Clustering and Genetic Algorithm Based Hybrid Flowshop Scheduling with Multiple Operations

This research ismotivated by a flowshop scheduling problemof our collaborativemanufacturing company for aeronautic products. The heat-treatment stage (HTS) and precision forging stage (PFS) of the case are selected as a two-stage hybrid flowshop system. In HTS, there are four parallel machines and eachmachine can process a batch of jobs simultaneously. In PFS, there are twomachines. Each machine can install any module of the four modules for processing the workpeices with different sizes. The problem is characterized bymany constraints, such as batching operation, blocking environment, and setup time and working time limitations of modules, and so forth. In order to deal with the above special characteristics, the clustering and genetic algorithm is used to calculate the good solution for the two-stage hybrid flowshop problem. The clustering is used to group the jobs according to the processing ranges of the different modules of PFS. The genetic algorithm is used to schedule the optimal sequence of the grouped jobs for the HTS and PFS. Finally, a case study is used to demonstrate the efficiency and effectiveness of the designed genetic algorithm.