GRASP to minimize makespan for a capacitated batch-processing machine

Abstract

This paper presents a Greedy Randomized Adaptive Search Procedure (GRASP) to minimize the makespan of a capacitated batch-processing machine. Given a set of jobs and their processing times and sizes, the objective is to group these jobs into batches and schedule the batches on a single batch-processing machine such that the time taken to complete the last batch of jobs (or makespan) is minimized. The batch-processing machine can process a batch of jobs simultaneously as long as the total size of all the jobs in that batch does not exceed the machine capacity. The batch-processing time is equal to the longest processing time for a job in the batch. It has been shown that the problem under study is non-deterministic polynomial-time hard. Consequently, a GRASP approach was developed. The solution quality of GRASP was compared to other solution approaches such as simulated annealing, genetic algorithm, and a commercial solver through an experimental study. The study helps to conclude that GRASP outperforms other solution approaches, especially on larger problem instances.