Journal of Chuxiong Normal University ›› 2023, Vol. 38 ›› Issue (3): 76-82.

• Mathematics • Previous Articles     Next Articles

Multi-objective Genetic Algorithm to Solve Fuzzy Flexible Job Shop Scheduling Problem

ZHUANG Xiaoye1, LI Ke2   

  1. 1. Department of Information Engineering, Weifang Engineering Vocational College, Qingzhou, Shandong Province 262500;
    2. Department of Operational Support, Rocket Sergeant Academy, Qingzhou, Shandong Province 262500
  • Received:2022-12-12 Online:2023-05-20 Published:2023-08-07

Abstract: A multi-objective fuzzy flexible job shop scheduling problem (MOFFJSP) is established that uses fuzzy numbers to represent workpiece processing time and delivery date, and the optimization goal is to maximize average customer satisfaction and minimize fuzzy completion time. Process-based encoding is used to represent scheduling solutions, and active decoding methods to improve the quality of the solutions. The algorithm uses crossover and mutation operators designed based on the principles of immunity and entropy to construct new solutions, and uses non-dominated sorting and improved elite retention Strategies to improve the diversity of Pareto solution sets. Simulation experiments prove that the proposed MOGA can effectively solve the problem model.

Key words: fuzzy flexible job shop, multi-objective genetic algorithm, Pareto optimal, immune and entropy principle

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