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Periodica Polytechnica
Mechanical Engineering
x(y) pp. x-x, (year)
doi: 10.3311/pp.me.201x­x.xx
web:
http://www.pp.bme.hu/me
© Periodica Polytechnica year
RESEARCH ARTICLE
DES Configurators for Rapid Virtual
Prototyping and Optimisation of
Manufacturing Systems
Csaba HARASZKÓ1, István NÉMETH1
Received 201x-xx-xx
Abstract
In the framework of the COPERNICO research project
configurators were developed under the Siemens
Tecnomatix Plant Simulation software environment, which
support the rapid creation of the discrete event simulation
(DES) models of several types of manufacturing systems.
With the use of the configurators after defining the
required input data – such as the product mix, production
processes, layout prototype, etc. – the DES models of the
manufacturing system alternatives will be automatically
created. The layout prototypes are based on a cladistics
classification of manufacturing systems. The aim of the
configurators is not only to purely create the simulation
models but also to create models that can be used for
further optimisations, that can be done e.g. by using the
built in genetic algorithms tool, based on specific
objectives. In this paper some research results are
demonstrated on how the configurators create DES
models and how the models can be applied.
Keywords
DES configurators, manufacturing systems, cladistic
classification, discrete event simulation, product sequence
optimisation
Acknowledgement
The authors acknowledge the support of the European
Commission through the 7th Framework Programme
under NMP-2008-3.4-1 Rapid Design and Virtual
Prototyping of Factories (COPERNICO; contract number
229025-2). The authors are grateful for James S. Baldwin
who was the cladistics expert of the COPERNICO project.
The size of this paper does not allow us to include his
genius cladistic classification system.
1
Department of Manufacturing Science and Engineering
Faculty of Mechanical Engineering
Budapest University of Technology and Economics
Műegyetem rkp. 3., H-1111 Budapest, Hungary
(e-mail: [email protected], [email protected])
DES Configurators for Rapid Virtual Prototyping
1 Introduction
Today the design or re-design process of
manufacturing systems requires wider and deeper
integration of design methodologies and software tools.
The design process should include the synthesis, analysis
and optimisation tasks integrating the equipment selection
and the layout planning activities as well. In order to
achieve that integrated models should be developed that
represent the products, processes and manufacturing
equipment in a virtual environment. Several parameters –
such as the product mix, control strategies of material
flow, layout types, parameterised existence of equipment
etc. – should be represented in these integrated models in
order to be able to optimise the key performance
indicators in a more comprehensive way. Small
companies cannot afford investing in expensive modelling
systems and employing personnel to design their new
manufacturing facilities or model and evaluate their
existing manufacturing systems. Therefore, affordable,
easy-to-use modelling tools are needed that support the
rapid design of manufacturing systems. The
COPERNICO research project [1] aimed to achieve these
goals. The major technical objectives of the project were
to develop a cladistics based classification system of
manufacturing systems, and to develop several software
tools to support the rapid design, simulation, visualisation
and evaluation of manufacturing systems. In the
framework of the project configurator software tools were
developed that support the rapid creation of the discrete
event simulation (DES) models of several types of
manufacturing systems.
The application of the DES methodologies and
software tools for the analysis part of the design process
of manufacturing systems is widespread [1][2][3][4].
Many DES systems are able to support the optimisation
part of the design process. The current DES tools should
also be extended with the capability of supporting the
synthesis part of the design process that will intensify the
diverging nature of the design process providing the
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optimisation methods with a wider search space. This
paper first presents the layout prototypes that serve the
basis for systematic layout design. These layout
prototypes were defined using the species derived by the
COPERNICO cladistic classification system. Then the
paper demonstrates the main functions of the DES
configurators that support the rapid creation and
evaluation of the DES models of manufacturing systems,
and it shows an example of performing optimisation with
automatically created DES model.
2 Layout prototypes
systems
of
manufacturing
Within the COPERNICO project a cladistics based
classification of manufacturing systems was developed.
Cladistics is an evolutionary classification scheme that
not only describes the attributes of existing entities but
also the ancestral characteristics. Cladistic classification
of manufacturing systems distinguishes present from past
systems and may assist in validating emerging new
systems. The application of cladistics to manufacturing
systems has opened up a new way to classify and link
evolutionary changes to main manufacturing changes of
the past and present. A comprehensively constructed
cladogram provides a blueprint that can be used as a guide
to help changing present structures. Using the cladistic
approach, the evolutionary relationships between fiftythree candidate species of manufacturing systems, using
‘descriptors’ drawn from a library of thirteen characters
with a total of eighty-three states, were hypothesized,
described and presented diagrammatically. The
manufacturing species were then organised in a
hierarchical classification with fifteen genera, six families
and three orders under one ‘class’ of discrete
manufacturing [5][6].
