A genetic algorithm to minimize maximum lateness on a batch processing machine

Authors:
Cheng-Shuo Wang;Reha Uzsoy
Affiliations:
United Technologies Research Center, 411 Silver Lane, MS 129-46, East Hartford, CT;School of Industrial Engineering, Purdue University, 1287 Grissom Hall, Purdue University, West Lafayette, IN
Venue:
Computers and Operations Research
Year:
2002

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Abstract

We consider the problem of minimizing maximum lateness on a batch processing machine in the presence of dynamic job arrivals. The batch processing machine can process up to B jobs simultaneously, and the processing time of a batch is given by that of the job with the longest processing time in the batch. We adapt a dynamic programming algorithm from the literature to determine whether a due-date feasible batching exists for a given job sequence. We then combine this algorithm with a random keys encoding scheme to develop a genetic algorithm for this problem. Computational experiments indicate that this algorithm has excellent average performance with reasonable computational burden.