Abstract
This article addresses a challenging industrial problem known as the unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup times. In UPMSP, we have a set of machines and a group of jobs. The goal is to find the optimal way to schedule jobs for execution by one of the several available machines. UPMSP has been classified as an NP-hard optimisation problem and, thus, cannot be solved by exact methods. Meta-heuristic algorithms are commonly used to find sub-optimal solutions. However, large-scale UPMSP instances pose a significant challenge to meta-heuristic algorithms. To effectively solve a large-scale UPMSP, this article introduces a two-stage evolutionary variable neighbourhood search (EVNS) methodology. The proposed EVNS integrates a variable neighbourhood search algorithm and an evolutionary descent framework in an adaptive manner. The proposed evolutionary framework is employed in the first stage. It uses a mix of crossover and mutation operators to generate diverse solutions. In the second stage, we propose an adaptive variable neighbourhood search to exploit the area around the solutions generated in the first stage. A dynamic strategy is developed to determine the switching time between these two stages. To guide the search towards promising areas, a diversity-based fitness function is proposed to explore different locations in the search landscape. We demonstrate the competitiveness of the proposed EVNS by presenting the computational results and comparisons on the 1640 UPMSP benchmark instances, which have been commonly used in the literature. The experiment results show that our EVNS obtains better results than the compared algorithms on several UPMSP instances.
Highlights
Increasing productivity and minimising cost are the most challenges issues faced by manufacturing companies
If we examine individual comparison, evolutionary variable neighbourhood search (EVNS) is better than Genetic algorithm (GA), Simulated annealing (SA), Iterated local search (ILS), Late acceptance hill-climbing (LAHC) and Step counting hill-climbing (SCHC)
We proposed an evolutionary descent algorithm that utilises various crossover operators and mutation operators to explore the search space
Summary
Increasing productivity and minimising cost are the most challenges issues faced by manufacturing companies. Handling these issues can significantly improve companies business sustainability. The machine scheduling problem (MSP) is the most critical task in production planning and control, which has a direct impact on productivity and production costs. The main goal is to reduce production completion times by efficiently allocating all resources in order to improve customer satisfaction, production costs and delivery. This paper addresses a particular variant of MSP known as the unrelated parallel machine scheduling problem (UPMSP) with sequencedependent setup times [4], [5], [6]. UPMSP has several real-world applications such as ceramics plant, mail facilities, semiconductor manufacturing, sport tournaments, block erection scheduling in a shipyard, automobile gear manufacturing process, steel making industry, chemical processes, hospital operating rooms management and human resources [7], [8], [9], [10], [11], VOLUME xx, xx
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