Some of the layout species were selected as layout
prototypes for rapid systematic layout design. Then
further layout sub-species (or sub-types) of each of the
selected species were identified. Configurator software
tools were developed to create the DES models of several
sub-species of the following layout species: ‘Manual
machine paced Stop & Go Line’; ‘Continuous Line’;
‘Unidirectional Line’; and ‘Robot Centred Cell’. The subspecies of the elaborated species basically are the
following: ‘Base 1’; ‘Base 2’ etc.; ‘Multi-line DependentBase’; ‘Multi-line Independent-Base’; ‘Multi-line
Independent-Dependent-Base’. It means that there are
some basic concepts, and some variations of them that can
be further extended.
3 DES configurators
The aim of the DES configurators is to automatically
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Per. Pol. Mech. Eng.
create the layout of the manufacturing systems as well as
the DES model of the manufacturing processes. It means
that the manufacturing processes (e.g. type and order of
operations) can be described, the type of manufacturing
equipment (e.g. workstations, machine tools), the type of
material flow solutions and the material handling systems
(e.g. robots, conveyors), and the layout type (species and
sub-species or sub-types) can be selected, and the product
mix can be defined. Then the arrangement of the
manufacturing system components and the complete DES
model of the manufacturing system can be automatically
created. The DES configurators have been developed
under the Siemens Tecnomatix Plant Simulation software
package [7]. By using this software multiple production
scenarios can be made and experiments can be run in
order to optimise the product sequence, throughput and
other parameters. The built-in programming language
SimTalk of Plant Simulation has been used for software
development.
On the first tab of the graphical user interface (GUI)
the products to be produced can be defined (shown in Fig.
1).
Fig. 1
DES configurator: ‘Product(s)’
After pressing the button ‘PRODUCT INFO TABLE’
the names, their sequence and the amount of each product
can be defined on a dialog matrix. This initial product
sequence may be modified later on during the application
of the generated DES model.
On the second tab of the GUI the production stations
can be configured (shown in Fig. 2). The
stations/equipment can be selected from database (using
the drop down list) or defined (by typing in data) as many
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stations (e.g. milling, drilling, turning, etc.) as needed,
then the process time table and setup time matrix for all
products on each station can also be specified. The order
of stations for each product can also be defined, but the
existence of this option depends on the layout type. Based
on the defined stations the components of the
manufacturing system model will be automatically
created and parameterised by the configurator later on. In
conclusion we have the capability to easily generate huge
models by using the configurator instead of the manual
drag & drop method.
the material flow as well; therefore, the created DES
models can be immediately used for performing
simulations or can be further modified based on several
other considerations such as 3D arrangement aspects [8].
Fig. 3
DES configurator: ‘Layout type’
4 An example of automatically created DES
model by using the configurator
Fig. 2
DES configurator: ‘Stations and Times’
On the third tab of the GUI the layout type (type of
manufacturing system layout) can be selected (shown in
Fig. 3). On this panel it is possible to select the species of
the discrete manufacturing systems identified by the
cladistics classification system. The selection can be done
by selecting the order, family, genera, and then the
species. The available species can also be selected from a
list directly.
By pressing the button ‘ADVANCED’ more detailed
options can be found such as sub-types and other species
specific parameters (such as the workers’ skill matrix that
shows which worker can perform activity on which
stations). After selecting and defining the most relevant
data described above, within this module the functional
simulation model of the system can be created easily by
pressing the button ‘GENERATE LAYOUT’.
It is important to note that the simulation models
created by the configurator contain not only topological
information of the layout but specific control logics for
DES Configurators for Rapid Virtual Prototyping
In the following section an example of automatically
created simulation model that contains evaluation and
rudimentary optimisation will be presented. This example
belongs to the ‘Stop&Go’ species that is a manual
assembly line layout (shown in Fig. 4). The sub-type is
‘Multi-line Dependent-Base’ which means that there are
parallel lines of the basic ‘Stop&Go’ concept that are
dependent because the workers can perform activities at
both lines. The following data set were specified by using
the configurator: Five products to be produced defined by
their name, amounts and sequence. Three assembly
stations on each line are defined by process times for each
product on each line and set up times. The layout species
‘Stop & Go Line’ and the sub-species ‘Multi-line
Dependent-Base’ as described above. Other species
specific feature was defined (workers skill matrix), the
optimisation objective was selected (traveling distance of
the workers).
