A particle swarm optimization algorithm for minimizing the number of tardy jobs of non-identical parallel batch processing machines
Author ORCID Identifier
Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016
Batch Processing Machines (BPM) are encountered in many different manufacturing and service environments such as semiconductor burn-in operations, wafer fabrication, environmental stress screening chambers, and process industries - to name a few. In this study, non-identical BPMs are available to process several jobs simultaneously in a batch. The batches are formed such that the total size of all the jobs in a batch formed does not exceed the machine capacity and each job is processed only once on a machine. The objective is to schedule the batches formed on the machines such that the number of tardy jobs is minimized. Since the problem under study is NP-hard, a Particle Swarm Optimization (PSO) algorithm has been proposed to minimize the number of tardy jobs. The proposed approach considers the batching and scheduling problems simultaneously. The effectiveness of the PSO algorithm is examined using random instances and the results were compared to a commercial solver used to solve a mixed-integer linear program. Experimental results reveal that the PSO algorithm is competitive on smaller problem instances and reports better quality solutions in a short time on larger problem instances.
Batch processing machines, Number of tardy jobs, Particle Swarm Optimization
Hulett, Maria and Damodaran, Purushothaman, "A particle swarm optimization algorithm for minimizing the number of tardy jobs of non-identical parallel batch processing machines" (2012). NIU Bibliography. 548.
Department of Industrial and Systems Engineering