Title: A solution to the job shop scheduling problem based on an enhanced slime mould algorithm

Authors: Trong-The Nguyen; Yingping Zeng; Chia-Hung Wang; Jinchen Yuan; Thi-Kien Dao

Addresses: School of Electronic Engineering, Fuzhou Institute of Technology, Fuzhou, 350506, China ' School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, China ' School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, China ' School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, China ' School of Electronic Engineering, Fuzhou Institute of Technology, Fuzhou, 350506, China

Abstract: 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.

Keywords: JSSP; job shop scheduling problem; slime mould algorithm; OBL; opposition-based learning; metaheuristic; scheduling optimisation.

DOI: 10.1504/IJCSM.2025.148202

International Journal of Computing Science and Mathematics, 2025 Vol.21 No.4, pp.289 - 302

Accepted: 15 May 2025
Published online: 29 Aug 2025 *

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