection network.
Blanc et al. (2004) use a VRP to obtain reliable estimates of transportation costs in a recycling network redesign problem. Blanc et al.
(2006) propose a multi-depot pickup and delivery model with
capacitated vehicles and alternative delivery locations for the
collection of containers from EOL vehicle dismantlers in the
Netherlands. They solve the model using a heuristic developed by
integrating route generation and set partitioning. Schultmann et al.
(2006) develop a symmetric capacitated VRP to generate a tour
schedule with minimal cost for EOL auto RL network. TS is used to
find the optimum schedule. Krikke et al. (2008) combine route
generation and set partitioning to solve the VRP associated with the
low-frequency collection of materials disassembled from EOL
vehicles. Kim et al. (2009a) construct a VRP for an RL network in
South Korea and then propose a TS algorithm to solve the problem.
Route planning with integrated return and delivery flows was
also investigated by the researchers. Dethloff (2001) considers a RL
system in which customers have both pickup and delivery
demands. He models this system as a Vehicle Routing Problem with
Simultaneous Delivery and Pickup (VRPSDP). Then a heuristic
construction procedure is developed to solve the problem. Dethloff
(2002) uses the heuristic procedure proposed in Dethloff (2001) to
solve the VRP with backhauls. Gribkovskaia et al. (2007) model
VRPSDP as an MILP model and develop general solutions using
conventional construction and improvement heuristics and TS.
Alshamrani et al. (2007) examine the blood distribution network of
the American Red Cross in which products are delivered from
a central processing point to customers (stops) in one period are
available for return to the central point in the following period.
They develop a heuristic procedure to develop route design and
pickup strategies simultaneously.
3.4. Selection of used products
Although OEMs in some countries are obligated to take products
at the end of their useful life, many third party firms collect used
products to make profit. These firms select used products by
comparing the revenues from recycle or resale of products’
components and collection and reprocessing costs of the used
products (Pochampally et al., 2009c). The most commonly used
technique in selection of used product for reprocessing is
construction of a cost benefit function. Veerakamolmal and Gupta
(1999) propose a cost benefit function to select the best product for
reprocessing from a set of candidate used products. The value of
cost benefit function is calculated by subtracting sum of revenue
terms from the sum of cost terms. Pochampally and Gupta (2005)
and Pochampally et al. (2009b) modify this cost benefit function by
considering the probability of breakage and the probability of
missing components in the used product of interest. Then an
integer linear programming model is formulated based on the
maximization of the modified cost benefit function. Pochampally
and Gupta (2008) propose a fuzzy benefit function by considering
the uncertainty associated with revenues and costs. Cost benefit
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function technique is preferred when the evaluation criteria could
be presented in terms of classical numerical constraints. For the
case of presentation of evaluation criteria in terms of range of
different degrees of desirability, Pochampally et al. (2009c) present
a Linear Physical Programming (LPP) formulation.
3.5. Selection and evaluation of suppliers
Due to the differences between the reverse and forward flows in
terms of the cost and complexity of transportation, storage and/or
handling operations, many firms outsource their RL operations to
Third Party Logistics Providers (3PLs) (Meade and Sarkis, 2002;
Efendigil et al., 2008). In order to support decision makers in
selection of the 3PLs, researchers developed multiple criteria
decision-making methodologies. Meade and Sarkis (2002) use ANP
to develop a conceptual model for selecting and evaluating 3PLs.
Presley et al. (2007) integrate Activity Based Costing (ABC),
balanced scorecard, AHP and QFD to select between two competing
3PLs. Efendigil et al. (2008) present a holistic approach based on the
NN and FL for selecting a 3PL in the presence of vagueness. Tsai and
Hung (2009) employ preemptive GP with environmental goals, ABC
goals, and supply chain goals to solve treatment supplier selection
problem of an electronic equipment manufacturer. Performance
weights of suppliers are calculated using AHP. Pochampally et al.
(2009b) propose a TOPSIS-based methodology to rate candidate
companies that collect and sell used products. The methodology
considers the concerns of three different categories of people:
consumers, local government officials and manufacturers. Kannan
et al. (2009b) use fuzzy TOPSIS and interpretive structural
modeling for the problem of selection of best 3PL. In order to solve
the same problem, Kannan (2009) employs AHP and fuzzy AHP
while Saen (in press) develops a DEA-based methodology.
Consideration of environmental factors in forward logistic
supplier selection problem was also studied by some researchers.
Lu et al. (2007) employ an AHP-based decision-making methodology for the performance measurement and evaluation of the
suppliers based on the environmental criteria. A FL process is also
integrated with the AHP to reduce subjective bias in designing
a weighting system.
Selection of feasible environmentally conscious manufacturing
or RL projects is a popular multi criteria decision-making problem
studied by the researchers. Sarkis (1998) evaluates environmentally
conscious business practices using ANP. Sarkis (1999) integrates
ANP and DEA to help the decision makers in evaluation of the
alternative environmentally conscious manufacturing programs.
Rao (2007) investigates the use of different methods including
Graph Theory and Matrix Approach, Simple Additive Weight
Method, AHP and its versions, TOPSIS and Modified TOPSIS method
by considering the problem presented in Sarkis (1999). Rao (2009)
proposes an improved compromise ranking method for the evaluation of environmentally conscious manufacturing programs. Ravi
et al. (2005) present an ANP and balanced scorecard-based
approach for the analysis of 3 RL alternatives, namely Third Party
Demanufacturing (TPD), Symbiotic Logistics Concept (SLC), and
Virtual Reverse Logistics Network (VRL) for PCs. Ravi et al. (2008)
integrate ANP and Zero One GP to evaluate alternative RL projects.
3.6. Performance measurement
Simulation is the most commonly used technique to study the
effect of different factors on the performance of a reverse or closedloop chain. Georgiadis and Vlachos (2004) present a System
Dynamics Simulation (SDS) model to analyze the long term
behavior of a closed-loop supply chain with respect to alternative
environmental protection policies concerning take-back obligation,
proper collection campaigns, and green image effect. Biehl et al.
(2007) develop a Discrete Event Simulation (DES) model for
a carpet RL supply chain and carry out an experimental design
study to analyze the impact of the system design factors together
with the environmental factors on the operational performance of
the RL system. Kara et al. (2007) model RL network of EOL white
goods in Sydney Metropolitan Area using DES. They carry out some
what-if analysis using the DES model to determine the most
important factors in the performance of the RL system. Georgiadis
and Besiou (2008) use a SDS model to examine the impact of
legislation, green image factor and DFE on the long term behavior of
a closed-loop supply chain with recycling activities.
Pochampally et al. (2009a) define metrics for the performance
evaluation of a reverse/closed-loop supply chain. They also propose
a QFD and LPP-based mathematical model to measure the performance of a reverse/closed-loop supply chain.
In some studies, the effect of green practices such as ISO 14000,
LCA on the performace of a supply chain is investigated. Sarkis and
Cordeiro (2001) use DEA to present the performance differences
between pollution prevention and end of pipe environmental
management policies. Sarkis and Talluri (2004) investigate the use
of DEA as an environmental performance measurement tool by
presenting an illustrative example. Wagner et al. (2001) give a good
overview of empirical studies investigating the relationship
between the environmental and economic performance. Hervani
et al. (2005) present an integrated framework for study, design and
evaluation of green supply chain management performance tools
based on the experiences, case studies and other literature related
to performance measurement in green supply chains. Kainuma and
Tawara (2006) propose a multiple attribute utility theory approach
integrating supply chain return on asset, customer satisfaction, and
LCA.
Country or region-based studies were also presented by the
researchers. Zhu and Sarkis (2004), Zhu et al. (2005, 2007) try to
analyze the relationship between the green supply chain
management practices and performance in China. Rao and Holt
(2005) study the effects of green supply chain practices on the
performance and competitiveness of a sample of organizations in
South East Asia.
3.7. Marketing-related issues
The most commonly studied marketing-related issues include
the pricing of manufactured and remanufactured products,
competition in remanufacturing and selection of an optimal return
policy. Guide et al. (2003) consider a remanufacturing system in
which the used phones with different quality levels are remanufactured to a single quality level and are sold at a certain price. The
selling price of remanufactured products could be completely
determined by the acquisition prices of returns since they assume
that demand and return flows perfectly matched. Demand is
assumed to be the function of the price. They develop a heuristic to
determine the optimal acquisition price of the used phones and
selling price of the remanufactured phones with the aim of maximizing the profit which is given as the difference between the total
revenue and acquisition and remanufacturing costs. Mitra (2007)
extends Guide et al. (2003) in four ways. First, he does not consider
acquisition prices by focusing on a manufacturer which is responsible to recover the returns. Second, more than one quality level is
considered for remanufactured products. Third, he considers
a probabilistic situation where not all items may be sold, and the
unsold items may have to be disposed of. Finally, demand is
modeled as a function of price and availability or supply of recovered goods. Vadde et al. (2007) investigate the pricing policies of
reusable and recyclable components for third party firms involved
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in discarded product processing under strict environmental regulations. Qiaolun et al. (2008) develop game theory-based models to
jointly determine the collecting, wholesale and retail prices in
a closed-loop supply chain. Qu and Williams (2008) compare two
hulk pricing strategies for an automotive shredder under constant,
increasing and decreasing trends for ferrous metal and hulk prices.
Liang et al. (2009) try to determine the acquisition price of the cores
in an open market based on the options framework and the
geometric Brownian motion followed by the sales price of the
cores. Karakayali et al. (2007) develop models to determine the
optimal acquisition price of EOL products and the selling price of
remanufactured parts by considering centralized as well as
remanufacturer- and collector-driven decentralized channels.
Mukhopadhyay and Setoputra (2004) propose a model to obtain
optimal policies for price and the return policy in terms of certain
market reaction parameters for e-business based on maximization
of profit. Debo et al. (2005) investigate the joint pricing and
production technology selection problem faced by an Original
Equipment Manufacturer (OEM) operating in a market where
customers differentiate between the new and the remanufactured
products.Vorasayan and Ryan (2006) model the sale, return,
refurbishment, and resale processes as an open queueing network
to find the optimal price and proportion of refurbished products.
Some researchers study the competition between an OEM and
an independent operator who may intercept product cores
produced by OEM to sell the remanufactured products in future
periods. Majumder and Groenevelt (2001) consider a two-period
model in which OEM may or may not remanufacture in the second
period. Ferrer and Swaminathan (2006) extend Majumder and
Groenevelt’s (2001) study to a multi period setting where the
independent operator competes with the OEM in the second and
subsequent periods. They also find closed form solutions for prices
and quantities and characterize an optimum solution region which
was numerically explored by Majumder and Groenevelt (2001).
Ferguson and Toktay (2006) try to determine the new product
pricing decisions and recovery strategy of an OEM in a two-period
model by assuming that OEM has an easier access to the used
product and average variable cost of remanufacturing increases
with the quantity remanufactured. Ferrer and Swaminathan (in
press) extend Majumder and Groenevelt (2001) and Ferguson and
Toktay (2006) studies by considering more than two periods with
differentiated remanufactured products. Webster and Mitra (2007)
and Mitra and Webster (2008) develop two-period models to
analyze the effect of take-back laws and government subsidies on
competitive remanufacturing strategy, respectively. Heese et al.
(2005) analyze a three-stage game involving sequential decisions of
two OEMs whether to takeback used products during the first two
stages. In the third stage, both firms simultaneously determine the
price for their new products together with the discount offered for
returned products.
An effective return policy can be used as a marketing tool to
increase sales. There are studies in the literature analyzing return
policies within the RL context. Mukhopadhyay and Setoputra (2005)
develop a profit maximization model to determine the optimal
return policy for build to order products. Yao et al. (2005) investigate
the role of return policy in the coordination of supply chain by using
a game theory-based methodology. Yalabik et al. (2005) develop an
integrated product returns model with logistics and marketing
coordination for a retailer servicing two distinct market segments.
Mukhopadhyay and Setaputra (2006) investigate the role of a Fourth
Party Logistics (4PL) as a return service provider, and propose
optimal decision policies for both the seller and the 4PL.
Robotis et al. (2005) investigate the use of remanufacturing as
a tool to satisfy the demand arising from secondary markets. They
point out that use of remanufacturing to satisfy the demand form
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secondary markets reduces the number of units procured from the
advanced market for the reseller.
