Bagchi Multiobjective Scheduling by Genetic Algorithms

Erscheinungsjahr 2012
ISBN: 978-1-4615-5237-6
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 358 Seiten, eBook

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 inspired by how nature has evolved a multitude of coexisting species of living beings on earth. Multiobjective flowshops, job shops and open shops are each highly relevant models in manufacturing, classroom scheduling or automotive assembly, yet for want of sound methods they have remained almost untouched to date. This text shows how methods such as ElitistNondominated Sorting Genetic Algorithm (ENGA) can find a bevy of Pareto optimal solutions for them. Also it accents the value of hybridizing Gas with both solution-generating and solution-improvement methods. It envisions fundamental research into such methods, greatly strengthening the growing reach of metaheuristic methods. This book is therefore intended for students of industrial engineering, operations research, operations management and computer science, as well as practitioners. It may also assist in the development of efficient shop management software tools for schedulers and production planners who face multiple planning and operating objectives as a matter of course.

Zielgruppe

Research

Autoren/Hrsg.

Bagchi, Tapan P.

Inhaltsverzeichnis

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.