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 multi-objective 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.