Multiobjective Scheduling by Genetic Algorithms
Multiobjective Scheduling by Genetic Algorithms describes methods for developing multiobjective solutions to common production scheduling equations modeling in the literature as flowshops, job shops and open shops. The methodology is metaheuristic, one in
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MUL TIOBJECTIVE SCHEDULING BY GENETIC ALGORITHMS by
Tapan P. Bagchi Indian Institute of Technology Kanpur
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Springer Science+Business Media, LLC
Library of Congress Cataloging-in-Publication Data Bagchi, Tapan P. Multiobjective scheduling by genetic algorithms / by Tapan P. Bagchi. p. cm. Includes bibliographical references and index. ISBN 978-1-4613-7387-2 ISBN 978-1-4615-5237-6 (eBook) DOI 10.1007/978-1-4615-5237-6 1. Production scheduling --Computer simulation. 2. Genetic algorithms. 3. Multiple criteria decis ion making. 1. Title. TSI57.5.B33 1999 658.5--dc21 99-37211 CIP Copyright © 1999 by Springer Science+Business Media New York Origina11y published by Kluwer Academic Publishers in 1999 Softcover reprint of the hardcover 1st edition 1999 AII rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, mechanical, photo-copying, recording, or otherwise, without the prior written permission of the publisher, Springer Science+ Business Media, LLC. Printed on acid-free paper.
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Table of Contents
Preface Chapter 1
1.1 1.2 1.3 1.4 1.5 Chapter 2
2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 Chapter 3
3.1 3.2 3.3 3.4 3.5 Chapter 4
4.1 4.2 4.3 4.4 4.5
Shop Scheduling: An Overview
What Is Scheduling? Machine Scheduling Preliminaries Intelligent Solutions to Complex Problems Scheduling Techniques: Analytical, Heuristic and Metaheuristic Outline of this Text
Page xi 1
1 5 7 11
15
What are Genetic Algorithms?
19
Evolutionary Computation and Biology Working Principles The Genetic Search Process The Simple Genetic Algorithm (SGA) An Application of GA in Numerical Optimization Genetic Algorithms vs. Traditional Optimization Theoretical Foundation of GAs Schema Processing: An Illustration Advanced Models of Genetic Algorithms
19 29 31' 35 35 43 46 51 51
Calibration of GA Parameters
55 55
GA Parameters and the Control of Search The Role of the "Elite" who Parent the Next Generation The Factorial Parametric Study Experimental Results and Their Interpretation Chapter Summary Flowshop Scheduling
The Flowshop Flowshop Model Formulation The Two-Machine Flowshop Sequencing the General m-Machine Flowshop Heuristic Methods for Flowshop Scheduling
60 65 71
75 77
77 80 81 83 85
4.6 4.7 4.8 4.9 4.10 4.11 Chapter 5
5.1 5.2 5.3 5.4 Chapter 6
6.1 6.2 6.3 6.4 6.5 6.6 Chapter 7
7.1 7.2 7.3 7.4 7.5 7.6 7.7
Darwinian and Lamarckian Genetic Algorithms Flowshop Sequencing by GA: An Illustration Darwinian and Lamarckian Theories of Natural Evolution Some Inspiring Results of using Lamarckism A Multiobjective GA for Flowshop Scheduling Chapter Summary
92 97 99 102 106 107
Job Shop Scheduling
109
The Classical Job Shop Problem (JSP) Heuristic Methods for Scheduling the Job Shop Genetic Algorithms for Job Shop Scheduling Chapter Summary
109 117 122 135
Multiobjective Optimization
136
Multiple Criteria Decision Making A Sufficient Condition: Conflicting Criteria Classification of Multiobjec
