A Self-Adaptive Multiobjective Differential Evolution Algorithm for the Unrelated Parallel Batch Processing Machine Scheduling Problem

In this paper, the unrelated parallel batch processing machine (UPBPM) scheduling problem is addressed to minimize total energy consumption (TEC) and makespan. Firstly, a mixed-integer line programming model (MILP) of UPBPM presented. Secondly, self-adaptive multiobjective differential evolution (AMODE) algorithm put forward. Since parameter value can affect performance greatly, an adaptive control method proposed according convergence index individual degree population improve exploitation exploration ability algorithm. Meanwhile, mutation strategy algorithm’s solutions’ diversity. Finally, verify effectiveness algorithm, comparative experiments are carried out on 20 instances with 5 different scales. Numerical comparisons indicate that achieve high comprehensive performance.