3.8. EOL alternative selection
Determination of the best option for EOL products is an
important problem faced by OEMs. There are five commonly used
options for a product at its end of life: direct reuse, repair, remanufacturing, recycling, and disposal. Direct reuse involves the reuse
of the whole product as is for its original task. In repair option,
damaged parts are changed in order to have a fully-functional
product. Remanufacturing consists of refurbishment of used
products up to a quality level similar to a new product. The aim of
recycling is to recover materials from the returned products.
Disposal involves the landfill or incineration of the used products.
Development of a decision model to select between these options
requires the consideration of various qualitative and quantitative
factors such as environmental impact, quality, legislative factors,
cost, etc. Mathematical programming models were extensively
used by researchers to develop EOL option selection methodologies. Krikke et al. (1998) use stochastic Dynamic Programming (DP)
to determine a product recovery and disposal strategy for one
product type based on the maximization of net profit considering
relevant technical, ecological and commercial feasibility criteria at
the product level. In follow-up papers, Krikke et al. (1999a,b) apply
the methodology proposed in Krikke et al. (1998) to real life cases
on the recycling of copiers and monitors, respectively. Teunter
(2006) extends Krikke et al. (1998) by considering multiple disassembly processes and partial disassembly. Lee et al. (2001b)
determine the EOL option of each part by defining the objective
function as the weighted sum of economic value and environmental impact. Das and Yedlarajiah (2002) propose a mixed integer
program to determine the optimal part disposal strategy based on
the maximization of the net profit. Jorjani et al. (2004) develop
a piecewise linear concave program to determine the optimal
allocation of disassembled parts to five disposal options (refurbish,
resell, reuse, recycle, landfill) based on the maximization of the
overall return. Ritchey et al. (2005) develop a mathematical model
to evaluate the economic viability of remanufacturing option under
a government mandated take-back program. Willems et al. (2006)
use LP to investigate the effect of reductions in the expected
disassembly time and cost on the optimal EOL strategy. Tan and
Kumar (2008) use an LP model to evaluate three EOL options for
each part, namely, repair, repackage or scrap.
In some studies, Multi Criteria Decision Making (MCDM)
methodologies are presented. Hula et al. (2003) present a multi
objective GA to consider the trade-offs between environmental and
economic variables in the selection of EOL alternatives. Bufardi
et al. (2004) obtain a partial ranking of EOL options using the
ELECTRE III MCDM methodology. Chan (2008) extends Bufardi et al.
(2004) by developing a GRA-based MCDM methodology which
considers complete ranking of EOL options under uncertainty
environment. Jun et al. (2007) develop a multi objective evolutionary algorithm to select the best EOL options of parts for maximizing the recovery value of an EOL product including recovery
cost and quality. Fernandez et al. (2008) develop a fuzzy approach
to evaluate five recovery options and one disposal option by
considering four criteria: product value, recovery value, useful life
and level of sophistication. Wadhwa et al. (2009) propose a FLbased MCDM methodology to consider the knowledge of experts
(evaluators or sortation specialists) in the selection of most
appropriate alternative(s) for product reprocessing with respect to
existing criteria. Iakovou et al. (2009) develop an MCDM methodology, called ‘‘Multicriteria Matrix’’, which considers the residual
value, environmental burden, weight, quantity and ease of
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disassembly of each component in the evaluation of EOL alternatives for a product. Xanthopoulos and Iakovou (2009) use GP to
determine the most attractive subassemblies and components to be
disassembled for recovery from a set of different types of EOL
products.
Gonzalez and Adenso-Diaz (2005) propose a bill of materialbased method for the joint determination of depth of disassembly
and EOL option for the disassembled parts based on the maximization of the profit. Kleber (2006) tries to develop dynamic policies
for three different new product development projects (design for
single use, design for reuse, and design for reuse with stockkeeping) which differ with respect to EOL recovery strategy. Shih
et al. (2006) develop a Case Based Reasoning (CBR)-based methodology to determine the product EOL strategy. Staikos and Rahimifard (2007a,b) integrate AHP, LCA and cost benefit analysis to
determine the most appropriate reuse, recovery and recycling
option for postconsumer shoes. Rahimifard et al. (2004) and Bakar
and Rahimifard (2007) develop computer-aided decision support
systems to support the EOL option selection process.
Some studies simultaneously investigate the EOL option selection problem and product design. Rose and Ishii (1999) and Gehin
et al. (2008) develop tools to help product designers in the identification of appropriate EOL strategies in the early design phase.
Mangun and Thurston (2002) develop a mathematical model in
order to incorporate planning for component reuse, remanufacture,
and recycle into product portfolio design. Kumar et al. (2007)
present a value flow model considering different product life cycle
stages to support decision maker in selecting the best EOL option
for a product. Zuidwijk and Krikke (2008) try to improve product
recovery strategy by concerning the innovations in product design
and recovery technologies.
3.9. Product acquisition management
Highly uncertain nature of quantity, quality and timing of
returns requires effective policies for the acquisition of used
products. Uncontrolled acquisition of used products results in
excessive inventory levels or low customer satisfaction (i.e. stockouts due to insufficient used products). According to Guide and
Jayaraman (2000), product acquisition management acts as an
interface between RL activities and production planning and
control activities for firms. There are two most commonly used
product acquisition systems: waste stream system and the marketdriven system (Guide and Pentico, 2003; Guide and Van
Wassenhove, 2001). In waste stream, the firms encouraged by the
legislation passively accept all product returns from the waste
stream. On the other hand, market-driven system employs financial
incentives to encourage users to return their products to the firm.
Several different forms of financial incentives are used by
firms in market-driven system including deposit systems, cash
paid for a specified level of quality, credit toward a new unit
(Guide and Van Wassenhove, 2001). The implementation of
different forms of financial incentives and their impact on the
performance of the RL activities are the main research issues in
product acquisition management literature. Klausner and Hendrickson (2000) present an implementation of buy-back
programs in power-tools industry. Guide and Van Wassenhove
(2001) present a real life case study to illustrate the implementation of a quality-dependent incentive policy in which
pre-determined prices are offered for products with a specific
nominal quality level. Guide et al. (2003), Aras and Aksen (2008)
and Aras et al. (2008) determine optimal incentive values under
a quality-dependent incentive policy. Aksen et al. (2009) extend
Aras et al. (2008) by considering a government subsidized
collection system. Wojanowski et al. (2007) investigate the use of
a deposit refund system which requires payment of a certain
deposit at the time of purchase, which is refunded upon the
return of the used product. Kaya (2010) determines the optimal
incentive value in case of stochastic demand and partial substitution between original and remanufactured products.
Ostlin et al. (2008) investigate seven different types of closedloop relationships for the acquisition of used products. The
relationships identified are ownership-based, service contract,
direct-order, deposit-based, credit-based, buy-back and voluntarybased relationships.
3.10. Other issues
In addition to the issues covered in this section, some other
issues including the feasibility of outsourcing, the importance of
information technology, equipment leasing, cash flows and barriers
in RL were also studied by the researchers.
Serrato et al. (2007) develop a Markov decision model to test the
hypothesis that outsourcing in RL is more suitable in case of highly
variable returns. They report that there are sufficient conditions on
the cost parameters and the return fraction that guarantee the
existence of an optimal threshold policy for outsourcing. Chan
(2007) reports the results of a case study on the collaborative use of
returnable packaging materials between a manufacturer and an
OEM supplier. Krumwiede and Sheu (2002) develop a RL decisionmaking model which supports 3PLs in their assessment of the
feasibility of RL activities.
Dhanda and Hill (2005) investigate the role of information
technology in RL through a case study. Daugherty et al. (2005) point
out the importance of resource commitment to information technology in RL by analyzing a survey of businesses in the automobile
aftermarket industry.
Horvath et al. (2005) propose a Markov chain-based methodology to investigate the expectations, risks, and potential shocks
associated with cash flows arising from RL activities. They also
provide managerial guidelines for avoiding liquidity problems
associated with RL activities.
Sharma et al. (2007) develop an MILP model for the simultaneous consideration of RL decisions with equipment leasing ones.
After validating the model, they present potential applications of
the model for alternative scenarios.
Ravi and Shankar (2005) use Interpretive Structural Modeling
(ISM) methodology to analyze the interaction among the major
barriers, which hinder or prevent the application of RL in automobile industries. Based on the results of a case study, they point
out that strategic management issues like lack of awareness of RL
and lack of commitment by top management have higher driving
power.
4. Remanufacturing
Remanufacturing is an industrial process involving the conversion of worn-out products into like-new conditions (Aksoy and
Gupta, 2005; Kim et al., 2006d). In remanufacturing, the products
are completely disassembled and some parts are machined to likenew condition, which sometimes includes cosmetic operations.
Remanufactured products usually have shorter lead times.
However, the high variability of remanufacturing operations makes
the use of traditional operations management techniques difficult.
That is why, researchers developed new methodologies to deal
with various operations management issues in remanufacturing
including forecasting, production planning and scheduling,
capacity planning, inventory management and effect of
uncertainty.
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4.1. Forecasting
Accurate estimation of product returns is an important input for
the analysis of remanufacturing systems. However, the uncertainty
in the timing and quantity of returns make the use of traditional
forecasting methods impossible (Marx-Gomez et al., 2002). That is
why the researchers develop their own forecasting approaches to
predict the product returns. Kelle and Silver (1989) develop four
forecasting methods with different information requirements to
predict the container returns throughout the lead time. Toktay et al.
(2000) employ the methods developed by Kelle and Silver (1989) to
estimate the total number of circuit boards for the Kodak’s singleuse camera return network. de Brito and van der Laan (2009) and
Toktay et al. (2004) investigate the performance of the forecasting
methods proposed in Kelle and Silver (1989) by considering the
impact of imperfect information on inventory related costs.
Linton and Yeomans (2003), Linton et al. (2002, 2005) try to
estimate the waste stream resulting from disposal of the CRTs in
USA for the period between the years 2000 and 2050. First they
develop a waste disposal model which captures the uncertainty
related with the television lifecycle, the CRT weight in the televisions, the time between television failure and actual enterance time
to the waste stream, the proportion of televisions that are
reclaimed. Then, future television sales are estimated by considering three technological change scenarios: no technological
change, moderate change, aggressive change. These scenarios are
investigated using Monte Carlo simulation.
Marx-Gomez et al. (2002) propose a FL-based forecasting
method to provide estimates for the return values of scrapped
products. First, they develop a simulation model to generate data
on return amounts, sales and failures. Then a fuzzy inference
system is designed to estimate the return amounts for a specific
planning period. Finally, a neuro-fuzzy system is employed for the
multi period forecasting of the return values.
4.2. Production planning
A production planning system for remanufacturing assists
managers in planning how much and when to disassemble, how
much and when to remanufacture, how much to produce and/or
order for new materials, and coordinates disassembly and reassembly (Guide et al., 1999a). Ferrer and Whybark (2001) develop
a Material Requirements Planning (MRP)-based methodology to
determine how many and which cores to buy, what mix of cores to
disassemble, and which components should be assembled to meet
demand. Souza and Ketzenberg (2002) and Souza et al. (2002)
consider a firm that meets demand for an order with remanufactured products, new products or a mix of both. There is a capacity
limitation on production and a service-level constraint measured in
terms of the average order lead time. They use a two-stage GI/G/1
queueing network model to find the optimal, long-run production
mix that maximizes profit subject to the service-level constraint. In
order to test the robustness of the model, a DES model is also
developed by considering some real life issues such as stochastic
product returns and stochastic production yield. Gupta and Veerakamolmal (2001) use an integer programming (IP)-based algorithm for the determination of the number of products to
disassemble in order to fulfill the demand for various components
for remanufacturing in different time periods. Jayaraman (2006)
proposes a mathematical programming model to determine the
number of units of core type with a nominal quality level that is
disassembled, disposed, remanufactured and acquired in a given
time period. Inventory of modules and cores that remain at the end
of a given time period can also be determined by the model. Kim
et al. (2006d) develop an MIP model in order to determine the
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quantity of products/parts processed in the remanufacturing
facilities/subcontractors and the amount of parts purchased from
the external suppliers based on the maximization of the total
remanufacturing cost saving. Lu et al. (2006) develop a short-term
bulk recycling planning model to determine what products to
accept, process, and reprocess. DePuy et al. (2007) present
a production planning method which estimates the expected
number of remanufactured units to be completed in each future
period together with the number of components needed to be
purchased to avoid any projected shortages. Li et al. (2009) integrate a hybrid cell evaluated GA with a DES model to optimize the
production planning and control policies for dedicated remanufacturing. Xanthopoulos and Iakovou (2009) propose an MILPbased aggregate production planning model which can determine
how many EOL products and components should be collected, nondestructively or destructively disassembled, recycled, remanufactured, stored, backordered and disposed in each period.
