An Improved Immune Genetic Algorithm for Solving the Flexible Job Shop Scheduling Problem with Batch Processing

This paper presents a mathematical model for the flexible job shop scheduling problem (FJSP) with batch processing for manufacturing enterprises with both the flexible job shop scheduling problem and a batch process (BP) problem in actual production. An improved immune genetic algorithm (IGA) based on greedy thought combined with local scheduling rules is used to solve this scheduling problem. In the flexible job shop part, the greedy optimal solution is obtained through the greedy thought. The concept of cross-entropy is then introduced to improve the standard IGA. Calculating the cross-entropy of the individual and greedy optimal solutions for optimization considerably accelerates the optimization speed of the algorithm and enhances the ability of the algorithm to escape the local optimum. In the batching process, effective batching rules are designed to reduce blockage and improve batching efficiency; thus, the job can quickly and effectively pass the batching process and complete the entire production process. In the algorithm verification stage, standard FJSP datasets are used to simulate and verify the proposed algorithm. Considering the specific FJFP with BP problem, we perform simulation experiments with actual production data of a transformer manufacturer. The results show that the proposed method can effectively solve such problems.