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Multiobjective Scheduling by Genetic Algorithms

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

31.08.1999

Abbildungen

XIII, 358 p.

Verlag

Springer Us

Seitenzahl

358

Maße (L/B/H)

24,1/16/2,5 cm

Gewicht

729 g

Auflage

1999

Sprache

Englisch

ISBN

978-0-7923-8561-5

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

31.08.1999

Abbildungen

XIII, 358 p.

Verlag

Springer Us

Seitenzahl

358

Maße (L/B/H)

24,1/16/2,5 cm

Gewicht

729 g

Auflage

1999

Sprache

Englisch

ISBN

978-0-7923-8561-5

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Multiobjective Scheduling by Genetic Algorithms
  • Produktbild: Multiobjective Scheduling by Genetic Algorithms
  • 1 Shop Scheduling: An Overview.- 1.1 What Is Scheduling?.- 1.2 Machine Scheduling Preliminaries.- 1.3 Intelligent Solutions to Complex Problems.- 1.4 Scheduling Techniques: Analytical, Heuristic and Metaheuristic.- 1.5 Outline of this Text.- 2 What are Genetic Algorithms?.- 2.1 Evolutionary Computation and Biology.- 2.2 Working Principles.- 2.3 The Genetic Search Process.- 2.4 The Simple Genetic Algorithm (SGA).- 2.5 An Application of GA in Numerical Optimization.- 2.6 Genetic Algorithms vs. Traditional ptimization.- 2.7 Theoretical Foundation of GAs.- 2.8 Schema Processing: An Illustration.- 2.9 Advanced Models of Genetic Algorithms.- 3 Calibration of GA Parameters.- 3.1 GA Parameters and the Control of Search.- 3.2 The Role of the “Elite” who Parent the Next Generation.- 3.3 The Factorial Parametric Study.- 3.4 Experimental Results and Their Interpretation.- 3.5 Chapter Summary.- 4 Flowshop Scheduling.- 4.1 The Flowshop.- 4.2 Flowshop Model Formulation.- 4.3 The Two-Machine Flowshop.- 4.4 Sequencing the General m-Machine Flowshop.- 4.5 Heuristic Methods for Flowshop Scheduling.- 4.6 Darwinian and Lamarckian Genetic Algorithms.- 4.7 Flowshop Sequencing by GA: An Illustration.- 4.8 Darwinian and Lamarckian Theories of Natural Evolution.- 4.9 Some Inspiring Results of using Lamarckism.- 4.10 A Multiobjective GA for Flowshop Scheduling.- 4.11 Chapter Summary.- 5 Job Shop Scheduling.- 5.1 The Classical Job Shop Problem (JSP).- 5.2 Heuristic Methods for Scheduling the Job Shop.- 5.3 Genetic Algorithms for Job Shop Scheduling.- 5.4 Chapter Summary.- 6 Multiobjective Optimization.- 6.1 Multiple Criteria Decision Making.- 6.2 A Sufficient Condition: Conflicting Criteria.- 6.3 Classification of Multiobjective Problems.- 6.4 Solution Methods.- 6.5 Multiple CriteriaOptimization Redefined.- 6.6 The Concept of Pareto Optimality and “Efficient” Solutions.- 7 Niche Formation and Speciation: Foundations of Multiobjective GAs.- 7.1 Biological Moorings of Natural Evolution.- 7.2 Evolution is also Cultural.- 7.3 The Natural World of a Thousand Species.- 7.4 Key Factors Affecting the Formation of Species.- 7.5 What is a Niche?.- 7.6 Population Diversification through Niche Compacting.- 7.7 Speciation: The Formation of New Species.- 8 The Nondominated Sorting Genetic Algorithm: NSGA.- 8.1 Genetic Drift: A Characteristic Feature of SGA.- 8.2 The Vector Evaluated Genetic Algorithm (VEGA).- 8.3 Niche, Species, Sharing and Function Optimization.- 8.4 Multiobjective Optimization Genetic Algorithm (MOGA).- 8.5 Pareto Domination Tournaments.- 8.6 A Multiobjective GA Based on the Weighted Sum.- 8.7 The Nondominated Sorting Genetic Algorithm (NSGA).- 8.8 Applying NSGA: A Numerical Example.- 8.9 Chapter Summary.- 9 Multiobjective Flowshop Scheduling.- 9.1 Traditional Methods to Sequence Jobs in the Multiobjective Flowshop.- 9.2 Disadvantages of Classical Methods.- 9.3 Adaptive Random Search Optimization.- 9.4 Recollection of the Concept of Pareto Optimality.- 9.5 NSGA Solutions to the Multiobjective Flowshop.- 9.6 How NSGA Produced Pareto Optimal Sequences.- 9.7 The Quality of the Final Solutions.- 9.8 Chapter Summary.- 10 A New Genetic Algorithm for Sequencing the Multiobjective Flowshop.- 10.1 The Elitist Nondominated Sorting Genetic Algorithm (ENGA).- 10.2 Initialization of ENGA (Box 1).- 10.3 Performance Evaluation.- 10.4 Genetic Processing Operators.- 10.5 The Additional Nondominated Sorting and Ranking (Box 8).- 10.6 Stopping Condition and Output Module.- 10.7 Parameterization of ENGA by Design of Experiments.- 10.8 Application of ENGA tothe 49-Job.- 15-Machine Flowshop.- 10.9 Chapter Summary.- 11 A Comparison of Multiobjective Flowshop Sequencing by NSGA and ENGA.- 11.1 NSGA vs. ENGA: Computational Experience.- 11.2 Statistical Evaluation of GA Results.- 11.3 Chapter Summary.- 12 Multiobjective Job Shop Scheduling.- 12.1 Multiobjective JSP Implementation.- 12.2 NSGA vs. ENGA: Computational Experience.- 12.3 Chapter Summary.- 13 Multiobjective Open Shop Scheduling.- 13.1 An Overview of the Open Shop.- 13.2 Multiobjective GA Implementation.- 13.3 NSGA vs. ENGA: Some Computational Results.- 14 Epilog and Directions for Further Work.- · Exact solutions.- · Solving the General Job Shop.- · Seeking Pareto Optimality.- · Optimization of GA Parameters.- · ENGA vs. Other Multiobjective Solution Methods.- · Conflicting and Synergistic Optimization Objectives.- · Darwinian and Lamarckian GAs: The High Value of Hybridizing.- · Concluding Remarks.- References.