Cost Optimization Problem of Hybrid Flow-Shop Based on Differential Evolution Algorithm

A differential evolution algorithm based job scheduling method is presented, whose optimization target is production cost. The cost optimization model of hybrid flow-shop is thereby constructed through considering production cost as a factor in scheduling problem of hybrid flow-shop. In the implementation process of the method, DE is used to take global optimization and find which machine the jobs should be assigned on at each stage, which is also called the process route of the job; then the local assignment rules are used to determine the job’s starting time and processing sequence at each stage. With converting time-based scheduling results to fitness function which is comprehensively considering the processing cost, waiting costs, and the products storage costs, the processing cost is taken as the optimization objective. The numerical results show the effectiveness of the algorithm after comparing between multi-group programs.