4.3. Production scheduling
Due to greater degree of uncertainty and complexity of remanufacturing systems, researchers have developed several remanufacturing oriented scheduling methodologies. The most commonly
used technique to test the performance of these methods is DES.
Guide (1996) uses DES modeling to compare an MRP-based current
production planning and control system with a Drum-Buffer-Rope
(DBR)-based proposed system for a real life remanufacturing
facility. He questions the use of MRP in remanufacturing systems by
stating that highly variable remanufacturing environment lacks the
stability which is one of the fundamental requirements for
a successful MRP system. Guide (1997) extends Guide (1996) by
testing the performance of priority dispatching rules in DBR environment using the DES analysis. Guide et al. (1997b) report that
consideration of product structure in the selection of specific
priority dispatching rules provides faster flow times and a better
delivery performance. Guide and Srivastava (1997a) develop a DES
model for the evaluation of order release strategies in a remanufacturing environment. Guide et al. (1997a) investigate the disassembly release mechanisms and priority dispatching rules via
a DES model. Guide et al. (1998) analyze the effect of proactive
expediting policies on mean flow time, mean lateness, mean root
mean square lateness and mean percentage late criteria by using
a DES model. Guide et al. (1999b) develop a DES model to investigate the effect of lead time variation on the performance of the
disassembly release mechanisms. Guide et al. (2000) carry out
a DES analysis to examine the performance of several priority dispatching rules for a repair shop. Guide et al. (2005b) analyze the
performance of static priority rules for a remanufacturing system
with shared facilities.
4.4. Capacity planning
Various researchers have developed capacity planning and
Rough Cut Capacity Planning (RCCP) techniques considering the
characteristics of remanufacturing environments. Guide and
Spencer (1997) develop an RCCP method for remanufacturing firms
by considering probabilistic material replacement and probabilistic
routing files. Guide et al. (1997c) compare the modified RCCP
techniques with traditional RCCP techniques. Based on the results,
they state that traditional techniques tend to perform poorly in
a recoverable environment.
LP and simulation were used to develop capacity plans for
remanufacturing facilities. Kim et al. (2005) develop a mathematical model to determine the capacity of remanufacturing facilities
based on the maximization of the saving from the investment on
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remanufacturing facilities. Georgiadis et al. (2006) and Vlachos
et al. (2007) develop SDS models for the remanufacturing capacity
planning in closed-loop supply chains. Georgiadis and Athanasiou
(in press) extend the Georgiadis et al.’s (2006) SDS model by
considering two sequential product lifecycles of two product types,
under two scenarios about the market with regard to customer
preferences over the product types. Franke et al. (2006) integrate LP
and DES to generate capacity plans for a cell phone remanufacturing facility.
4.5. Inventory management
Consideration of the product returns and remanufacturing
options causes two additional complexities in traditional inventory
management approaches. First, an uncertain element is added due
to uncertain product returns. Second, there is a need for coordination between the remanufacturing and regular mode of
procurement (Inderfurth and van der Laan, 2001). Researchers have
developed various inventory models to deal with these complexities. In this section, we review these models by considering the
different modeling approaches for demand and returns. The other
inventory management issues including costs and valuation, effect
of lead times and spare part inventories are also covered in this
section.
4.5.1. Inventory models
In this study, we mainly consider two types of inventory models:
deterministic and stochastic. In deterministic models the stationary
and dynamic demands are considered while the stochastic models
are organized into two categories: periodic review and continuous
review.
4.5.1.1. Deterministic models. These models are based on the
assumption that demand and return quantities are known for
entire planning horizon. They try to find an optimal balance
between fixed setup costs and variable inventory holding costs.
4.5.1.1.1. Stationary demand. In case of stationary demand,
deterministic models exploit the logic of Economic Order Quantity
(EOQ) to find an optimal trade-off between fixed setup costs and
variable inventory holding costs (Fleischmann et al., 1997).
Assuming infinite production rates for manufacturing and remanufacturing, the first EOQ model with item returns was developed
by Schrady (1967). Nahmias and Rivera (1979) extend Schrady
(1967) by considering finite remanufacturing rates. As another
extension to Schrady (1967), Mabini et al. (1992) consider stockout
service-level constraints and a multi-item system. Richter (1996a,b,
1997) present EOQ waste disposal and repair models with variable
remanufacturing and return rates. They determine the optimal
number of remanufacturing and production batches for the
different values of the return rate. Richter and Dobos (1999) and
Dobos and Richter (2000) develop integer non-linear models for
the analysis of EOQ repair and waste disposal problem with integer
setup numbers. It is found that the pure strategy (total repair or
total waste disposal) is optimal. Dobos and Richter (2003) investigate a production-recycling system by assuming that there is only
one recycling lot and one production lot. Dobos and Richter (2004)
generalize the results of Dobos and Richter (2003) by considering
multiple production and recycling lots. Dobos and Richter (2006)
extend the model of Dobos and Richter (2004) by relaxing the
assumption of perfect quality of the returned items. El Saadany and
Jaber (2008) extend Richter (1996a,b) by considering the costs
associated with switching between production and recovery runs.
They also point out that ignoring the first time interval causes an
overestimation of holding costs due to an unnecessary residual
inventory. Jaber and Rosen (2008) apply the first and second laws of
thermodynamics to reduce system entropy by considering the EOQ
repair and waste disposal model of Richter (1996a,b). Jaber and El
Saadany (2009) incorporated the possibility of lost sales into the
inventory models presented in Richter (1996a,b), El Saadany and
Jaber (in press) extend Dobos and Richter (2003, 2004) by considering a price and quality dependant return rate. Jaber and El Saadany (in press) incorporate the learning effects in production and
remanufacturing (repair) into the model of Dobos and Richter
(2003, 2004).
Teunter (2001) develops EOQ formulae by using different
holding cost rates for manufactured and recovered items. Koh et al.
(2002) develop a joint EOQ and EPQ model for a system in which
the stationary demand can be satisfied with remanufacturing or
procurement. They extend previous studies by considering
a capacitated repair facility. As an extension to Koh et al. (2002),
Wee et al. (2006) allow shortage backorders and develop a search
procedure for the optimal ordering and recovery policy. Teunter
(2004) develops simple expressions for the determination of
optimal lot sizes for the production/procurement of new items and
for the recovery of returned items. The expressions are more
general than those in the literature in a sense that they are valid for
finite and infinite production rates as well as finite and infinite
recovery rates. Konstantaras and Papachristos (2006) extend
Teunter (2004) by considering backordering and developing
a mathematically rigorous approach which leads to overall optimal
policy within a specific set of policies. Tang and Grubbström (2005)
consider stochastic lead times for manufacturing and remanufacturing and compare the performance of cycle order policy and
dual sourcing ordering policy. Oh and Hwang (2006) try to develop
optimal policy parameters for a recycling system in which returned
items are used as raw material in the production of new products.
Tang and Teunter (2006) consider a hybrid remanufacturing/
manufacturing system in which manufacturing and remanufacturing operations for multiple product types are performed on
the same production line. They formulate an MIP problem to find
the cycle time and the production start times for (re)manufacturing
lots based on the minimization of the total cost per time unit. In
a follow-up paper, Teunter et al. (2008) investigate the multi
product economic lot scheduling problem for the case of dedicated
lines for manufacturing and remanufacturing. Chung et al. (2008)
develop an optimal production and replenishment policy for
a multi echelon inventory system with remanufacturing by simultaneously considering the concerns of the supplier, the manufacturer, the retailer and the third party recycler. Roy et al. (2009)
consider a remanufacturing-production system in which defective
units are continuously transferred to the remanufacturing and the
constant demand is met by the perfect items from production and
remanufactured units. They model the rate of defectiveness of
production system as a fuzzy parameter whereas the remanufactured units are treated as perfect items. A GA is designed to
determine the total number of cycles in the time horizon, the
duration for which the defective items are collected and the cycle
length after the first cycle based on the maximization of total profit.
Rubio and Corominas (2008) consider a lean production-remanufacturing environment in which capacities of manufacturing and
remanufacturing can be adjusted according to constant demand in
order to prevent the excessive inventory levels. Optimal values for
manufacturing and remanufacturing capacities, return rates and
use rates for EOL products are determined.
4.5.1.1.2. Dynamic demand. Modifications of classical WagnerWhitin algorithms were used to deal with the dynamic demand in
the earlier studies. More recent studies develop DP-based algorithms to find the optimal parameter values. Richter and
Sombrutzki (2000) extend the Wagner/Whitin algorithm for
a deterministic recovery system by assuming a linear cost model
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with no backordering and negligible lead times. Their model is
applicable only for the case of large quantity of used products. In
other words, they assume that the quantity of used products
matches the demand of remanufactured goods. In a follow-up
paper, Richter and Weber (2001) extend this model by considering
variable manufacturing and remanufacturing costs. Richter and
Gobsch (2003) apply the Richter and Sombrutzki’s (2000) model in
a just in time framework. Minner and Kleber (2001) use control
theory to find an optimal policy for a remanufacturing system with
dynamic demand by considering no backorders and lead times.
Kiesmüller (2003a) extends Minner and Kleber (2001) by finding
an optimal policy for the case of positive and different lead times
for production and remanufacturing. Golany et al. (2001) model the
lot sizing problem with remanufacturing as a network flow
problem. They present a polynomial time algorithm for the case of
linear costs. In a follow-up paper, Yang et al. (2005) propose
a polynomial time algorithm for the case of concave costs. Kleber
et al. (2002) use Pontryagin’s Maximum Principle to determine the
optimal policy by considering multiple remanufacturing options.
However they assume that no backorders are allowed and lead
times are zero. Beltran and Krass (2002) consider the dynamic lot
sizing problem with directly saleable returns. In order to determine
the manufacturing and disposal decisions, they develop a DP
algorithm with a O(N3) complexity for the case of concave costs.
Teunter et al. (2006) study the dynamic lot sizing problem with
product returns and remanufacturing by considering two scenarios
for setup costs: a joint setup cost for manufacturing and remanufacturing (single production line) or separate setup costs (dedicated
production lines). By modeling both problems as MIP programs, the
authors propose a DP algorithm for the joint setup cost case. They
also provide the modified versions of Silver Meal (SM), Least Unit
Cost (LUC), and Part Period Balancing (PPB) heuristics for both
setup cost schemes. Konstantaras and Papachristos (2007) propose
an optimal policy that specifies the period of switching from
remanufacturing to manufacturing, the periods where remanufacturing and manufacturing activities take place and the
corresponding lot sizes. Bera et al. (2008) investigate a productionremanufacturing control problem based on the assumptions of
stochastic product defectiveness and fuzzy upper bounds for
production, remanufacturing and disposal.
4.5.1.2. Stochastic models. In stochastic models, stochastic
processes are employed to model demand and returns. Continuous
and periodic review policies are two common approaches used in
stochastic models.
4.5.1.2.1. Continuous review models. These models use continuous time axis and try to determine the optimal static control
policies based on minimization of the long-run average costs per
unit of time (Fleischmann et al., 1997). Heyman (1977) presents the
first study in this area by considering a continuous review strategy
for a single item inventory system with remanufacturing and
disposal. He derives an optimum disposal level by assuming no
fixed ordering costs and instantaneous outside procurement. As an
extension to Heyman (1977), Muckstadt and Isaac (1981) develop
a model involving non-zero lead times for repair and procurement
and non-zero fixed costs. However, their model does not include
disposal of the products and an approximate numerical procedure
is used to determine the optimal parameter values. van der Laan
et al. (1996a,b) extend Muckstadt and Isaac (1981) by adding
a disposal option. They compare a number of policies numerically
for this model. van der Laan and Salomon (1997) and van der Laan
et al. (1999b) present a detailed analysis of different policies to
control serviceable and recoverable stock in the above setting by
considering non-zero lead times for both sources. They mainly
consider two policies: a push and a pull-driven recovery policy. van
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der Laan et al. (1999a) extend van der Laan et al. (1999b) by
considering stochastic lead times for manufacturing and remanufacturing. Fleischmann et al. (2002) optimize the parameters of a (s,
Q) policy for a basic inventory model involving Poisson demand
and returns. Fleischmann and Kuik (2003) use general results on
Markov decision processes to develop an average cost optimal (s, S)
policy for an inventory system involving independent stochastic
demand and item returns. van der Laan and Teunter (2006) use
certain extensions of (s, Q) policy and propose closed form
expressions for each policy to calculate near optimal policy
parameters by assuming the equality of manufacturing and remanufacturing lead times. Zanoni et al. (2006) consider some inventory control policies extended from the traditional inventory
control models such as (s, Q) and (s, S) for a hybrid manufacturing/
remanufacturing system where demand, return rate, and lead
times are stochastic. They use DES to compare different inventory
control policies based on the total cost. Heisig and Fleischmann
(2001) address planning stability of production and remanufacturing setups in a product recovery system. In the above models,
priority decision between manufacturing and remanufacturing is
taken based on an average cost comparison. Aras et al. (2006)
question the reliability of this technique and develop two alternative strategies that use either manufacturing or remanufacturing as
the primary source to satisfy demand.
