A multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem

作者：Wang Xiaojuan; Gao Liang^*; Zhang Chaoyong; Li Xinyu

来源：International Journal of Computer Applications in Technology, 2012, 45(2-3): 115-125.

DOI：10.1504/IJCAT.2012.050700

• Chromosome structure
• Computational results
• Crossover and mutation
• Entropy principle
• Flexible job-shop scheduling
• Flexible job-shop scheduling problem
• Job shop scheduling problems
• Multi objective
• Multi objective algorithm
• Multi-objective genetic algorithm
• Pareto-optimality
• Premature convergence
• Processing time
• Real-world application
• Scheduling problem
• Uncertain factors

In many real-world applications, processing times may vary dynamically due to human factors or operating faults and there are some other uncertain factors in the scheduling problems. Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a genetic algorithm based on immune and entropy principle to solve the multi-objective fuzzy FJSP. In this improved multi-objective algorithm, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The computational results demonstrate the effectiveness of the proposed algorithm.

• 出版日期2012
• 单位华中科技大学