A Feature-Based Learning Differential Evolution Algorithm for the Flexible Job-Shop Scheduling With Occupational Repetitive Actions Index

Learning differential evolution (DE) algorithms are widely adopted to address flexible job-shop scheduling problems (FJSPs) because of the optimization ability. However, traditional learning DEs are not sufficient to develop the feature information of the problem. In this article, a feature-based learning DE algorithm (FLDE) is proposed to address FJSP considering worker health. Occupational repetitive actions index (OCRA) is an indicator that describes the degree of worker fatigue. The OCRA is utilized to ensure the feasibility of scheduling solutions generated by FLDE. A feature-based decision model (FDM) is designed to select the appropriate optimization operator for a scheduling solution. A critical operation search method is introduced to extract feature information from the scheduling solution. Experimental results reveal that FDM is critical to improving the local optimization ability of FLDE, and that FLDE outperforms the comparison algorithms on 40 problem instances.