Solving the parallel batch-processing machines with different release times, job sizes, and capacity limits by metaheuristics
This study addresses the scheduling problem of parallel batch-processing machines encountered in different manufacturing environments, such as the burn-in operation in the manufacture of semiconductors and the aging test operation in the manufacture of thin film transistor-liquid crystal displays (TFT-LCDs). Each machine simultaneously processes several jobs in a batch, as long as the total size of all jobs in the batch does not exceed machine capacity. The processing time of a batch is represented by the longest time among the jobs in the batch. For this problem, a mixed integer programming (MIP) model is provided, and metaheuristics based on simulated annealing (SA) and genetic algorithm (GA) are proposed. In the proposed GA and SA, a string with (n+m?1) numbers is used to assign jobs into each machine. The multi-stage dynamic programming (MSDP) algorithm, taken from the dynamic programming (DP) algorithm of Chou in 2007, is then applied to group the jobs into batches for each machine. The experimental results show that the proposed metaheuristic techniques were successfully employed in solving the scheduling problems of parallel batch-processing machines with makespan criterion.
parallel batch-processing machine simulated annealing genetic algorithm makespan