Flexible Job Shop Scheduling Multi-Objective Optimization Based on Improved Strength Pareto Evolutionary Algorithm

Abstract:

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Scheduling for the flexible job shop is very important in fields of production management. To solve the multi–objective optimization in flexible job shop scheduling problem (FJSP), the FJSP multi-objective optimization model is constructed. The cost, quality and time are taken as the optimization objectives. An improved strength Pareto evolutionary algorithm (SPEA2+) is put forward to optimize the multi-objective optimization model parallelly. The algorithm uses a new model of a Multi-objective genetic algorithm that includes more effective crossover and could obtain diverse solutions in the objective and variable spaces to archive the Pareto optimal sets for FJSP multi-objective optimization. Then an approach based on fuzzy set theory was developed to extract one of the Pareto-optimal solutions as the best compromise one. The optimization results were compared with those obtained by NSGA-II and POS. At last, an instance of flexible job shop scheduling problem in automotive industry is given to illustrate that the proposed method can solve the multi-objective FJSP effectively.

Info:

Periodical:

Edited by:

Wenya Tian and Linli Xu

Pages:

546-551

DOI:

10.4028/www.scientific.net/AMR.186.546

Citation:

W. Wei et al., "Flexible Job Shop Scheduling Multi-Objective Optimization Based on Improved Strength Pareto Evolutionary Algorithm", Advanced Materials Research, Vol. 186, pp. 546-551, 2011

Online since:

January 2011

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Price:

$35.00

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