A Hybrid Genetic Algorithm for Flexible Job Shop Scheduling Problem with Batch Processing Machines

The flexible job scheduling problem with batch processing machines (FJSP-BPM) is an extension of the flexible job shop scheduling problem and batch scheduling problem in some engineering scenarios. It allows an operation to be processed by any usable machines, and multiple jobs can be processed on a batch in batch processing machines simultaneously. In this study, a mixed integer linear programming model is proposed to minimize the makespan. This study first designs a strategy of chromosome slicing based on the marginal cost to generate batches and a hybrid genetic algorithm (HGA) is proposed to solve the FJSP-BPM problem. Neighborhood structures are designed to search for better solutions. Finally, the experimental results demonstrate that the proposed HGA has obtained the best solutions for all instances and has effectively solved the FJSP-BPM problem.