Korugan and Gupta (1998) develop a queueing network model
to analyze the behavior of a multi echelon inventory system with
returns. Toktay et al. (2000) develop a closed queueing network
model to investigate the procurement of new components for
recyclable products. Bayindir et al. (2003) present a queueing
network model comprising of manufacturing/remanufacturing
operations, supplier’s operations for the new parts and useful life
time of the product. Using this model, they try to investigate the
conditions on different system parameters (lifetime of the product,
supplier lead time, lead time and value added of manufacturing and
remanufacturing operations, capacity of the production facilities)
that make remanufacturing alternative attractive considering the
total cost. Ching et al. (2003) develop a closed form solution for the
system steady-state probability distribution for an inventory model
with returns and lateral transshipments between inventory
systems. Nakashima et al. (2002, 2004) develop Markov chain
models to analyze the behavior of stochastic remanufacturing
systems. Takahashi et al. (2007) consider a decomposition process
in which recovered products are decomposed into parts, materials
and waste. The performance of the proposed policies is evaluated
by using a Markov chain model of the system.
4.5.1.2.2. Periodic review models. These models search for
optimal policies based on the minimization of expected costs over
a finite planning horizon (Fleischmann et al., 1997). By assuming
zero lead times and a disposal option, Simpson (1978) proposes an
optimal policy with three parameters (S, M, U), where S is produceup-to level, M is remanufacture-up-to-level and U is the disposedown-to level. Inderfurth (1997) extends Simpson’s (1978) model
by considering non-zero lead times. He shows the optimality of the
(S, M, U) policy for the case of equal lead times. Kiesmüller and
Scherer (2003) provide an exact computation method and two
approximations in order to determine the optimal parameter
values for the periodic review policy studied by Simpson (1978) and
Inderfurth (1997). Mahadevan et al. (2003) consider a pull policy
and develop heuristics to determine only produce-up-to level by
assuming that all available recoverables can be remanufactured.
Kiesmüller (2003b) and Kiesmuller and Minner (2003) present
simple expressions for computing the produce-up-to level and the
remanufacture-up-to level for the cases of identical and nonidentical lead times. Inderfurth et al. (2001) develop an approximation algorithm for the determination of optimal policy
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parameters of a stochastic remanufacturing system with multiple
reuse options.
Some researchers consider multi echelon systems. DeCroix
(2006) extends Simpson (1978) and Inderfurth (1997) to a series
system with no disposal. DeCroix et al. (2005) show the optimality
of an echelon base-stock policy for an infinite-horizon series
system where returns go directly to stock. DeCroix and Zipkin
(2005) carry out a similar analysis for an assembly system.
Newsboy problem can be considered as a special case of periodic
review models with only one period (Dong et al., 2005). Vlachos
and Dekker (2003) and Mostard and Teunter (2006) extend the
classical newsboy problem to incorporate returns. Their main aim is
to determine the initial order quantity. Vlachos and Dekker (2003)
assume that a constant portion of the sold products is returned and
products can be resold at most once. Mostard and Teunter (2006)
extend Vlachos and Dekker (2003) by analyzing a newsboy
problem in which sold products are returned with a certain probability and resold provided they are not damaged.
investigate the effect of lead time. Ferrer (2003) and Ferrer and
Ketzenberg (2004) analyze the impact of shorter supplier lead
times on manufacturing costs. Mahadevan et al. (2003) investigate
the effect of manufacturing and remanufacturing lead times on
optimal parameter value and total cost. Teunter et al. (2004)
propose strategies for hybrid manufacturing/remanufacturing
systems with long manufacturing lead time and short remanufacturing lead time. Tang and Naim (2004), Zhou et al. (2006), and
Zhou and Disney (2006) use control theory and simulation to
investigate the effect of manufacturing/remanufacturing lead
time on system performance. Tang et al. (2007) try to estimate the
planned lead time in a manufacture-to-order remanufacturing
environment by assuming a normally distributed lead times. In
a follow-up paper, Bao et al. (2008) question this normality
assumption and use the Minimum Relative Entropy (MRE) method
to approximate the lead time distribution. They report that the
MRE approach provides a better approximation than the normal
approximation.
4.5.2. Costs and valuation
Valuation of the inventory and the determination of the holding
cost rates are two important topics studied by researchers. There
are a number of studies investigating the effect of different holding
cost setting rules on the performance of a remanufacturing system.
Teunter et al. (2000) carry out a simulation analysis to evaluate the
performance of alternative rules to set the inventory holding cost
rates for a continuous review model with stochastic demand and
return of items, and fixed positive lead times for manufacturing and
remanufacturing. Teunter and van der Laan (2002) show that the
Average Cost (AC) approach may not always be appropriate for
a deterministic RL inventory model with both remanufacturing and
disposal of returned products. For the stochastic version of the
same problem, van der Laan (2003) uses an exact procedure rather
than simulation to compare Net Present Value (NPV) and AC rules.
Corbacioglu and van der Laan (2007) show that the correct holding
cost rates are different from traditional valuation methodology and
demonstrate the impact of this finding on operational performance
for a two product two source remanufacturing environment. Akcali
and Bayindir (2008) investigate the effect of different inventory
holding cost setting rules on the performance of a disassembly and
recovery system. Tang et al. (2004) develop an MRP theory and
decision analysis-based methodology to calculate the values of the
return products. Then, these values are used to estimate the
inventory holding costs.
4.5.4. Inventory substitution
Some remanufacturing inventory models include the possibility
of substitution between manufactured and remanufactured products. Inderfurth (2004) and Bayindir et al. (2005) try to develop
optimal policies for hybrid manufacturing/remanufacturing
systems in which there is a significant difference between the
remanufactured and new products and a new product can be
offered as a substitute to the remanufactured product in case of
a shortage of a remanufactured product. They consider continuous
review (S–1, S) inventory control policy for both types of products.
Bayindir et al. (2007) extend these two studies by considering
a finite production capacity. Yongjian et al. (2006) consider the
problem with multiple product types and multiple periods and
develop a DP-based approximate solution procedure to obtain near
optimal solutions. However, they assume that demand and return
quantities in each period are deterministic and there is no capacity
constraint on production.
4.5.3. Effect of lead time
Researchers developed models to investigate the effect of
manufacturing, procurement and remanufacturing lead times on
inventory control policies. Inderfurth (1997) uses DP to develop
simple optimal replenishment and disposal policies for
a stochastic product recovery system by considering lead times for
procurement and remanufacturing. He states that optimal decision rules can be derived if the difference between lead times is at
most one period. van der Laan et al. (1999a) provide a numerical
analysis of the effects of lead time duration and lead time variability on total expected costs in production/inventory systems
with remanufacturing. van der Laan et al. (1999b) analyze the
effect of lead times by assuming a deterministic manufacturing
lead time and a deterministic remanufacturing lead time, rather
than a deterministic manufacturing lead time and stochastic
remanufacturing lead time resulting from limited remanufacturing capacity. Inderfurth and van der Laan (2001) consider
a remanufacturing system in which lead times for remanufacturing and regular procurement differ. For this system, they define
lead time as a decision variable and develop a DP approach to
4.5.5. Spare part inventories
The studies in this category try to develop spare parts
inventory policies by considering the recovered parts from EOL
products as a source of spare parts supply. Fleischmann et al.
(2003) consider the use of disassembly as a source of spare parts
in a case study conducted in IBM. They use a basic analytic
inventory model and a simulation model to test alternative
policies based on alternative channel designs and alternative
coordination mechanisms. Spengler and Schroeter (2003) and
Schroter and Spengler (2004) use SDS modeling to evaluate
various spare part acquisition alternatives for electronic equipment in EOL phase. Inderfurth and Mukherjee (2008) develop an
integrated approach by considering various spare part acquisition
alternatives. First they use a decision tree for structuring the
underlying decision problem and demonstrating the interdependencies of different alternatives to choose. Then stochastic DP
is employed for developing simple decision rules and a heuristic
solution procedure for determining the parameters of a simple
order-up-to policy. Ilgin and Gupta (2008a) propose a GA-based
simulation-optimization methodology to determine the optimal
final order quantities for a number of spare parts considering
only the parts recovered from discarded EOL products as a source
of spare parts during post product life cycle. Ilgin and Gupta
(2008b) present a simulation-optimization study to determine
the optimum reorder and order quantity levels for the spare
Printed Circuit Boards (PCBs) by simultaneously considering two
spare parts acquisition alternatives: the recovered PCBs from EOL
TVs and newly purchased PCBs.
M.A. Ilgin, S.M. Gupta / Journal of Environmental Management 91 (2010) 563–591
4.6. Effect of uncertainty
One of the problems associated with remanufacturing is the
high degree of variability in the operation processing times. The
uncertainty in the quantity, quality and timing of returned products
further complicates the analysis of remanufacturing systems. Some
of the researchers develop decision-making models to investigate
the impact of this high level of uncertainty on the behavior of
remanufacturing systems. Ferrer (2003) and Ferrer and Ketzenberg
(2004) develop decision models to analyze a remanufacturer’s
trade-off between limited information about remanufacturing
yields and potentially long supplier lead times. They state that
identification of product yield early in the disassembly process is
significantly more valuable than placing purchase orders with
a short lead time. Ketzenberg et al. (2003) investigate the impact of
having advanced remanufacturing yield information in long-run
average flow time. As an extension to these three studies, Ketzenberg et al. (2006) carry out a study on the value of information in
remanufacturing by simultaneously considering the uncertainties
related with demand, return and yield. Ketzenberg (2009) extends
Ketzenberg et al. (2006) by considering disposal of returned
products and a capacitated product recovery system. Inderfurth
(2005) uses numerical analysis to investigate the effect of uncertainties related with return, quality and demand on the recovery
behavior.
Bakal and Akcali (2006) analyze the effect of recovery yield
information on pricing decisions of a remanufacturer. Zikopoulos
and Tagaras (2007) investigate the effect of uncertainty in quality
levels of returned products on the profitability of a single period
refurbishing operation. Galbreth and Blackburn (2006) try to find
optimal sorting and acquisition policies by considering the variability
in the condition of the used products. Zikopoulos and Tagaras (2008)
present an infinite-horizon analysis on the attractiveness of sorting
procedures for a single collection center with stochastic yield.
Tagaras and Zikopoulos (2008) perform a similar analysis for
multiple collection centers with deterministic yield. The impact of
quality-based categorization of returned products is studied by Aras
et al. (2004) using a Markov chain model. Behret and Korugan (2009)
use a DES model to analyze the effect of quality uncertainties on the
performance of a system by considering the classification of returned
products based on quality uncertainties. The uncertainty related
with the quality of returns is also considered by Bakal and Akcali
(2006), Aras et al. (2006) and Souza et al. (2002).
As an alternative to the static modeling, dynamic control
methods are used in some studies to analyze the effect of uncertainty. Tang and Naim (2004) carry out mathematical and simulation analyses to test the robustness of push type hybrid
manufacturing-remanufacturing system to various uncertainties of
the remanufacturing process. Zhou et al. (2006) carry out a similar
analysis for a pull type system. In a follow-up paper, Zhou and
Disney (2006) use control theory and simulation to investigate the
impact of lead time and return rate on inventory variance and the
bullwhip phenomenon. Huang et al. (2009b) develop dynamic
closed-loop supply chain models by considering the uncertainty
due to time-delay in remanufacturing and returns, system cost
parameters and customer demand’s disturbances.
Some studies investigate the use of inventory buffers to deal
with material recovery uncertainty and probabilistic routings.
