Planning and Scheduling
in Manufacturing and Services
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
What is Scheduling About?
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Applied operations research
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Models
Algorithms
Solution using computers
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Implement algorithms
Draw on common databases
Integration with other systems
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
2
Application Areas
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Procurement and production
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Transportation and distribution
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Information processing and
communications
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
3
Manufacturing Scheduling
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Short product life-cycles
Quick-response manufacturing
Manufacture-to-order
More complex operations must be
scheduled in shorter amount of time
with less room for errors!
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
4
Scope of Course
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Levels of planning and scheduling
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Long-range planning (several years),
middle-range planning (1-2 years),
short-range planning (few months),
scheduling (few weeks), and
reactive scheduling (now)
These functions are now often integrated
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
5
Scheduling Systems
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Enterprise Resource Planning (ERP)
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Materials Requirement Planning (MRP)
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Common for larger businesses
Very common for manufacturing companies
Advanced Planning and Scheduling (APS)
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Most recent trend
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Considered advanced feature of ERP
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
6
Scheduling Problem
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Allocate scarce resources to tasks
Combinatorial optimization problem
Maximize profit
Subject to constraints
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Mathematical techniques and heuristics
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
7
Our Approach
Scheduling Problem
Problem Formulation
Model
Solve with Computer Algorithms
Conclusions
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
8
Scheduling Models
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Project scheduling
Job shop scheduling
Flexible assembly systems
Lot sizing and scheduling
Interval scheduling, reservation,
timetabling
Workforce scheduling
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
9
General Solution Techniques
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Mathematical programming
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Enumerative methods
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Linear, non-linear, and integer programming
Branch-and-bound
Beam search
Local search
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Simulated annealing/genetic algorithms/tabu
search/neural networks.
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
10
Scheduling System Design
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Order
master file
Databases
Shop floor
data collection
Database Management
Schedule
generation
Automatic Schedule Generator
Schedule Editor
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User interfaces
Performance
Evaluation
Graphical Interface
User
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
11
LEKIN
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Generic job shop scheduling system
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User friendly windows environment
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C++ object oriented design
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Can add own routines
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
12
Advanced Topics
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Uncertainty, robustness, and reactive
scheduling
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Multiple objectives
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Internet scheduling
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
13
Topic 1
Setting up the
Scheduling Problem
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
Modeling
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Three components to any model:
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Decision variables
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Objective function
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This is what we can change to affect the system, that is,
the variables we can decide upon
E.g, cost to be minimized, quality measure to be
maximized
Constraints
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July 11, 2017
Which values the decision variables can be set to
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
15
Decision “Variables”
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Three basic types of solutions:
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A sequence: a permutation of the jobs
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A schedule: allocation of the jobs in a more
complicated setting of the environment
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A scheduling policy: determines the next job
given the current state of the system
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
16
Model Characteristics
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Multiple factors:
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Number of machine and resources,
configuration and layout,
level of automation, etc.
Our terminology:
Resource = machine (m)
Entity requiring the resource = job (n)
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
17
Notation
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Static data:
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Processing time (pij)
Release date (rj)
Due date (dj)
Weight (wj)
Dynamic data:
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Completion time (Cij)
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
18
Machine Configuration
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Standard machine configurations:
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Single machine models
Parallel machine models
Flow shop models
Job shop models
Real world always more complicated.
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
19
Constraints
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Precedence constraints
Routing constraints
Material-handling constraints
Storage/waiting constraints
Machine eligibility
Tooling/resource constraints
Personnel scheduling constraints
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
20
Other Characteristics
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Sequence dependent setup
Preemptions
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preemptive resume
preemptive repeat
Make-to-stock versus make-to-order
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
21
Objectives and
Performance Measures
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Throughput (TP) and makespan (Cmax)
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Due date related objectives
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Work-in-process (WIP), lead time
(response time), finished inventory
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Others
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
22
Throughput and Makespan
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Throughput
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Defined by bottleneck machines
Makespan
Cmax  max C1 , C2 ,..., Cn 
Ci  max Ci1 , Ci 2 ,..., Cim , i  1,..., n
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Minimizing makespan tends to maximize
throughput and balance load
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
23
Due Date Related Objectives
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Lateness L j  C j  d j
Minimize maximum lateness (Lmax)
Tardiness T j  max C j  d j ,0
Minimize the weighted tardiness
n
w T
j 1
July 11, 2017
j
j
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
24
Due Date Penalties
Tardiness
Lateness
Lj
Tj
Cj
Cj
dj
dj
Late or Not
In practice
Uj
1
Cj
dj
July 11, 2017
Cj
dj
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
25
WIP and Lead Time
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Work-in-Process (WIP) inventory cost
Minimizing WIP also minimizes average lead
time (throughput time)
Minimizing lead time tends to minimize the
average number of jobs in system
Equivalently, we can minimize sum of the
completion times:
n
C
i 1
July 11, 2017
n
j
w C
j 1
j
j
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
26
Other Costs
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Setup cost
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Personnel cost
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Robustness
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Finished goods inventory cost
July 11, 2017
Lecture Notes for Planning and Scheduling
Prepared by Siggi Olafsson
27