A Review on Metaheuristic Approaches for Job-Shop Scheduling Problems

Abdolrazzagh-Nezhad, Majid
Abdullah, Salwani

Data Science: Journal of Computing and Applied Informatics
Vol. 8 No. 1 (2024): Data Science: Journal of Computing and Applied Informatics (JoCAI)

Over the past several decades, interest in metaheuristic approaches to address job-shop scheduling problems (JSSPs) has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides a significant attention on reviewing state-of-the-art metaheuristic approaches that have been developed to solve JSSPs. These approaches are analysed with respect to three steps: (i) preprocessing, (ii) initialization procedures and (iii) improvement algorithms. Through this review, the paper highlights the gaps in the literature and potential avenues for further research.