A Two-Stage Adaptive Improved Genetic Algorithm for Flexible Job-Shop Scheduling Problem with AGVs

This paper studies the flexible job-shop scheduling problem (FJSPT) with AGVs, which can be regarded as an extension of the flexible job-shop scheduling problem (FJSP). Based on the FJSP problem, AGVs or transfer robots transport the workpiece, which makes the problem more complex. Therefore, a segmented greedy coding and greedy decoding method is proposed to select high quality initial population, and an adaptive genetic operator design is combined to improve the quality of the solution. On this basis, the solutions without transport conditions are fused into the initial population with transport conditions in order to provide directions for finding the optimal solutions. In addition, the effectiveness of the proposed method is verified by some experiments in existing literatures.