Guide and Srivastava (1997b, 1998) develop DES models to investigate the use of inventory buffers in an MRP system. Minner (2001)
studies the use of inventory buffers in a supply chain with internal
and external product returns and reuse. Helber (1998) uses Markov
process models to determine average buffer levels for assembly/
disassembly networks with limited buffer capacity and random
processing times. Aksoy and Gupta (2005) combine open queueing
577
networks, decomposition principle and expansion methodology to
determine a near optimal buffer allocation plan for a remanufacturing cell with finite buffers and unreliable servers.
The uncertainty in the timing of returns was investigated by
some researchers. Atasu and Çetinkaya (2006) develop cost optimization models to investigate the impact of the timing of returns
on the cost efficiency of a simple remanufacturing system. Guide
et al. (2005a, 2006), Geyer et al. (2007) investigate the concept of
‘‘time value of returns’’ which involves the use of returns at the best
possible time, i.e. whenever suitable market conditions for returns
occur.
5. Disassembly
Disassembly can be defined as the systematic separation of an
assembly into its components, subassemblies or other groupings
(Moore et al., 2001; Pan and Zeid, 2001). Disassembly is an
important process in material and product recovery since it allows
for the selective separation of desired parts and materials. We refer
the reader to a recent book by Lambert and Gupta (2005) for the
detailed information on the general area of disassembly. This
section starts with the investigation of the studies on the two
important phases of disassembly process, scheduling and
sequencing. Then a review of the studies on disassembly line
balancing, disassembly to order systems, ergonomics and automation of disassembly systems is presented.
5.1. Scheduling
Disassembly scheduling is the scheduling of the ordering and
disassembly of EOL products to fulfill the demand for the parts or
components over a planning horizon (Veerakamolmal and Gupta,
1998; Lee and Xirouchakis, 2004). In general, disassembly scheduling problems can be categorized as capacitated and uncapacitated. For the uncapacitated case, Gupta and Taleb (1994) propose
an MRP-like algorithm for disassembly scheduling of a discrete,
well-defined product structure. The algorithm determines the
quantity and timing of disassembly of a single product to fulfill the
demand for its various parts. Taleb et al. (1997) and Taleb and Gupta
(1997) extend the Gupta and Taleb (1994) by considering components/materials commonality and the disassembly of multiple
product types. Lee and Xirouchakis (2004) suggest a two-phase
heuristic algorithm for the objective of minimizing various costs
related with the disassembly process. In the first phase, Gupta and
Taleb’s (1994) algorithm is used to find an initial solution. The
second phase improves this initial solution using backward move.
Barba-Gutierrez et al. (2008) extend the reverse MRP algorithm of
Gupta and Taleb (1994) by developing a methodology which allows
lot sizing in reverse MRP. Kim et al. (2003) propose a heuristic
algorithm based on the LP relaxation for the case of multiple
product types with parts commonality with the aim of minimizing
the sum of setup, disassembly operation and inventory holding
costs. Kim et al. (2006b) develop a two-phase heuristic by
extending the Kim et al.’s (2003) study. The first phase involves the
construction of an initial solution using the LP relaxation heuristic
suggested by Kim et al. (2003). The second phase improves the
initial solution using DP. Lee et al. (2004) present three IP models
for the three cases of the uncapacitated disassembly scheduling
problem, i.e. a single product type without parts commonality and
single and multiple product types with parts commonality. Kim
et al. (2009d) propose a branch and bound algorithm for the case of
single product type without parts commonality.
For the capacitated case, Meacham et al. (1999) present an
optimization algorithm by considering common components
among products, and limited inventory of products available for
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disassembly. Lee et al. (2002) develop an IP model based on the
minimization of sum of disassembly operation and inventory
holding costs. However, the model requires excessive computation
times to find optimal solutions for practical-sized problems. As an
extension to Lee et al. (2002), Kim et al. (2006a) develop a Lagrangean heuristic algorithm to find an optimal solution for practical
problems in a reasonable amount of time. In this study, disassembly
setup costs were also included in objective function. Kim et al.
(2006c) propose an optimal algorithm for the case of single product
type without parts commonality based on the minimization of the
number of disassembled products. According to this algorithm, first
an initial solution is determined using the Gupta and Taleb’s (1994)
algorithm. Then, the feasibility of this solution is checked. If the
solution is not feasible, it is modified to satisfy the capacity
constraints.
Stuart and Christina (2003) define disassembly and bulk recycling scheduling rules based on the product turnover in incoming
staging space. Rios and Stuart (2004) extend Stuart and Christina
(2003) by considering product turnover together with the outgoing
plastics demand. In both studies, scheduling rules are evaluated
using DES models. Brander and Forsberg (2005) develop a cyclic lot
scheduling heuristic for disassembly processes by considering
sequence-dependent setups.
5.2. Sequencing
Disassembly sequencing deals with the problem of determining
the best order of operations in the separation of a product into its
constituent parts or other groupings (Dong and Arndt, 2003; Moore
et al., 1998). Various graphical approaches were developed to solve
the disassembly sequencing problem. Lambert (1997) presents an
AND/OR graph-based graphical method for the generation of the
optimum disassembly sequence. Kaebernick et al. (2000) use
a cluster graph which is created by sorting the components of
a product into different levels based on their accessibility for
disassembly. Torres et al. (2003) develop an algorithm based on the
product representation to establish a partial non-destructive
disassembly sequence of a product. Li et al. (2006) present
a Disassembly Constraint Graph (DCG) to generate possible disassembly sequences for maintenance. Dong et al. (2006) propose
a method for the automatic generation of disassembly sequences
from a hierarchical attributed liaison graph.
Some researchers present Case Based Reasoning (CBR) applications for disassembly sequencing. Zeid et al. (1997) apply CBR to
develop a disassembly plan for a single product. In a follow-up
paper, Veerakamolmal and Gupta (2002) present a CBR approach
for the automatic generation of disassembly process plans for
multiple products. Pan and Zeid (2001) develop a knowledge base
to assist the users in indexing and retrieving disassembly
sequences.
Petri net (PN) modeling represents a popular alternative for
disassembly sequencing problem. Moore et al. (1998, 2001) present
a PN-based approach for the automatic generation of disassembly
process plans for products with complex AND/OR precedence
relationships. Zussman and Zhou (1999, 2000) develop Disassembly Petri Nets (DPNs) for the design and implementation of
adaptive disassembly systems. Zha and Lim (2000) integrate expert
systems and ordinary PNs to develop an expert PN model for the
disassembly planning. Tang et al. (2001) present an integrated
approach for disassembly planning and demanufacturing scheduling by developing PN models for workstation status, product
disassembly sequences, and scheduling. Tiwari et al. (2001) integrate cost-based indices with PNs to determine an effective disassembly sequencing strategy. Rai et al. (2002) develop a PN-based
heuristic approach for disassembly sequence generation. Kumar
et al. (2003) and Singh et al. (2003) try to deal with the unmanageable complexity of normal PNs by proposing an expert
enhanced coloured stochastic PN which consists of a knowledge
base, graphic characteristics and artificial intelligence. Gao et al.
(2004) propose a fuzzy reasoning PN to deal with the uncertainty
associated with the disassembly process. Tang et al. (2006) consider
the uncertainty associated with the human factors in disassembly
planning and propose a fuzzy attributed PN to deal with this
uncertainty. Grochowski and Tang (2009) integrate a DPN and
a hybrid Bayesian network to develop an expert system capable of
determining the optimal disassembly action without human
assistance.
The use of mathematical programming techniques in disassembly sequence generation is another popular approach. Lambert
(1999) presents an algorithm based on straightforward LP for the
determination of optimal disassembly sequences. Lambert (2006)
proposes a methodology based on the iterative use of Binary
Integer Linear Programming (BILP) in case of sequence-dependent
costs and disassembly precedence graph representation. Lambert
(2007) applies the same methodology for the problems with AND/
OR representation.
Due to combinatorial nature of the disassembly sequencing
problem, there is an increasing trend in the use of metaheuristics.
Seo et al. (2001) develop a GA-based heuristic algorithm to determine the optimal disassembly sequence considering both economic
and environmental aspects. Li et al. (2005) integrate DCG and a GA
to develop an object oriented intelligent disassembly sequence
planner. Kongar and Gupta (2006b), Giudice and Fargione (2007),
Duta et al. (2008a) and Hui et al. (2008) present GA-based
approaches for disassembly sequencing of EOL products. Gonzalez
and Adenso-Diaz (2006) propose a scatter search-based methodology to deal with the optimum disassembly sequence problem for
complex products with sequence-dependent disassembly costs by
assuming that only one component can be released at each time.
Chung and Peng (2006) develop a GA to generate a feasible selective disassembly plan considering batch disassembly and tool
accessibility. Shimizu et al. (2007) apply genetic programming as
a resolution method to derive an optimal disassembly sequence.
Reveliotis (2007) presents a reinforcement-learning-based
approach to provide (near-) optimal disassembly sequences.
Tripathi et al. (2009) present a fuzzy disassembly sequencing
problem formulation by considering the uncertainty inherent in
quality of the returned products. They develop an Ant Colony
Optimization (ACO)-based metaheuristic to determine the optimal
disassembly sequence as well as the optimal depth of disassembly.
Kongar and Gupta (in press) employ a multi objective TS algorithm
to generate near optimal/optimal disassembly sequences.
In some studies, heuristic procedures are developed. Gungor
and Gupta (1997) develop a methodology to evaluate different
disassembly strategies. They also propose a heuristic procedure to
determine the near optimal disassembly sequences. Gungor and
Gupta (1998) address the uncertainty related difficulties in disassembly sequence planning. They present a methodology for
disassembly sequence planning for products with defective parts in
product recovery. Kuo (2000) provides a disassembly sequence and
cost analysis study for the electromechanical products during the
design stage. He divides disassembly planning into four stages:
geometric assembly representation, cut-vertex search analysis,
disassembly precedence matrix analysis, and disassembly
sequences and plan generation. The disassembly cost is categorized
into three types: target disassembly, full disassembly, and optimal
disassembly. Gungor and Gupta (2001b) use a branch and bound
algorithm for disassembly sequence plan generation. In Erdos et al.
(2001), a heuristic is used to decompose the problem by discovering the subassemblies within the product structure. Then,
M.A. Ilgin, S.M. Gupta / Journal of Environmental Management 91 (2010) 563–591
shortest hyperpath calculation is applied to determine the optimal
disassembly sequence. Kang et al. (2003) propose an algorithm
based on mini-max regret criterion to solve the disassembly
sequencing problem with interval profit values in the objective
function. Mascle and Balasoiu (2003) propose a wave propagationbased disassembly algorithm to select the disassembly sequence of
a specific component of a product. Lambert and Gupta (2008)
present a heuristic algorithm for detecting ‘‘good enough’’ solutions
for disassembly sequencing problems in case of sequence-dependent costs. They applied both the heuristic algorithm and the
iterative BILP method (Lambert, 2006) using disassembly precedence graph of a cell phone. Sarin et al. (2006) propose a precedence-constrained asymmetric traveling salesman problem
formulation together with a three phase iterative solution procedure. Adenso-Diaz et al. (2008) propose a GRASP and path-relinking-based heuristic methodology to solve a bi-criteria disassembly
planning problem.
Hsin-Hao et al. (2000) employ a neural network for disassembly
sequence generation.
5.3. Line balancing
Disassembly operations can be performed at a single workstation, in a disassembly cell or on a disassembly line. Although
a single workstation and disassembly cell is more flexible, the
highest productivity rate is provided by a disassembly line. Moreover, the disassembly line is more suitable for automated disassembly (Gungor and Gupta, 2001a).
Disassembly Line Balancing Problem (DLBP) concerns with the
assignment of disassembly tasks to a set of ordered disassembly
stations while satisfying the disassembly precedence constraints
and minimizing the number of stations needed and the variation in
idle times between all stations (Altekin et al., 2008; McGovern and
Gupta, 2007a). Gungor and Gupta (2001a) and Gungor and Gupta
(2002) presented the first examples of disassembly line balancing
algorithms. Gungor and Gupta (2001a) investigate the Disassembly
Line Balancing Problem in the presence of task Failures (DLBP-F). A
disassembly line balancing algorithm is presented to assign tasks to
workstations such that the effect of the defective parts on the
disassembly line is minimized. Gungor and Gupta (2002) discuss
the disassembly line related complications and their effects. They
also demonstrate the applicability of some important factors in
disassembly to balance a paced disassembly line by modifying the
existing concepts of assembly line balancing. Tang and Zhou (2006)
develop a two-phase PNs and DES-based methodology to maximize
system throughput and system revenue by dynamically configuring
the disassembly system into many disassembly lines while
considering line balance, different process flows, and meeting
different order due dates.
