Multiobjective Scheduling by Genetic Algorithms

Online-Ressource (XIII, 358 p)

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1 Shop Scheduling: An Overview1.1 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 Criteria Optimization 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 to the 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.

10.1007/978-1-4615-5237-6 [DOI]

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