Study on Scheduling Optimization for Flexible Job Shop
A hybrid genetic algorithm based on Pareto was proposed and applied to flexible job shop scheduling problem (FJSP) with multi-objective, and the multiobjective FJSP optimization model was built, where the make-span and the machine utilization rate were concerned. The algorithm embeds Pareto ranking strategy into 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. Pareto filter saves the optimum individual occurring in the course of evolution, which avoids losing the optimum solutions. Three simulation experiments are carried out to illustrate that the proposed method could solve multi-objective job shop scheduling problem effectively.
multi-objective optimization Pareto optimum genetic algorithm flexible job shop scheduling
Xiaoxia Liu Chunbo Liu Ze Tao
Henan University of Technology, Zhengzhou 450007 China Shenyang Ligong University, Shenyang 110168, China
国际会议
2010 International Conference on Advanced Mechanical Engineering(2010年先进机械工程国际学术会议 AME 2010)
洛阳
英文
821-825
2010-09-04（万方平台首次上网日期，不代表论文的发表时间）