Metaheuristics were also used to develop disassembly line
balancing algorithms. McGovern and Gupta (2006) present an ACO
algorithm to obtain optimal or near optimal solution. A collaborative ant colony algorithm was proposed by Agrawal and Tiwari
(2006) for stochastic mixed-model U-shaped disassembly line
balancing. In McGovern and Gupta (2007a), a number of combinatorial optimization techniques (exhaustive search, GA and ACO
metaheuristics, a greedy algorithm, and greedy/hill-climbing and
greedy/2-optimal hybrid heuristics) are applied to obtain near
optimal solutions. They develop a known, optimal, varying size
dataset to illustrate the implementation of the methodologies,
measure performance and enable comparisons. McGovern and
Gupta (2007b) develop a new formula for quantifying the level of
balancing. They also present a first-ever set of a priori instances to
be used in the evaluation of any disassembly line balancing solution
579
technique. A GA is presented for obtaining optimal or near optimal
solutions for DLBPs.
Some researchers use mathematical programming techniques
to solve the DLBP. Altekin et al. (2008) provide an MIP formulation
for profit maximization in partial DLBP. The proposed model
simultaneously determines the parts and tasks, the number of
stations and the cycle time. Duta et al. (2008b) consider the
problem of Disassembly Line Balancing in Real time (DLBP-R) and
propose a mixed integer quadratic programming and branch and
cut algorithm-based method. Koc et al. (2009) develop IP and DP
formulations for DLBP by using an AND/OR graph to check the
feasibility of the precedence relations among the tasks.
5.4. Disassembly to order systems
The objective of the Disassembly to Order Systems (DTOs) is the
determination of the optimal lot sizes of EOL products to disassemble in order to satisfy the demand of various components from
a mix of different product types that have a number of components
and/or modules in common (Lambert and Gupta, 2002).
The first line of research involves the heuristics developed under
the assumption of deterministic disassembly yield. Lambert and
Gupta (2002) develop a method called tree network model by
modifying the disassembly graph method for a multi product
demand driven disassembly system with commonality and multiplicity. Kongar and Gupta (2002) propose a single period integer GP
model for a DTO system to determine the best combination of
multiple products to selectively disassemble to meet the demand
for items and materials under a variety of physical, financial and
environmental constraints and goals. Kongar and Gupta (2006a)
extend Kongar and Gupta (2002) by using fuzzy GP to model the
fuzzy aspiration levels of various goals. Langella (2007) develops
a multi period heuristic considering holding costs and external
procurement of items. Gupta et al. (in press) use NN to solve the
DTO problem. Kongar and Gupta (2009) propose a LPP-based
solution methodology which can satisfy tangible or intangible
financial, environmental and performance related measures of DTO
systems. Kongar and Gupta (in press) develop a multi objective TS
algorithm by considering multiple objective functions, viz. maximizing the total profit, maximizing the resale/recycling percentage,
and minimizing the disposal percentage.
The papers in the second line of research take into consideration
the uncertainty related with disassembly yield. Inderfurth and
Langella (2006) develop two heuristic procedures (i.e., one-to-one,
one-to-many) to investigate the effect of stochastic yields on the
DTO system. Imtanavanich and Gupta (2006) use the heuristic
procedures developed by Inderfurth and Langella (2006) to deal
with the stochastic elements of the DTO system. Then, they use a GP
procedure to determine the number of returned products that
satisfy various goals.
5.5. Automation
The majority of the existing disassembly plants are based on the
manual labor. However, increase in the amount of electronic scrap,
the demand for higher productivity levels and increase in labor cost
force firms to automate their disassembly processes (Santochi et al.,
2002; Kopacek and Kopacek, 2006). That is why the researchers
studied different aspects of disassembly automation in recent
years. Seliger et al. (2002) develop modular disassembly processes
and tools to be used in an integrated disassembly cell controlled by
product accompanying information systems. Torres et al. (2004)
present a semi-automated personal computer disassembly cell
composed of several sub-systems. A computer vision subsystem is
used for the recognition and localisation of the product and of each
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Table 2
Classification of references based on problem domain.
Category
PD
RCLSC
REM
References
DFX
DFE
Kalisvaart and van der Horst, 1995; Cristofari et al., 1996; Fiksel, 1996; Billatos and Basaly, 1997; Feldmann et al., 1999; Zhang et al., 1999;
Hundal, 2001; Mehta and Wang, 2001; Santos-Reyes and Lawlor-Wright, 2001; Knight and Curtis, 2002; Lye et al., 2002;
Madu et al., 2002; Kuo and Wu, 2003; Masui et al., 2003; Bhamra, 2004; Giudice et al., 2006; Hopkinson et al., 2006;
Kuo et al., 2006; Bevilacqua et al., 2007; Boks and Stevels, 2007; Bovea and Wang, 2003, 2007; Grote et al., 2007; Johansson et al., 2007; Sakao,
2007; Li et al., 2008b; Mascle and Zhao, 2008; Mathieux et al., 2008; Park and Tahara, 2008; Platcheck et al., 2008; Qian and Zhang, 2009
DFD Kroll and Hanft, 1998; Kroll and Carver, 1999; Veerakamolmal and Gupta, 1999; Das et al., 2000; Chen, 2001; Ferrer, 2001; Viswanathan and
Allada, 2001, 2006; Desai and Mital, 2003a, 2005; Villalba et al., 2004; Banda and Zeid, 2006; Gungor, 2006; Mital and Desai, 2007; Chu et al., 2009;
Kwak et al., 2009; Giudice and Kassem, in press
DFR Coulter et al., 1998; Pento, 1999; Knight and Sodhi, 2000; Boon et al., 2002; Liu et al., 2002; Ardente et al., 2003; Wright et al., 2005;
Ferrao and Amaral, 2006; Houe and Grabot, 2007; Masanet and Horvath, 2007
LCA
Zhang et al., 1999; Mehta and Wang, 2001; Arena et al., 2003; Zhu and Deshmukh, 2003; Giudice et al., 2006; Kuo, 2006; Nakamura and
Kondo, 2006; Bevilacqua et al., 2007; Boks and Stevels, 2007; Bovea and Wang, 2003, 2007; Grote et al., 2007; Sakao, 2007; Li et al., 2008a
MS
Isaacs and Gupta, 1997; Holloway, 1998; Sodhi et al., 1999; Knight and Sodhi, 2000; Boon et al., 2001, 2003; Bandivadekar et al., 2004;
Villalba et al., 2002, 2004; Giudice et al., 2005; Chan and Tong, 2007; Williams et al., 2007; Kumar and Sutherland, 2008; Tseng et al., 2008;
Zhou et al., 2009; Huang et al., 2009a
ND
DM Barros et al., 1998; Jayaraman et al., 1999; Krikke et al., 1999b; Louwers et al., 1999; Realff et al., 1999; Fleischmann et al., 2000, 2001; Shih,
2001; Hu et al., 2002; Jayaraman et al., 2003; Schultmann et al., 2003; Beamon and Fernandes, 2004; Savaskan et al., 2004; Sim et al., 2004;
Amini et al., 2005; Fernandez and Kekale, 2005; Salema et al., 2005, 2007; Sheu et al., 2005; Zhou et al., 2005; Bautista and Pereira, 2006;
Bhattacharya et al., 2006; Bian and Yu, 2006; Min et al., 2006a,b; Savaskan and Van Wassenhove, 2006; Karakayali et al., 2007; Ko and Evans, 2007;
Lee et al., 2007a,b, 2009; Lu and Bostel, 2007; Uster et al., 2007; Wang et al., 2007; Wang and Yang, 2007; Wojanowski et al., 2007; Aras et al.,
2008; Aras and Aksen, 2008; de Figueiredo and Mayerle, 2008; Demirel and Gökçen, 2008; Du and Evans, 2008; Hong et al., 2008; Kannan et al.,
2008; Lee and Dong, 2008; Min and Ko, 2008; Pati et al., 2008; Pochampally and Gupta, 2008; Srivastava, 2008a,b; Tuzkaya and Gülsün, 2008;
Walther et al., 2008; Cruz-Rivera and Ertel, 2009; Dehghanian and Mansour, 2009; Mutha and Pokharel, 2009; Pochampally and Gupta, 2009;
Yang et al., 2009; Kannan et al., 2009a, in press; Pishvaee et al., in press; Salema et al., in press; Wang and Hsu, 2010
SM
Realff et al., 2000a, 2004; Listes and Dekker, 2005; Hong et al., 2006; Lee et al., 2007c; Lieckens and Vandaele, 2007; Listes, 2007; Chouinard et al.,
2008; Lee and Dong, 2009; Qin and Ji, in press
SCNPD
Krikke et al., 2003, 2004; Fernandez and Kekale, 2005
OTG
Mourao and Almeida, 2000; Mourao and Amado, 2005; Dethloff, 2001, 2002; Blanc et al., 2004, 2006; Schultmann et al., 2006; Alshamrani et al.,
2007; Gribkovskaia et al., 2007; Aras et al., 2008; Krikke et al., 2008; Kim et al., 2009a
SUP
Veerakamolmal and Gupta, 1999; Pochampally and Gupta, 2005, 2008; Pochampally et al., 2009b,c
SES
Sarkis, 1998, 1999; Meade and Sarkis, 2002; Ravi et al., 2005; Lu et al., 2007; Presley et al., 2007; Rao, 2007, 2009; Efendigil et al., 2008;
Ravi et al., 2008; Kannan, 2009; Pochampally et al., 2009b; Tsai and Hung, 2009; Kannan et al., 2009b; Saen, in press
PM
Sarkis and Cordeiro, 2001; Wagner et al., 2001; Georgiadis and Vlachos, 2004; Sarkis and Talluri, 2004; Zhu and Sarkis, 2004; Hervani et al.,
2005; Rao and Holt, 2005; Zhu et al., 2005, 2007; Kainuma and Tawara, 2006; Biehl et al., 2007; Kara et al., 2007;
Georgiadis and Besiou, 2008; Pochampally et al., 2009a
MRI
Majumder and Groenevelt, 2001; Guide et al., 2003; Mukhopadhyay and Setoputra, 2004, 2005, 2006; Debo et al., 2005; Heese et al., 2005;
Robotis et al., 2005; Yalabik et al., 2005; Yao et al., 2005; Ferguson and Toktay, 2006; Vorasayan and Ryan, 2006; Karakayali et al., 2007;
Mitra, 2007; Vadde et al., 2007; Webster and Mitra, 2007; Mitra and Webster, 2008; Qiaolun et al., 2008; Qu and Williams, 2008;
Liang et al., 2009; Ferrer and Swaminathan, 2006, in press
EOALS
Krikke et al., 1998, 1999a,b; Rose and Ishii, 1999; Lee et al., 2001b; Das and Yedlarajiah, 2002; Mangun and Thurston, 2002; Hula et al., 2003;
Bufardi et al., 2004; Jorjani et al., 2004; Rahimifard et al., 2004; Gonzalez and Adenso-Diaz, 2005; Ritchey et al., 2005; Kleber, 2006; Shih et al.,
2006; Teunter, 2006; Willems et al., 2006; Bakar and Rahimifard, 2007; Jun et al., 2007; Kumar et al., 2007; Staikos and Rahimifard, 2007a,b; Chan,
2008; Fernandez et al., 2008; Gehin et al., 2008; Tan and Kumar, 2008; Zuidwijk and Krikke, 2008; Wadhwa et al., 2009; Iakovou et al., 2009
PAM
Guide and Jayaraman, 2000; Guide and Van Wassenhove, 2001; Guide et al., 2003; Guide and Pentico, 2003; Wojanowski et al., 2007; Aras and
Aksen, 2008; Aras et al., 2008; Ostlin et al., 2008; Aksen et al., 2009; Kaya, 2010
OTH
Krumwiede and Sheu, 2002; Daugherty et al., 2005; Dhanda and Hill, 2005; Horvath et al., 2005; Ravi and Shankar, 2005; Chan, 2007;
Serrato et al., 2007; Sharma et al., 2007
FRCST
Kelle and Silver, 1989; Toktay et al., 2000; Linton et al., 2002, 2005; Marx-Gomez et al., 2002; Linton and Yeomans, 2003; Toktay et al., 2004;
de Brito and van der Laan, 2009
PP
Ferrer and Whybark, 2001; Gupta and Veerakamolmal, 2001; Souza et al., 2002; Souza and Ketzenberg, 2002; Jayaraman, 2006;
