摘要
针对柔性作业车间调度问题,建立了以最大完工时间最小、机器最大负荷最小、总机器负荷最小为优化目标的多目标优化模型.引入多色集合理论,建立了柔性车间调度问题的多色集合约束模型,提出了基于多色集合约束模型的元胞遗传算法(apolychromatic collection based cellular genetic algorithm,PCGA),以解决遗传算法在求解柔性车间调度问题时表现出的早熟和收敛性不足等问题.用改进的元胞遗传算法求解柔性车间调度的多目标优化算例,并与其它遗传算法进行比较,实验结果表明,基于多色集合的改进元胞遗传算法在求解此问题时更为高效.
In order to solve the flexible job-shop scheduling problems（FJSP）, a multiobjective optimization model is established aiming at minimizing the makespan, minimizing maximum machine load and minimizing total machine load. According to polychromatic sets theory, the polychromatic collection constraint model of FJSP is established; and a polychromatic collection based cellular genetic algorithm（PCGA） is proposed, which is used to address the problem of premature and insufficient convergence when solving FJSP by genetic algorithm. Compared with other genetic algorithm, modified cellular genetic algorithm based on polychromatic sets is more effective for solving multiobjective flexible job shop scheduling optimization from the experi- mental results.
出处
《三峡大学学报（自然科学版）》
CAS
北大核心
2018年第1期86-91,共6页
Journal of China Three Gorges University：Natural Sciences
基金
国家自然科学基金(71501110)
关键词
多色集合
柔性作业车间调度
多目标优化
元胞遗传算法
polychromatic sets
flexible job-shop scheduling
multiobjective optimization
cellular genet- ic algorithm
