Multiprocessor task scheduling in multistage hybrid flow-shops: an ant colony system approach

Abstract

The hybrid flow-shop scheduling problem (HFSP) has been of continuing interest for researchers and practitioners since its advent. This paper considers the multistage HFSP with multiprocessor tasks, a core topic for numerous industrial applications. A novel ant colony system (ACS) heuristic is proposed to solve the problem. To verify the developed heuristic, computational experiments are conducted on two well-known benchmark problem sets and the results are compared with genetic algorithm (GA) and tabu search (TS) from the relevant literature. Computational results demonstrate that the proposed ACS heuristic outperforms the existing GA and TS algorithms for the current problem. Since the proposed ACS heuristic is comprehensible and effective, this study successfully develops a near-optimal approach which will hopefully encourage practitioners to apply it to real-world problems.