Kim et al., 2006d; Lu et al., 2006; DePuy et al., 2007; Li et al., 2009; Xanthopoulos and Iakovou, 2009
PS
Guide, 1996, 1997; Guide et al., 1997a,b, 1998, 1999b, 2000, 2005b ; Guide and Srivastava, 1997a
CP
Guide and Spencer, 1997; Guide et al., 1997c; Kim et al., 2005; Franke et al., 2006; Georgiadis et al., 2006; Vlachos et al., 2007;
Georgiadis and Athanasiou, in press
IM
IMOD Schrady, 1967; Heyman, 1977; Simpson, 1978; Nahmias and Rivera, 1979; Muckstadt and Isaac, 1981; Mabini et al., 1992; Richter, 1996a,b,
1997; van der Laan et al., 1996a,b, 1999a,b; Inderfurth, 1997, 2004; van der Laan and Salomon, 1997; Korugan and Gupta, 1998; Richter and
Dobos, 1999; Dobos and Richter, 2000, 2003, 2004, 2006; Richter and Sombrutzki, 2000; Toktay et al., 2000; Golany et al., 2001; Heisig and
Fleischmann, 2001; Inderfurth et al., 2001; Minner and Kleber, 2001; Richter and Weber, 2001; Teunter, 2001, 2004; Beltran and Krass, 2002;
Fleischmann et al., 2002; Kleber et al., 2002; Koh et al., 2002; Nakashima et al., 2002, 2004; Bayindir et al., 2003; Ching et al., 2003;
Fleischmann and Kuik, 2003; Kiesmüller, 2003a,b; Kiesmuller and Minner, 2003; Kiesmüller and Scherer, 2003; Mahadevan et al., 2003;
Richter and Gobsch, 2003; Vlachos and Dekker, 2003; DeCroix et al., 2005; DeCroix and Zipkin, 2005; Tang and Grubbström, 2005;
Yang et al., 2005; Aras et al., 2006; DeCroix, 2006; Konstantaras and Papachristos, 2006; Mostard and Teunter, 2006; Oh and Hwang, 2006;
Tang and Teunter, 2006; Teunter et al., 2006, 2008; van der Laan and Teunter, 2006; Wee et al., 2006; Zanoni et al., 2006; Konstantaras and
Papachristos, 2007; Takahashi et al., 2007; Bera et al., 2008; Chung et al., 2008; El Saadany and Jaber, 2008, in press; Jaber and Rosen, 2008;
Rubio and Corominas, 2008; Jaber and El Saadany, 2009; Roy et al., 2009; Jaber and El Saadany, in press
CV
Teunter et al., 2000; Teunter and van der Laan, 2002; van der Laan, 2003; Tang et al., 2004; Corbacioglu and van der Laan, 2007; Akcali and
Bayindir, 2008
ELT
Inderfurth, 1997; van der Laan et al., 1999a,b; Inderfurth and van der Laan, 2001; Ferrer, 2003; Mahadevan et al., 2003; Ferrer and
Ketzenberg, 2004; Tang and Naim, 2004; Teunter et al., 2004; Zhou et al., 2006; Zhou and Disney, 2006; Tang et al., 2007; Bao et al., 2008
IS
Inderfurth, 2004; Bayindir et al., 2005; Yongjian et al., 2006; Bayindir et al., 2007
SP
Fleischmann et al., 2003; Spengler and Schroeter, 2003; Schroter and Spengler, 2004; Ilgin and Gupta, 2008a,b; Inderfurth and Mukherjee, 2008
EU
Guide and Srivastava, 1997b, 1998; Helber, 1998; Minner, 2001; Souza et al., 2002; Ferrer, 2003; Ketzenberg et al., 2003, 2006; Aras et al., 2004,
2006; Ferrer and Ketzenberg, 2004; Tang and Naim, 2004; Aksoy and Gupta, 2005; Guide et al., 2005a, 2006; Inderfurth, 2005; Atasu and
Çetinkaya, 2006; Bakal and Akcali, 2006; Galbreth and Blackburn, 2006; Zhou et al., 2006; Zhou and Disney, 2006; Geyer et al., 2007;
Zikopoulos and Tagaras, 2007, 2008; Tagaras and Zikopoulos, 2008; Behret and Korugan, 2009; Huang et al., 2009a; Ketzenberg, 2009
M.A. Ilgin, S.M. Gupta / Journal of Environmental Management 91 (2010) 563–591
581
Table 2 (continued).
Category
DIS
References
SCH
SEQ
DLB
DTOS
AUTO
ERGO
Gupta and Taleb, 1994; Taleb et al., 1997; Taleb and Gupta, 1997; Meacham et al., 1999; Lee et al., 2002, 2004; Kim et al., 2003, 2006a,b,c, 2009d;
Stuart and Christina, 2003; Lee and Xirouchakis, 2004; Rios and Stuart, 2004; Brander and Forsberg, 2005; Barba-Gutierrez et al., 2008
Gungor and Gupta, 1997, 1998, 2001b; Lambert, 1997, 1999, 2006, 2007; Zeid et al., 1997; Moore et al., 1998, 2001; Hsin-Hao et al., 2000;
Kaebernick et al., 2000; Kuo, 2000; Zha and Lim, 2000; Zussman and Zhou, 1999, 2000; Erdos et al., 2001; Pan and Zeid, 2001; Seo et al., 2001; Tang
et al., 2001, 2006; Tiwari et al., 2001; Rai et al., 2002; Veerakamolmal and Gupta, 2002; Kang et al., 2003; Kumar et al., 2003; Mascle and Balasoiu,
2003; Singh et al., 2003; Torres et al., 2003; Gao et al., 2004; Li et al., 2005, 2006; Chung and Peng, 2006; Dong et al., 2006; Gonzalez and AdensoDiaz, 2006; Kongar and Gupta, 2006b, in press; Sarin et al., 2006; Giudice and Fargione, 2007; Reveliotis, 2007; Shimizu et al., 2007; Adenso-Diaz
et al., 2008; Duta et al., 2008a; Hui et al., 2008; Lambert and Gupta, 2008; Tripathi et al., 2009; Grochowski and Tang, 2009
Gungor and Gupta, 2001a, 2002; Agrawal and Tiwari, 2006; McGovern and Gupta, 2006, 2007a,b; Tang and Zhou, 2006; Altekin et al., 2008; Duta
et al., 2008b; Koc et al., 2009
Kongar and Gupta, 2002, 2006a, in press, 2009; Lambert and Gupta, 2002; Imtanavanich and Gupta, 2006; Inderfurth and Langella, 2006; Langella,
2007; Gupta et al., in press
Wiendahl et al., 2001; Santochi et al., 2002; Seliger et al., 2002; Torres et al., 2004; Kopacek and Kopacek, 2006; Weigl-Seitz et al., 2006; Kim et al.,
2007a, 2009b,c; Duta and Filip, 2008
Kroll, 1996; Takata et al., 2001; Bley et al., 2004; Desai and Mital, 2005; Kazmierczak et al., 2004, 2005, 2007; Tang et al., 2006; Tang and Zhou,
2008
AUTO: automation; CP: capacity planning; CV: costs and valuation; DFD: design for disassembly; DFE: design for environment; DFR: design for recycling; DFX: design for X;
DIS: disassembly; DLB: disassembly line balancing; DM: deterministic models; DTOS: disassembly to order systems; ELT: effect of lead time; EOALS: end of life alternative
selection; ERGO: ergonomics; EU: effect of uncertainty; FRCST: forecasting; IM: inventory management; IMOD: inventory models; IS: inventory substitution; LCA: life cycle
analysis; MRI: marketing-related issues; MS: material selection; ND: network design; OTG: optimization of transportation of goods; OTH: other issues; PAM: product
acquisition management; PD: product design; PM: performance measurement; PP: production planning; PS: production scheduling; RCLSC: reverse & closed-loop supply
chains; REM: remanufacturing; SCH: scheduling; SCNPD: simultaneous consideration of network and product design; SEQ: sequencing; SES: selection and evaluation of
suppliers; SM: stochastic models; SP: spare parts; SUP: selection of used products.
of its components. Disassembly sequence and planning of the
disassembly movements are provided by a modeling subsystem.
Weigl-Seitz et al. (2006) discuss the required equipment and suitable strategies for the automated disassembly by considering the
disassembly of a video camera recorder and PC. They also propose
a disassembly line layout with the suitable software structures.
Robotized, semi-automatised, flexible disassembly cells for minidisks, PCBs and mobile phones in industrial use are discussed by
Kopacek and Kopacek (2006). A modular approach is also proposed
for disassembly cells by integrating disassembly families, mobile
robots and Multi Agent Systems (MAS). Kim et al. (2009b) investigate the advantages of emulation in control logic development
and validation of new conceptual disassembly systems. Kim et al.
(2007a) develop an adaptive and modular control system for the
generation of automatic control sequences for a partly automated
system by considering the availability of disassembly tools and the
technological feasibility of disassembly processes and tools. Kim
et al. (2009c) propose a dynamic process planning procedure which
generates available alternatives by a database-supported procedure
and selects the best suitable among them if a device or tool is not
available. Software tools developed to optimize the disassembly
process of discarded goods are discussed in Santochi et al. (2002).
Wiendahl et al. (2001) present an overview of layouts and modules
of automated disassembly systems developed at various companies
and research institutes. Duta and Filip (2008) investigate several
solutions for the design of robotic disassembly cells.
5.6. Ergonomics
The hands-on nature of disassembly tasks requires the consideration of ergonomic factors in the design of disassembly lines.
However, the number of studies on the ergonomics of disassembly
is very small. Kazmierczak et al. (2004) analyze the current situation and future perspectives for the ergonomics of car disassembly
in Sweden using several explorative methods such as site visits,
interviews. In a follow-up paper, Kazmierczak et al. (2005) analyze
disassembly work in terms of time and physical work load
requirements of constituent tasks. Kazmierczak et al. (2007)
combine human and flow simulations to predict the performance
of alternative system configurations in terms of productivity and
ergonomics for a serial-flow car disassembly line. In order to
address the uncertainty due to manual operations in disassembly,
Tang et al. (2006) and Tang and Zhou (2008) define the effect of
several human factors (e.g., disassembly time, quality of disassembled components, and labor cost) as membership functions
in their fuzzy attributed PN models. Human involvement in disassembly was investigated by Bley et al. (2004) and Takata et al.
(2001).
Kroll (1996) applies Maynard Operation Sequence Technique
(MOST) to determine the difficulty scores of standard disassembly
tasks. Desai and Mital (2005) use Methods Time Measurement
(MTM) to calculate the ease of disassembly scores for disassembly
tasks.
6. Conclusions
This paper presented a review of the state of the art literature on
Environmentally Conscious Manufacturing and Product Recovery
(ECMPRO) published since the 1999 review by Gungor and Gupta
(1999). Table 2 shows the cited references organized into appropriate categories. The following general conclusions can be drawn
from our literature review:
Environmental issues have an increasing popularity among
researchers. Hence, in recent years, there is a significant
increase in the number of studies on ECMPRO.
Product Design research mainly focuses on multi criteria techniques which allow for the simultaneous consideration of
environmental, economic, consumer and material requirements. However, the environmental impact of production
processes is ignored in most of these studies. Thus, there is
a need for environmentally conscious product design methodologies that integrate design of products and processes.
Reverse Logistics (RL) literature is dominated by the studies on
location-allocation models. In order to develop a more integrated RL framework, more research is necessary on other
issues such as marketing, competition, and technology.
Remanufacturing systems are often analyzed by considering
only one specific operations management issue (e.g., inventory
management or production planning). In order to have a more
realistic analysis of these systems, integrated methodologies
should be developed.
582
M.A. Ilgin, S.M. Gupta / Journal of Environmental Management 91 (2010) 563–591
Table 3
Classification of references based on chosen OR/IE technique.
