The Improved Genetic Algorithm for Multi-Objective Flexible Job Shop Scheduling Problem

Abstract:

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To solve the multi-objective flexible job shop scheduling problem, an improved non-dominated sorting genetic algorithm is proposed. Multi-objective mathematical model is established, four objectives, makespan, maximal workload, total workload and total tardiness are considered together. In this paper a dual coding method is employed, and infeasible solutions were avoided by new crossover and mutation methods. Pareto optimal set was taken to deal with multi-objective optimization problem, in order to reduce computational complexity, the non-dominated sorting method was improved. The niche technology is adopted to increase the diversity of solutions, and a new self adaptive mutation rate computing method is designed. The proposed algorithm is tested on some instances, and the computation results demonstrate the superiority of the algorithm.

Info:

Periodical:

Edited by:

Honghua Tan

Pages:

870-875

DOI:

10.4028/www.scientific.net/AMM.66-68.870

Citation:

J. J. Yang et al., "The Improved Genetic Algorithm for Multi-Objective Flexible Job Shop Scheduling Problem", Applied Mechanics and Materials, Vols. 66-68, pp. 870-875, 2011

Online since:

July 2011

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Price:

$35.00

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