In the simulation model the assumption is that every
station is connected to the adjacent one, the exact workers
footpaths and the length of the conveyors were neglected.
If it is needed these parameters and many others can be
easily modified when the DES model is created.
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Fig. 4
Example DES model created by the configurator; layout type ‘Stop & Go Line’, subtype ‘Dependent parallel lines’
The main elements of the created model are:
 manufacturing system elements: conveyor (Line),
workstations (Station), foot path (FootPath),
workplace (Workplace);
 data table of products scheduling (Delivery),
definitions of the products (P1-P5);
 data tables of the setup times and process times
(SetupMatrix and ProcTime);
 input and output (Source and Drain), event controller
(EventController);
 workers (WorkerPool), broker (Broker);
 other elements are created for realising the control
strategies, generating report at the end of the
simulation, etc.;
 preconfigured genetic algorithm object (GAWizard).
After the simulation is completed the resource
statistics can be analysed [9]. Based on the initial set up of
the model by using the configurator, the eventual resource
statistics is shown in Fig. 5.
The simulation model operates as follows. The
conveyors transport the products from the left to the right.
When a product reaches a station, then the broker (which
has own built in control logic) sends a competent worker
to the corresponding station. The presented example
model has 5 workers and two of them can perform
activities on two stations as it shown in Fig. 6. The
worker sets up a station and works on it until the product
is ready to go to the next workstation. If there is no other
product, the worker goes back to the worker pool and will
wait for instructions from the broker. The broker will send
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Per. Pol. Mech. Eng.
those workers to the stations who can perform activity on
them if it is preconfigured (using special skill matrix).
Other control logics can be defined, such as the
worker stays on the station until there is a product before
the station on the line which has not zero process time,
etc., and later on, when the configurators are used for
other situations, these control logics can be selected as
well.
Fig. 5
Resource statistics
If we have the model, then we can use the Genetic
Algorithm (GA) module, the so-called GAWizard, of
Plant Simulation (shown in Fig. 7) to optimise several
system parameters [7], such as throughput, throughput
time, energy consumption, buffer capacity, workers’
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travelling distances, etc., therefore many potential
objectives and their combinations may be examined.
Fig. 6
The workers’ services when defining as input (above), and in
the generated model (below)
Fig. 7
Genetic Algorithm module of Plant Simulation
Genetic algorithms (GAs) are robust methods for
search and optimisation where the optimisation
parameters must be encoded in chromosomes.
Chromosomes are data structures that hold a string of
genes. A gene can take several values that are called
alleles. At the beginning of the genetic search, a
population of individuals is initialised by randomly
selecting values from the available sets of the alleles to
the genes in the chromosomes of the individuals. Then,
each individual of the population is evaluated, where the
optimisation parameters are decoded from the
DES Configurators for Rapid Virtual Prototyping
chromosomes and the objective function is calculated
from these optimisation parameters. The result of the
objective function is called the objective value. From the
objective value of each individual of the population a
fitness value is derived with the help of an appropriate
transformation that is called fitness scaling. Next, an
iterative loop of producing subsequent populations
(generations) is entered where some individuals of an old
population are selected and their chromosomes are
recombined to form a new population, and, finally, the
individuals of the new population are evaluated. The
survival of the individuals from one generation to the
other is controlled by their fitness value. The production
process of newer populations terminates after a certain
number of generations or upon a well-defined
convergence criterion. The best individual encountered is
the solution of the search (optimisation) problem, and its
chromosome must be decoded to the space of the original
optimisation parameters. This process imitates the
evolution observed in nature. Three basic operators are
applied for the individuals of each population, such as
selection, crossover and mutation. The selection operator
selects individuals to mate based on their fitness values.
The higher the fitness value of an individual, the higher
the probability of being selected for mating. This is the
analogy of the well-known rule of the nature: the survival
of the fittest. The selected individuals, as parents, will
produce offsprings (children) using the crossover and
mutation operators. The crossover operator combines the
chromosomes of the parents with a certain probability
producing the offsprings. It is a simple information
exchange between the parents at the chromosome level.
The mutation operator randomly modifies some
information in the chromosomes of the offsprings. This
process imitates the mechanisms discovered by genetics.