Method References
ABC
ACO
AHP
Presley et al., 2007; Tsai and Hung, 2009
Agrawal and Tiwari, 2006; McGovern and Gupta, 2006; Tripathi et al., 2009
Mehta and Wang, 2001; Santos-Reyes and Lawlor-Wright, 2001; Liu et al., 2002; Lye et al., 2002; Madu et al., 2002; Bian and Yu, 2006; Kuo et al., 2006;
Lu et al., 2007; Presley et al., 2007; Staikos and Rahimifard, 2007a,b; Kannan et al., 2008; Li et al., 2008b; Pochampally and Gupta, 2008; Dehghanian and
Mansour, 2009; Kannan, 2009; Qian and Zhang, 2009; Rao, 2009; Tsai and Hung, 2009
ANOVA Zhu et al., 2007
ANP
Sarkis, 1998; Sarkis, 1999; Meade and Sarkis, 2002; Ravi et al., 2005; Gungor, 2006; Ravi et al., 2008; Tuzkaya and Gülsün, 2008
B&B
Gungor and Gupta, 2001b; Kang et al., 2003; Sarin et al., 2006; Salema et al., 2007; Kim et al., 2009d
BDN
Zhu and Deshmukh, 2003; Grochowski and Tang, 2009
BSC
Ravi et al., 2005; Presley et al., 2007
CBR
Zeid et al., 1997; Veerakamolmal and Gupta, 2002; Shih et al., 2006
DEA
Sarkis, 1999; Sarkis and Cordeiro, 2001; Sarkis and Talluri, 2004; Park and Tahara, 2008; Saen, in press
DES
Guide, 1996, 1997; Guide and Spencer, 1997; Guide and Srivastava, 1997a,b, 1998; Guide et al., 1998; Guide et al., 1997a,b,c, 1999b, 2000, 2005b;
Teunter et al., 2000; Linton et al., 2002; Marx-Gomez et al., 2002; Souza and Ketzenberg, 2002; Souza et al., 2002; Fleischmann et al., 2003; Ketzenberg
et al., 2003; Mahadevan et al., 2003; Schultmann et al., 2003; Stuart and Christina, 2003; Rios and Stuart, 2004; Aras et al., 2006; Blanc et al., 2006;
Franke et al., 2006; Zanoni et al., 2006; Biehl et al., 2007; Kara et al., 2007; Kazmierczak et al., 2007; Ilgin and Gupta, 2008a,b; Krikke et al., 2008;
Behret and Korugan, 2009; Huang et al., 2009b; Ketzenberg, 2009; Li et al., 2009
DP
Inderfurth, 1997; Krikke et al., 1998, 1999a; Sodhi et al., 1999; Kiesmüller and Scherer, 2003; Richter and Gobsch, 2003; Yang et al., 2005; Inderfurth
and Mukherjee, 2008; Ketzenberg, 2009; Koc et al., 2009
EA
Jun et al., 2007
ELECTRE Bufardi et al., 2004
ES
Zha and Lim, 2000; Kumar et al., 2003; Singh et al., 2003; Grochowski and Tang, 2009
EP
Viswanathan and Allada, 2006
FL
Marx-Gomez et al., 2002; Gao et al., 2004; Kongar and Gupta, 2006a; Kuo et al., 2006; Tang et al., 2006; Lee et al., 2007a; Li et al., 2008b; Lu et al., 2007;
Bera et al., 2008; Efendigil et al., 2008; Fernandez et al., 2008; Mascle and Zhao, 2008; Pochampally and Gupta, 2008; Tang and Zhou, 2008; Tuzkaya and
Gülsün, 2008; Kannan, 2009; Pochampally and Gupta, 2009; Qian and Zhang, 2009; Rao, 2009; Roy et al., 2009; Tripathi et al., 2009; Wadhwa et al., 2009;
Kannan et al., 2008; Qin and Ji, in press
GP
Boon et al., 2001, 2003; Kongar and Gupta, 2002, 2006a; Imtanavanich and Gupta, 2006; Lee et al., 2007a; Shimizu et al., 2007; Pati et al., 2008;
Ravi et al., 2008; Tsai and Hung, 2009; Xanthopoulos and Iakovou, 2009
GA
Seo et al., 2001; Hula et al., 2003; Sim et al., 2004; Li et al., 2005, 2009; Zhou et al., 2005, 2009; Bautista and Pereira, 2006; Chung and Peng, 2006;
Ferrao and Amaral, 2006; Kongar and Gupta, 2006b; Min et al., 2006a,b; Bovea and Wang, 2007; Giudice and Fargione, 2007; Ko and Evans, 2007;
Lee et al., 2007a,b, 2009; Lieckens and Vandaele, 2007; Duta et al., 2008a; Hui et al., 2008; Ilgin and Gupta, 2008a; Chu et al., 2009; Dehghanian and
Mansour, 2009; Kannan et al., 2009a, in press; Roy et al., 2009; Tripathi et al., 2009; Gupta et al., in press; Qin and Ji, in press; Wang and Hsu, 2010
GRA
Kuo and Wu, 2003; Chan and Tong, 2007; Chan, 2008
GT
Majumder and Groenevelt, 2001; Savaskan et al., 2004; Debo et al., 2005; Heese et al., 2005; Yao et al., 2005; Bhattacharya et al., 2006; Ferguson and Toktay,
2006; Ferrer and Swaminathan, 2006; Savaskan and Van Wassenhove, 2006; Webster and Mitra, 2007; Mitra and Webster, 2008; Qiaolun et al., 2008
IP
Veerakamolmal and Gupta, 1999; Knight and Sodhi, 2000; Gupta and Veerakamolmal, 2001; Pochampally and Gupta, 2005; Lambert, 2006, 2007; Langella,
2007; Wang et al., 2007; Lambert and Gupta, 2008; Zuidwijk and Krikke, 2008; Koc et al., 2009; Pochampally et al., 2009b; Wang and Hsu, 2010
LP
Lambert, 1999; Sim et al., 2004; Franke et al., 2006; Jayaraman, 2006; Tan and Kumar, 2008; Walther et al., 2008
LPP
Kongar and Gupta, 2009; Pochampally et al., 2009a,c
MC
Helber, 1998; Nakashima et al., 2002; Ching et al., 2003; Fleischmann and Kuik, 2003; Aras et al., 2004; Nakashima et al., 2004; Horvath et al., 2005; Serrato et al., 2007
MIP
Realff et al., 1999, 2000a,b, 2004; Fleischmann et al., 2001; Shih, 2001; Das and Yedlarajiah, 2002; Beamon and Fernandes, 2004; Blanc et al., 2004;
Zhou et al., 2005; Hong et al., 2006; Inderfurth and Langella, 2006; Kim et al., 2006d; Lu et al., 2006; Min et al., 2006a,b; Tang and Teunter, 2006;
Ko and Evans, 2007; Lee et al., 2007c; Lieckens and Vandaele, 2007; Salema et al., 2007; Sharma et al., 2007; Uster et al., 2007; Wang and Yang, 2007;
Williams et al., 2007; Altekin et al., 2008; Aras et al., 2008; Aras and Aksen, 2008; Demirel and Gökçen, 2008; Du and Evans, 2008; Duta et al., 2008b;
Srivastava, 2008a,b; Teunter et al., 2008; Aksen et al., 2009; Xanthopoulos and Iakovou, 2009; Kannan et al., in press; Pishvaee et al., in press
NLP
Qu and Williams, 2008
NN
Hsin-Hao et al., 2000; Liu et al., 2002; Marx-Gomez et al., 2002; Efendigil et al., 2008; Li et al., 2008a; Pochampally and Gupta, 2009; Zhou et al., 2009; Gupta et al.,
in press
NS
Lee and Dong, 2008
PN
Moore et al., 1998; Zussman and Zhou, 1999, 2000; Zha and Lim, 2000; Moore et al., 2001; Tang et al., 2001, 2006; Tiwari et al., 2001; Rai et al., 2002; Kumar et al.,
2003; Singh et al., 2003; Gao et al., 2004; Tang and Zhou, 2006, 2008; Reveliotis, 2007; Grochowski and Tang, 2009
PSO
Kannan et al., 2009a
QFD
Cristofari et al., 1996; Zhang et al., 1999; Mehta and Wang, 2001; Madu et al., 2002; Bovea and Wang, 2003; Kuo and Wu, 2003; Masui et al., 2003; Presley et al.,
2007; Sakao, 2007; Pochampally et al., 2009a
QT
Korugan and Gupta, 1998; Toktay et al., 2000; Souza and Ketzenberg, 2002; Souza et al., 2002; Bayindir et al., 2003; Aksoy and Gupta, 2005; Guide et al., 2005b;
Vorasayan and Ryan, 2006
RA
Zhu and Sarkis, 2004
RO
Realff et al., 2000b, 2004; Hong et al., 2006
SA
Lee and Dong, 2009; Pishvaee et al., in press
SDS
Spengler and Schroeter, 2003; Bandivadekar et al., 2004; Georgiadis and Vlachos, 2004; Schroter and Spengler, 2004; Tang and Naim, 2004; Georgiadis et al.,
2006; Tang and Zhou, 2006; Zhou et al., 2006; Zhou and Disney, 2006; Vlachos et al., 2007; Georgiadis and Besiou, 2008; Georgiadis and Athanasiou, in press
SP
Listes and Dekker, 2005; Chouinard et al., 2008; Lee et al., 2007c; Lee and Dong, 2009
SS
Gonzalez and Adenso-Diaz, 2005, 2006; Du and Evans, 2008
TOPSIS Rao, 2007; Tuzkaya and Gülsün, 2008; Pochampally and Gupta, 2009; Pochampally et al., 2009b; Wadhwa et al., 2009; Kannan et al., 2009b
TS
Schultmann et al., 2006; Gribkovskaia et al., 2007; Aras et al., 2008; Aras and Aksen, 2008; Lee and Dong, 2008; Aksen et al., 2009; Kim et al., 2009a;
Kongar and Gupta, in press.
WP
Mascle and Balasoiu, 2003
ABC: activity based costing; ACO: ant colony optimization; AHP: analytic hierarchy process; ANOVA: analysis of variance; ANP: analytic network process; B&B: Branch & bound;
BDN: Bayesian decision networks; BSC: balanced scorecard; CBR: case based reasoning; DEA: data envelopment analysis; DES: discrete event simulation; DP: dynamic
programming; EA: evolutionary algorithms; ELECTRE: elimination and choice translating reality; EP: evolutionary programming; ES: expert systems; FL: fuzzy logic; GP: genetic
programming; GA: genetic algorithms; GRA: grey relational analysis; GT: game theory; IP: integer programming; LP: linear programming; LPP: linear physical programming;
MC: Markov chains. MIP: mixed integer programming; NLP: non-linear programming; NN: neural networks; NS: neighbourhood search; PN: petri nets; PSO: particle swarm
optimization; QFD: quality function deployment; QT: queueing theory; RA: regression analysis; RO: robust optimization; SA: simulated annealing; SDS: system dynamics
simulation; SP: stochastic programming; SS: scatter search; TOPSIS: technique for order preference by similarity to an ideal solution TS: tabu search; WP: wave propagation.
M.A. Ilgin, S.M. Gupta / Journal of Environmental Management 91 (2010) 563–591
Disassembly sequencing and scheduling are widely studied
areas by the researchers. Disassembly automation also received
attention of researchers in recent years. Although a significant
portion of the current disassembly systems is based on manual
labor, the research on the ergonomics of disassembly is not
well developed.
A high degree of uncertainty exists in RL, remanufacturing and
disassembly systems. The most popular approaches to deal
with this uncertainty are simulation, stochastic programming,
robust optimization, sensitivity and scenario analysis. More
studies are needed to better control the effects of uncertainties.
Majority of the studies use quantitative analysis. A broad
spectrum of Industrial Engineering (IE) – Operations Research
(OR) techniques were utilized in the studies. Table 3 gives
a classification of reviewed papers based on the use of a specific
IE or OR technique. There is an increasing trend in the use of
artificial intelligence techniques including metaheuristics,
fuzzy systems, and neural networks.
With stricter environmental regulations and increased environmental awareness in society, firms must educate their
employees in environmental aspects of manufacturing to increase
their competitive edge. Moreover, ECM principles should be
incorporated into engineering curriculums at universities. We refer
the reader to Borchers et al. (2006) and Kumar et al. (2005) for
more information on the design of courses and programs with an
ECM perspective.
While ECMPRO was in its infancy 10 years ago, today it is a well
established and growing research area. The main reason for this
rapid development is the increasing attention of governments,
firms and academicians toward environmental issues such as
decreasing natural resources and global warming. In this developmental period, research mainly focused on operational and tactical
issues in order to satisfy the immediate needs of firms. There is
a need to develop strategic models for the analysis of ECMPRO
systems with respect to technological and organizational dynamics.
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