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 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 Elitist Nondominated 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.
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SHOP SCHEDULING AN OVERVIEW
WHAT ARE GENETIC ALGORITHMS?
CALIBRATION OF GA PARAMETERS
JOB SHOP SCHEDULING
NICHE FORMATION AND SPECIATION FOUNDATIONS OF MULTIOBJECTIVE GAs
THE NONDOMINATED SORTING GENETIC ALGORITHM NSGA
A COMPARISON OF MULTIOBJECTIVE FLOWSHOP SEQUENCING BY NSGA AND ENGA
MULTIOBJECTIVE JOB SHOP SCHEDULING
MULTIOBJECTIVE OPEN SHOP SCHEDULING
EPILOG AND DIRECTIONS FOR FURTHER WORK
C++ Codes for a Hybridized GA to Sequence the SingleObjective Flowshop
MULTIOBJECTIVE FLOWSHOP SCHEDULING
A NEW GENETIC ALGORITHM FOR SEQUENCING THE MULTIOBJECTIVE FLOWSHOP
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adaptation allele applied average fitness cell Chapter chrom chromosome computational constraints convergence cprintf cputs cputsl crossover and mutation Darwin Darwinian delete due dates dummy fitness efficient elite evaluate factors Figure FINDING PARETO OPTIMAL fitness landscape float flowshop problem flowshop scheduling gametes genes Genetic Algorithm global Goldberg gotoxy heuristic heuristic methods hybrid improve initial population job sequence job shop scheduling Lamarckian machine makespan mating pool mean flow mean tardiness meiosis meta-heuristic minimization multiobjective flowshop multiobjective optimization multiobjective problem natural selection niche formation NJOBS NP-hard NSGA and ENGA objective function offspring optimization problems optimum organisms parameterization parameters parent Pareto optimal solutions partial schedule performance phenotype procedure produce progeny protein random random seeds randomly recombination representation reproduction Robust Design scheduling problem scheme Seed simulated annealing single objective solve speciation species Step string Table tabu search void
Multi-Objective Optimization Using Evolutionary Algorithms
Limited preview - 2001
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Limited preview - 2004