Table Of ContentShiroma
Efficient Production Planning and Scheduling
GABLER EDITION WISSENSCHAFT
Information Engineering und
IV -Control Ii n g
Herausgegeben von Professor Dr. Franz Lehner
Die Schriftenreihe prasentiert aktuelle Forschungsergebnisse der
Wirtschaftsinformatik sowie interdisziplinare Ansatze aus Informatik
und Betriebswirtschaftslehre. Ein zentrales Anliegen ist dabei die
Pfiege der Verbindung zwischen Theorie und Praxis durch eine an
wendungsorientierte Darstellung sowie durch die Aktualitat der Bei
trage. Mit der inhaltlichen Orientierung an Fragen des Information
Engineerings und des IV-Controllings 5011 insbesondere ein Beitrag
zur theoretischen Fundierung und Weiterentwicklung eines wichtigen
Teilbereichs der Wirtsc~aftsinformatik geleistet werden.
Patricia Jay Shiroma
Efficient
Production Planning
and Scheduling
An Integrated Approach with
Genetic Algorithms and Simulation
With a Foreword
by Prof. Dr. Gerhard Niemeyer
Springer Fachmedien Wiesbaden GmbH
DieDeutsche Bibliothek -C1P-Einheitsaufnahme
Shiroma, PatriciaJay:
Efficient production planningandscheduling :on integroted
approach withgeneticalgorithmsand simulation
/ PatriciaJay Shiroma.Witha foreword byGerhard Niemeyer.
-Wiesbaden :Dt.Univ.-Verl.;Wiesbaden :Gabler, 1996
(Gabler EditionWissenschaft:Information Engineering
und IV-Controlling)
Zugl.:Regensburg, Univ.,Diss., 1996
GablerVerlag,Deutscher Universitäts-Verlag, Wiesbaden
©SpringerFachmedienWiesbaden1996
UrsprünglicherschienenbeiBetriebswirtschaftlicherVerlagDr.Th.GablerGmbH,Wiesbaden 1996.
Lektorat:Cloudia Splittgerber
DosWerk einschließlich oller seiner Teileisturheberrechtlich geschützt.
Jede Verwertung außerhalb der engen Grenzen des Urheberrechtsge
setzes istohne Zustimmung des Verlages u.(lzulässig und strafbar. Dos
gilt insbesondere für Vervielfältigungen, Ubersetzungen, Mikroverfil
mungen und die Einspeicherung uno Verarbeitung in elektronischen
Systemen.
Höchsteinhaltlicheund technischeQualitätunsererProdukteistunserZiel.BeiderProduktionund
Auslieferung unsererBücherwollenwirdie Umweltschonen:DiesesBuchistauf säurefreiemund
chlorfrei gebleichtem Papiergedruckt.
DieWiedergabe von Gebrauchsnomen, Handelsnomen, Warenbezeichnungen usw. in diesem
Werk berechtigtauchohnebesondereKennzeichnungnichtzuderAnnahme,daßsolche Namen
imSinne der Warenzeichen- und Markenschutz-Gesetzgebung als freizu betrachten wären
und dahervon jedermann benutztwerden dürften.
ISBN978-3-8244-6426-5 ISBN978-3-663-08438-9(eBook)
DOI10.1007/978-3-663-08438-9
Foreword
Production scheduling with resource allocation is a highly complex planning process.
In addition to evaluating the large number of possible job sequences, a number of other
variables must also be considered. Machine capacities, availability of personnel and
material, delivery deadlines as well as penalty costs for missed deadlines must all be
taken into account.
Most of the competing theoretical methods used today make a number of unrealistic
assumptions in order to simplify the problem. Although the resulting models can be
solved mathematically, they are often so constrained that they no longer reflect reality
and are therefore unusable in practice.
This work presents an original solution to the problem of production scheduling with
simultaneous resource allocation. The interactive integration of genetic algorithms
with system simulation makes it possible to handle the complexity inherent to this
planning problem without losing information. Furthermore, the combination of a
genetic algorithm with tabu search represents a further innovation which improves
efficiency of the search process. Finally, the introduction of an adaptive mutation
controller prevents the algorithm from stagnating at local optima. The effectiveness of
these methods is tested on an application to generate production schedules for an actual
manufacturing firm with large numbers of job orders.
Gerhard Niemeyer
Regensburg, Germany
v
Preface
This dissertation investigates the possibility of combining genetic algorithms and
simulation studies to generate improved production plans for job-based manufacturing
processes. A genetic algorithm is a type of search method based on principles of natu
ral selection and evolution. Simulation has proven highly effective in modeling the
complexity inherent to dynamic, non-linear systems. The integration of genetic algo
rithms with simulation studies takes advantage of the synergistic effects between the
two methods and results in a flexible, highly effective production scheduling system.
First, the problem of job-oriented production planning and scheduling is formally de
fined. Next, the advantages and disadvantages of existing methods to solve this prob
lem are evaluated. The combination of genetic algorithms with simulation is proposed
as a new solution. The nature and theoretical foundations of genetic algorithms are
discussed in detail. New methods for the hybridization of genetic algorithms with
simulation studies are introduced. The feasibility of embedding genetic algorithms
within the AMTOS simulation system is investigated. The results of an actual case
study applying a hybrid system of genetic algorithms with simulation in order to im
prove production planning for a large pharmaceutical firm are presented. Finally,
plans for future research are discussed.
Acknowledgements
I would like to thank my dissertation advisors at the University of Regensburg, Prof.
Dr. Gerhard Niemeyer and Prof. Dr. Dieter Bartmann, for their valuable guidance and
helpful suggestions during the both development phase and through the review
process. I also thank the editor of this series, Prof. Dr. Franz Lehner of the University
of Regensburg, for his assistance in the publication of this work.
