Hybrid meta-heuristics for the unrelated parallel machine scheduling problem with setup times

Abstract

This study focuses on an unrelated parallel machine scheduling problem with machine and job sequence-dependent setup times (UPMST) aiming to minimise the makespan. Recently, adaptive large neighbourhood search with learning automata (LA-ALNS) has been employed to tackle the UPMST with excellent performance. However, its search efficiency is heavily affected by re-visiting the recent solutions, which is known as the short-term cycle. Moreover, it remains challenging to balance the computational cost and quality of the solutions. To address these challenges, we propose a hybrid meta-heuristic method based on LA-ALNS and tabu search (LA-ALNS-TS). In the proposed algorithm, LA-ALNS is used to guide the exploration and TS is used to alleviate the short-term cycle experienced during the search process. In addition, we design two local search operators to achieve a balanced trade-off between the search efficiency and quality of the solutions. A dynamic perturbation scheme is proposed to improve the solution and help the algorithm escape from local optima. Comprehensive experiments were conducted to assess the performance of the algorithm on two exhaustive benchmarks. Experimental results demonstrate the effectiveness and efficiency of the proposed hybrid meta-heuristic method.