Minimizing Makespan on a Single Batch Processing Machine with Non-identical Job Sizes: A Hybrid Genetic Approach

Abstract

This paper addresses minimizing makespan by genetic algorithm (GA) for scheduling jobs with non-identical sizes on a single batch processing machine. We propose two different genetic algorithms based on different encoding schemes. The first one is a sequence based GA (SGA) that generates random sequences of jobs and applies the batch first fit (BFF) heuristic to group the jobs. The second one is a batch based hybrid GA (BHGA) that generates random batches of jobs and ensures feasibility through using knowledge of the problem. A pairwise swapping heuristic (PSH) based on the problem characteristics is hybridized with BHGA that has the ability of steering efficiently the search toward the optimal or near optimal schedules. Computational results show that BHGA performs considerably well compared with a modified lower bound and significantly outperforms the SGA and a simulated annealing (SA) approach addressed in literature. In comparison with a constructive heuristic named FFLPT, BHGA also shows its superiority.