Multiobjective scheduling by genetic algorithms:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston [u.a.]
Kluwer Acad. Publ.
1999
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 358 S. graph. Darst. |
| ISBN: | 0792385616 |
Internformat
MARC
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| 100 | 1 | |a Bagchi, Tapan P. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Multiobjective scheduling by genetic algorithms |c by Tapan P. Bagchi |
| 264 | 1 | |a Boston [u.a.] |b Kluwer Acad. Publ. |c 1999 | |
| 300 | |a XIII, 358 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Algorithmes génétiques | |
| 650 | 7 | |a Algoritmen |2 gtt | |
| 650 | 4 | |a Décision multicritère | |
| 650 | 4 | |a Ordonnancement (Gestion) - Simulation par ordinateur | |
| 650 | 7 | |a Productieplanning |2 gtt | |
| 650 | 7 | |a Wachttijdproblemen |2 gtt | |
| 650 | 7 | |a Wiskundige modellen |2 gtt | |
| 650 | 4 | |a Genetic algorithms | |
| 650 | 4 | |a Multiple criteria decision making | |
| 650 | 4 | |a Production scheduling |x Computer simulation | |
| 650 | 0 | 7 | |a Reihenfolgeproblem |0 (DE-588)4242167-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Genetischer Algorithmus |0 (DE-588)4265092-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Mehrkriterielle Optimierung |0 (DE-588)4610682-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Reihenfolgeproblem |0 (DE-588)4242167-6 |D s |
| 689 | 0 | 1 | |a Mehrkriterielle Optimierung |0 (DE-588)4610682-0 |D s |
| 689 | 0 | 2 | |a Genetischer Algorithmus |0 (DE-588)4265092-6 |D s |
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Datensatz im Suchindex
| _version_ | 1804127654844039168 |
|---|---|
| adam_text | Table of Contents
Page
Preface xi
Chapter 1 Shop Scheduling: An Overview 1
1.1 What Is Scheduling? 1
1.2 Machine Scheduling Preliminaries 5
1.3 Intelligent Solutions to Complex Problems 7
1.4 Scheduling Techniques: Analytical,
Heuristic and Metaheuristic 11
1.5 Outline of this Text 15
Chapter 2 What are Genetic Algorithms? 19
2.1 Evolutionary Computation and Biology 19
2.2 Working Principles 29
2.3 The Genetic Search Process 31
2.4 The Simple Genetic Algorithm (SGA) 35
2.5 An Application of GA in Numerical Optimization 35
2.6 Genetic Algorithms vs. Traditional Optimization 43
2.7 Theoretical Foundation of GAs 46
2.8 Schema Processing: An Illustration 51
2.9 Advanced Models of Genetic Algorithms 51
Chapter 3 Calibration of GA Parameters 55
3.1 GA Parameters and the Control of Search 55
3.2 The Role of the Elite who Parent the
Next Generation 60
3.3 The Factorial Parametric Study 65
3.4 Experimental Results and Their Interpretation 71
3.5 Chapter Summary 75
Chapter 4 Flowshop Scheduling 77
4.1 The Flowshop 77
4.2 Flowshop Model Formulation 80
4.3 The Two Machine Flowshop 81
4.4 Sequencing the General m Machine Flowshop 83
4.5 Heuristic Methods for Flowshop Scheduling 85
4.6 Darwinian and Lamarckian Genetic Algorithms 92
4.7 Flowshop Sequencing by GA: An Illustration 97
4.8 Darwinian and Lamarckian Theories of
Natural Evolution 99
4.9 Some Inspiring Results of using Lamarckism 102
4.10 A Multiobjective GA for Flowshop Scheduling 106
4.11 Chapter Summary 107
Chapter 5 Job Shop Scheduling 109
5.1 The Classical Job Shop Problem (JSP) 109
5.2 Heuristic Methods for Scheduling the Job Shop 117
5.3 Genetic Algorithms for Job Shop Scheduling 122
5.4 Chapter Summary 135
Chapter 6 Multiobjective Optimization 136
6.1 Multiple Criteria Decision Making 136
6.2 A Sufficient Condition: Conflicting Criteria 138
6.3 Classification of Multiobjective Problems 138
6.4 Solution Methods 139
6.5 Multiple Criteria Optimization Redefined 142
6.6 The Concept of Pareto Optimality and
Efficient Solutions 143
Chapter 7 Niche Formation and Speciation:
Foundations of Multiobjective GAs 147
7.1 Biological Moorings of Natural Evolution 148
7.2 Evolution is also Cultural 154
7.3 The Natural World of a Thousand Species 158
7.4 Key Factors Affecting the Formation of Species 160
7.5 What is a Niche? 161
7.6 Population Diversification through
Niche Compacting 163
7.7 Speciation: The Formation of New Species 166
Chapter 8 The Nondominated Sorting Genetic Algorithm:
NSGA 171
8.1 Genetic Drift: A Characteristic Feature of SGA 171
8.2 The Vector Evaluated Generic Algorithm (VEGA) 173
8.3 Niche, Species, Sharing and Function Optimization 174
8.4 Multiobjective Optimization Genetic Algorithm
(MOGA) 179
viii
8.5 Pareto Domination Tournaments 179
8.6 A Multiobjective GA Based on the Weighted Sum 180
8.7 The Nondominated Sorting Genetic Algorithm
(NSGA) 181
8.8 Applying NSGA: A Numerical Example 187
8.9 Chapter Summary 202
Chapter 9 Multiobjective Flowshop Scheduling 203
9.1 Traditional Methods to Sequence Jobs in
the Multiobjective Flowshop 203
9.2 Disadvantages of Classical Methods 206
9.3 Adaptive Random Search Optimization 207
9.4 Recollection of the Concept of Pareto Optimality 207
9.5 NSGA Solutions to the Multiobjective Flowshop 209
9.6 How NSGA Produced Pareto Optimal Sequences 211
9.7 The Quality of the Final Solutions 214
9.8 Chapter Summary 214
