Research on job shop scheduling under uncertainty

Authors:
Zhenhao Xu;Xingsheng Gu;Bin Jiao;Jinwei Gu
Affiliations:
East China University of Science & Technology, Shanghai, China;East China University of Science & Technology, Shanghai, China;Shanghai Dianji University, Shanghai, China;East China University of Science & Technology, Shanghai, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

Citing 3
Cited 0

Local Search Genetic Algorithms for the Job Shop Scheduling Problem

Applied Intelligence
Job shop scheduling with setup times, deadlines and precedence constraints

Journal of Scheduling
Scheduling: Theory, Algorithms, and Systems

Scheduling: Theory, Algorithms, and Systems

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Abstract

In many real world applications, the processing time of products in Job Shop scheduling problems is not a fixed value, and may vary dynamically with the situation. In this study, the scheduling mathematical model of Job Shop problems with uncertain processing time has been established based on fuzzy programming theory. The uncertain processing time can be described by the triangular fuzzy numbers, and the Maximum Membership Functions of Mean Value method is applied to convert the fuzzy scheduling model to the general optimization model. Furthermore, a fuzzy immune scheduling algorithm combined with the feature of the Immune Algorithm is proposed, which can prevents the possibility of stagnation in the iteration process and achieves fast convergence for global optimization. The effectiveness and efficiency of the fuzzy scheduling model and the proposed algorithm are demonstrated by simulation results.