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

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Abstract:

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.

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Periodical:

Applied Mechanics and Materials (Volumes 66-68)

Pages:

870-875

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.66-68.870

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Online since:

July 2011

Authors:

Jian Jun Yang, Lu Yan Ju, Bao Ye Liu

Keywords:

Flexible Job-Shop Scheduling, Genetic Algorithm (GA), Multi-Objective Optimization (MOP), Non-Dominated Sorting, Pareto Optimum

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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