Chapter 10 A New Genetic Algorithm for Sequencing the
Multiobjective Flowshop 216
10.1 The Elitist Nondominated
Sorting Genetic Algorithm (ENGA) 217
10.2 Initialization of ENGA (Box 1) 219
10.3 Performance Evaluation 220
10.4 Genetic Processing Operators 224
10.5 The Additional Nondominated Sorting
and Ranking (Box 8) 230
10.6 Stopping Condition and Output Module 232
10.7 Parameterization of ENGA by Design of
Experiments 232
10.8 Application of ENGA to the 49 Job
15 Machine Flowshop 237
10.9 Chapter Summary 237
Chapter 11 A Comparison of Multiobjective Flowshop
Sequencing by NSGA and ENGA 245
11.1 NSGA vs. ENGA: Computational Experience 245
11.2 Statistical Evaluation of GA Results 247
11.3 Chapter Summary 249
ix
Chapter 12 Multiobjective Job Shop Scheduling 256
12.1 Multiobjective JSP Implementation 256
12.2 NSGA vs. ENGA: Computational Experience 261
12.3 Chapter Summary 261
Chapter 13 Multiobjective Open Shop Scheduling 267
13.1 An Overview of the Open Shop 267
13.2 Multiobjective GA Implementation 268
13.3 NSGA vs. ENGA: Some Computational Results 270
Chapter 14 Epilog and Directions for Further Work 277
• Exact solutions 278
• Solving the General Job Shop 278
• Seeking Pareto Optimality 279
• Optimization of GA Parameters 279
• ENGA vs. Other Multiobjective Solution Methods 281
• Conflicting and Synergistic Optimization Objectives 283
• Darwinian and Lamarckian GAs:
The High Value of Hybridizing 288
• Concluding Remarks 289
References 293
Appendix C++ Codes for a Hybridized GA to Sequence the
Single Objective Flowshop 307
Glossary 341
Index 351
x
|
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| id | DE-604.BV012961115 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:36:47Z |
| institution | BVB |
| isbn | 0792385616 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008827552 |
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| physical | XIII, 358 S. graph. Darst. |
| publishDate | 1999 |
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| publisher | Kluwer Acad. Publ. |
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| spelling | Bagchi, Tapan P. Verfasser aut Multiobjective scheduling by genetic algorithms by Tapan P. Bagchi Boston [u.a.] Kluwer Acad. Publ. 1999 XIII, 358 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Algorithmes génétiques Algoritmen gtt Décision multicritère Ordonnancement (Gestion) - Simulation par ordinateur Productieplanning gtt Wachttijdproblemen gtt Wiskundige modellen gtt Genetic algorithms Multiple criteria decision making Production scheduling Computer simulation Reihenfolgeproblem (DE-588)4242167-6 gnd rswk-swf Genetischer Algorithmus (DE-588)4265092-6 gnd rswk-swf Mehrkriterielle Optimierung (DE-588)4610682-0 gnd rswk-swf Reihenfolgeproblem (DE-588)4242167-6 s Mehrkriterielle Optimierung (DE-588)4610682-0 s Genetischer Algorithmus (DE-588)4265092-6 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008827552&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bagchi, Tapan P. Multiobjective scheduling by genetic algorithms Algorithmes génétiques Algoritmen gtt Décision multicritère Ordonnancement (Gestion) - Simulation par ordinateur Productieplanning gtt Wachttijdproblemen gtt Wiskundige modellen gtt Genetic algorithms Multiple criteria decision making Production scheduling Computer simulation Reihenfolgeproblem (DE-588)4242167-6 gnd Genetischer Algorithmus (DE-588)4265092-6 gnd Mehrkriterielle Optimierung (DE-588)4610682-0 gnd |
| subject_GND | (DE-588)4242167-6 (DE-588)4265092-6 (DE-588)4610682-0 |
| title | Multiobjective scheduling by genetic algorithms |
| title_auth | Multiobjective scheduling by genetic algorithms |
| title_exact_search | Multiobjective scheduling by genetic algorithms |
| title_full | Multiobjective scheduling by genetic algorithms by Tapan P. Bagchi |
| title_fullStr | Multiobjective scheduling by genetic algorithms by Tapan P. Bagchi |
| title_full_unstemmed | Multiobjective scheduling by genetic algorithms by Tapan P. Bagchi |
| title_short | Multiobjective scheduling by genetic algorithms |
| title_sort | multiobjective scheduling by genetic algorithms |
| topic | Algorithmes génétiques Algoritmen gtt Décision multicritère Ordonnancement (Gestion) - Simulation par ordinateur Productieplanning gtt Wachttijdproblemen gtt Wiskundige modellen gtt Genetic algorithms Multiple criteria decision making Production scheduling Computer simulation Reihenfolgeproblem (DE-588)4242167-6 gnd Genetischer Algorithmus (DE-588)4265092-6 gnd Mehrkriterielle Optimierung (DE-588)4610682-0 gnd |
| topic_facet | Algorithmes génétiques Algoritmen Décision multicritère Ordonnancement (Gestion) - Simulation par ordinateur Productieplanning Wachttijdproblemen Wiskundige modellen Genetic algorithms Multiple criteria decision making Production scheduling Computer simulation Reihenfolgeproblem Genetischer Algorithmus Mehrkriterielle Optimierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008827552&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bagchitapanp multiobjectiveschedulingbygeneticalgorithms |