I would like to express my gratitude to Prof. Fred Glover, Prof. James Kelly and Prof.
Manuel Laguna, of the University of Colorado at Boulder, for their expert advice and
assistance in the area of tabu search.
VII
I am grateful for the editorial assistance provided by Ms. Claudia Splitgerber at
Gabler-Verlag in the completion of this book.
I thank Brett Tanaka, of the Hawaii Pacific University, for his help in reviewing and
proofreading the manuscript.
I thank my colleagues at the Institute for Business Informatics, Michael Bosch, Chris
tine Handl, Werner Hopf, Claus Lindenau, Norbert Meck! and Veronika Wolf, for their
continued encouragement and thought-provoking discussions.
Special thanks go to my partner and best friend, Matthias Brockmann, for his under
standing and tolerance on the long and often difficult road to completing this disserta
tion.
Finally, I would like to thank my entire family for their inexhaustible encouragement
and moral support; This book is dedicated to my parents.
Patricia Jay Shiroma
Regensburg, Germany
VIII
Table of Contents
Introduction ................................................................................................................. 1
1.1 Motivation .......................................................................................................... 1
1.2 Problem Definition and Extensions ................................................................... 5
1.2.1 Definition of the Standard Job-Based Production Planning Problem ..... 5
1.2.2 Deficiencies in the Standard Problem Definition .................................... 6
1.2.2.1 Time-Cost Tradeoffs .................................................................. 6
1.2.2.2 Time Aspect of Dynamic Resource Allocation .......................... 7
1.2.3 Expanded Problem Definition ................................................................. 8
1.3 Failure of Traditional Operations Research Methods to Solve Problems
with Nonlinear Dependencies 9
1.3.1 Linear Programming 9
1.3.2 Hillclimbing Methods 9
1.4 Simulation ........................................................................................................ 11
1.5 Cybernetic System Theory ............................................................................... 12
1.5.1 Automata Theory ................................................................................... 13
1.5.2 Cybernetics ............................................................................................ 15
1.6 Generation of Input Parameters for Simulation Studies .................................. 16
1.6.1 Simple Heuristics .................................................................................. 16
1.6.2 Nearest Neighbor. .................................................................................. 16
1.6.3 Balancing Machine Load ...................................................................... 18
1.6.4 Expert Systems ...................................................................................... 19
1.6.5 Enumeration 21
1.6.6 Dynamic Programming ......................................................................... 22
1.6.7 Branch and Bound ................................................................................. 24
1.6.8 Simulated Annealing ............................................................................. 29
2 The Nature of Evolutionary Algorithms ................................................................... 33
2.1 Evolutionary Programming .............................................................................. 35
2.2 Evolutionary Strategies .................................................................................... 36
2.3 Genetic Algorithms .......................................................................................... 37
2.3.1 Selection ................................................................................................ 38
2.3.2 Crossover ............................................................................................... 39
2.3.3 Mutation ................................................................................................ 40
2.3.4 Convergence .......................................................................................... 41
2.3.5 Example of a Genetic Algorithm in Pseudo-Pascal .............................. 42
IX
3 Theoretical Foundations of Genetic Algorithms ....................................................... 43
3.1 Schema Theorem .............................................................................................. 43
3.1.1 Schemata 44
3.1.2 Upper and Lower Bounds on the Number of Schemata Evaluated ....... 45
3.1.3 Hyperplane Sampling ............................................................................ 45
3.1.4 Effects of Fitness Proportional Reproduction ....................................... 47
3.1.5 Effects of Crossover .............................................................................. 49
3.1.6 Effects of Mutation ............................................................................... 50
3.1.7 Schema Theorem Summarized .............................................................. 51
3.2 The Building Block Hypothesis ....................................................................... 52
3.3 Interacting Roles of Crossover and Mutation .................................................. 54
3.4 Self-Organizing Systems and Artificial Life .................................................... 55
3.4.1 Artificial Life ........................................................................................ 55
3.4.2 The Game of Life .................................................................................. 56
3.4.3 Manipulation of DNA to Solve Combinatorial Problems ..................... 57
3.4.4 Generation of Computer Programs with Natural Selection .................. 59
3.4.5 Sirriulation of a Market Economy with Autonomous Agents ............... 61
4 Methodology ............................................................................................................. 63
4.1 Classic vs. Hybrid Genetic Algorithms ............................................................ 65
4.2 Time Constraints when Combining Genetic Algorithms with Simulation ...... 66
4.2.1 Minimization of the Number of Simulation Runs ................................. 67
4.2.1.1 Delta Evaluation ....................................................................... 68
4.2.1.2 Messy Genetic Algorithms ....................................................... 68
4.2.1.3 Chromosome Representation ................................................... 69
4.2.1.3.1 Binary vs. Real Coding Schemes .............................. 69
4.2.1.3.2 Hierarchical, Dynamic Data Structure ...................... 71
4.2.1.4 Modification of Genetic Operators .......................................... 73
4.2.1.4.1 Selection .................................................................... 73
4.2.1.4.1.1 Generational vs.
Steady State Algorithms ........................ 74
4.2.1.4.1.2 Fitness Proportional vs.
Rank-Based Selection 75
4.2.1.4.2 Modifications to the Crossover Operator ................. 77
4.2.1.4.2.1 Multi-Point Crossover ........................... 77
4.2.1.4.2.2 Uniform Crossover. ............................... 78
4.2.1.4.2.3 Intelligent Crossover ............................. 79
4.2.1.4.2.4 Order Crossover 80
x
