An improved multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem Online publication date: Wed, 05-Jun-2013
by Xiaojuan Wang; Wenfeng Li; Ying Zhang
International Journal of Computer Applications in Technology (IJCAT), Vol. 47, No. 2/3, 2013
Abstract: In many real-world applications, processing time may vary dynamically due to human factors or operating faults and there are some other uncertain factors in the scheduling problems. In this paper, fuzzy sets are used to model uncertain processing time and due date. In addition, an improved multi-objective genetic algorithm is presented to solve the multi-objective fuzzy Flexible Job-shop Scheduling Problem (FJSP). About this improved multi-objective genetic algorithm, Pareto-optimality is applied, including the non-dominated sorting scheme and an improved elite reservation strategy based on NSGA-II. Meanwhile, the immune and entropy principle is used to preserve the diversity of individuals. Moreover, the advanced crossover and the mutation operators are used to adapt to the special chromosome structure. The computational results demonstrate the effectiveness of the proposed algorithm.
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