[10]
Within the GAWizard the fitness function, the
optimisation parameters and some control parameters
must be defined (Fig. 7). The DES configurators generate
a preconfigured GAWizard within the DES model (see
Fig. 4), so that the user can immediately run optimisations
or he/she may change some settings. In the preconfigured
GAWizard the aggregated sum of the workers’ travelling
time was chosen directly as the fitness function and the
optimisation parameters were set to be the product
sequences specified in the Delivery tables. The
GAWizard was set for minimising this fitness value. The
default data representation of chromosomes and the
crossover and mutation operators of GAWizard were not
changed.
The model with the preconfigured GAWizard can
provide appropriate product sequences in each line and
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calculates the workers’ travelling distances. The GA will
search for the optimal product sequence for each line so
that the workers’ travelling time will be minimal. For
stochastic simulation the individuals should be evaluated
by several simulation runs, in this case we did not set
stochastic parameters in the model, therefore, the
observation per individual can be one. We can enter the
‘Number of generations’ and ‘Size of generation’ values
on the tab Define as well (Fig. 7).
In the first generation Plant Simulation evaluates the
number of generations which is entered as the ‘Size of
generation’. In each of the following generations it has to
evaluate twice as many individuals. The number of
simulation runs, which the GA module executes, results
from this formula: Number of simulation runs =
observations per individual * (generation size + 2 *
generation size * (number of generations -1)) [7]. In this
case Plant Simulation has to execute 90 simulation runs (1
* (10 + 2 * 10 * 4). The initial and the optimal product
sequences and the values of the fitness function for the
genetic algorithm are presented in Fig. 8 and Fig. 9.
and Line 2 (lower line in Fig. 4) and the value of the
optimal (minimal) workers’ travelling time (result of
Generation 5 Individual 4). Fig. 10 to Fig. 12 show the
evolution of the optimisation. Fig. 10 presents the fitness
value of the individuals of the first generation. The best
five results of the optimisation are shown in Fig. 11 where
the chromosomes (managed by the GA wizard) represent
the product sequences on each line; ‘Chrom 1’ and
‘Chrom 2’ are the best five product sequences on Line 1
and Line 2 (the best sequences are shown in Fig. 9).
We note here that the optimisation took 90 simulations
in this case. The brute-force search would take
5!*5!=14.400 simulations to calculate all the possible
sequence combination.
Fig. 10 Some intermediate results of the genetic algorithm
Fig. 8
Initial product sequence for both lines (4th column: P1 means
Product 1 etc., 3rd column: number of products); Initial fitness
value: 2:24:07.4551 (h:min:sec)
Fig. 11 The best five results of the genetic algorithm
Fig. 9
Optimal product sequences for Line1 (upper) and Line2
(lower), (4th column: P3 means Product 3 etc., 3rd column:
number of products); Optimal fitness value: 2:05:18.0192
(h:min:sec)
It can be seen that changing the product sequences on
the lines affects the travelling time (the objective
function). Fig. 9 shows the optimisation results: the
optimal product sequences on Line 1 (upper line in Fig. 4)
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Per. Pol. Mech. Eng.
Fig. 12 Performance graph of the genetic algorithm
Fig. 12 depicts the performance graph of the genetic
algorithm. The vertical axis of the performance graph
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presents the values of the fitness function, while the
horizontal axis shows the number of generations
(iterations). The red line (lower) indicates the fitness
value of the best solution in the population that represents
the optimal product sequences at the end of the
optimisation.
5 Conclusion
The paper has presented DES configurator software
tools that support the rapid creation of the DES models of
manufacturing systems. The configurators use layout
prototypes that are derived from the species of a cladistic
classification system. The created DES models contain
the layout together with the methods of the material flow
control. Such tool can significantly ease and accelerate
the systematic design of manufacturing systems since the
rapid creation of several layout alternatives can be
possible. The presented configurators are currently
software prototypes but their extended successors will be
very useful in industrial applications to help creating the
DES models of the logistics or production systems to
design their layout as well as simulate, evaluate and
optimise their performance.
The extensions of the current configurators are
planned as follows:
 Inclusion of additional layout types (species) and
looking for their sub-types (sub-species), and
development of configuration methods to create
simulation models for them;
 Further development of methods and tools for the
optimisation of some parameters of the created
simulation models;
 Development of methods to configure variants of
control logics and basic scheduling methods for the
simulation models;
 Development of more customisable GAs.
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