摘要
研究了多目标柔性作业车间调度问题(FJSP),提出了一种基于Pareto的混合遗传算法,并建立了包括生产周期、总拖期时间和机床负载在内的多目标优化模型.该算法采用基于工序的编码方式和活动化解码方法,将Pareto排序策略与Pareto竞争方法结合起来.为了保证解的多样性,采用小生境技术并同时使用多种交叉方法,用Pareto解集过滤器保存进化过程中的最优个体,防止最优解的遗失.算法最后给出问题的Pareto最优解集.仿真试验证明,提出的混合遗传算法可以有效解决多目标FJSP.
A hybrid genetic algorithm based on Pareto was proposed and applied to the multi- objective flexible job shop scheduling problem （FJSP）, and a multi-objective FJSP optimization model was developed including make-span, total tardiness and machine utilization rate. The algorithm combines Pareto ranking strategy with Pareto competition method. The operation-based encoding and an active scheduling decoding method are employed. In order to promote solution diversity, the niche technology and many kinds of crossover operations are used here. Pareto filter saves the optimum individual occurring in the course of evolution, which avoids losing the optimum solutions. The set of Pareto optimum solutions is obtained. In the end, a simulation experiment was carried out to illustrate that the proposed method can solve multi-objective FJSP effectively.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2008年第3期362-365,382,共5页
Journal of Northeastern University(Natural Science)
基金
国家高技术研究发展计划项目(2001AA412020)．
