Improved NSGA2 Algorithm to Solve Multi-Objective Flexible Job Shop Scheduling Problem

The NSGA2 algorithm is one of the effective methods to solve multi-objective flexible job shop scheduling problems (MOFJSP). An improved NSGA2 algorithm is proposed to solve the MOFJSP model that aims to minimize the maximum completion time, the total workload of all machines, the total workshop carbon emissions, the total workshop energy consumption, and the delivery time. Firstly, the improved algorithm performs neighborhood search and cross-mutation operation respectively according to the nondominated ranking level and randomly generated probability of individuals to balance their local search and global search ability of the algorithm. Then, in order to further enrich the diversity of the population and improve the solving ability of the improved algorithm, an elite retention combined with random retention is proposed to retain the parent individuals. At last, the experiment proves the effectiveness of the improved NSGA2 algorithm for solving multi-objective flexible job shop scheduling problems.