A solution to the job shop scheduling problem based on an enhanced slime mould algorithm
Trong The Nguyen, Yingping Zeng, Chia Hung Wang, Jinchen Yuan, Thi Kien Dao · International Journal of Computing Science and Mathematics · 2025
The job shop scheduling problem (JSSP) is a complex optimisation challenge with broad industrial applications. This study introduces an enhanced slime mould algorithm (ESMA), designed to effectively tackle JSSP. ESMA integrates opposition-based learning (OBL) and non-linear inertia weight strategies to improve both exploration and exploitation. Benchmark evaluations demonstrate ESMA's superior performance, achieving up to a 3.36% improvement in average makespan for small-scale problems and a 15.56% reduction in makespan for large-scale instances compared to traditional and metaheuristic approaches. These results confirm ESMA's strong global search capabilities as a powerful solution to JSSP.