Particle swarm optimization for scheduling batch processing machines in a permutation flowshop

Abstract

Batch processing machines are capable of processing several jobs in a batch simultaneously. These machines are used in many real-life applications. This paper presents solution approaches to schedule batch processing machines arranged in a permutation flowshop in order to minimize its makespan (or completion time of the last batch). The processing time of each job on all the machines and their sizes are given. Each machine can process a batch of jobs as long as its capacity is not violated. The batch processing time is equal to the longest processing job in the batch. Since the problem under study is NP-hard, commercial mixed-integer solvers may require prohibitively long run time to solve even modest sized problems. Consequently, a particle swarm optimization (PSO) algorithm is proposed. Three heuristics to update the particle’s positions are also proposed. The effectiveness of the proposed PSO algorithm is compared with a commercial solver (which was used to solve a mathematical model) and several heuristics from the literature. The experimental study conducted indicates that the proposed PSO algorithm outperforms both the commercial solver and the heuristics in terms of solution quality. The commercial solver requires longer run times compared to PSO.