A multi-objective genetic algorithm for a fuzzy parallel blocking flow shop scheduling problem

作者：Yang, Zhi; Liu, Cungen

来源：Academic Journal of Manufacturing Engineering, 2018, 16(2): 3-11.

• Blocking flow shops
• Crossover and mutation
• Fuzzy processing time
• Fuzzy scheduling
• Local search method
• Multi objective evolutionary algorithms
• Multi-objective genetic algorithm
• Representation of a solutions

This paper aims to effectively schedule n jobs in two parallel blocking flow shops, each of which has m machines. Considering the imprecise and vague temporal parameters in real-world production, the authors formulated a fuzzy parallel blocking flow shop scheduling (PBFSP) problem with fuzzy processing time and fuzzy due date, seeking to minimize the fuzzy makespan and maximize the average agreement index. Then, a multi-objective genetic algorithm (MOGA) was proposed to solve the problem. In the MOGA, the solutions were represented by a chromosome with a separator, the crossover and mutation operations were designed based on the specific representation of a solution, and a local search method was embedded to enhance the search capability. After that, the MOGA was contrasted with two famous multi-objective evolutionary algorithms through an experiment on the production instances of panel block assembly in shipbuilding. The results demonstrate the superiority of the MOGA in generating optimal solutions to the bi-criterion fuzzy PBFSP. The research findings shed new lights on the solution to the PBFSP and similar problems.

• 出版日期2018
• 单位上海交通大学