Alma Mater:National Chengchi University, Taiwan
Education Level:Postgraduate (Postdoctoral)
[MORE]1. Awardee of Excellent Young Role Models from Tainan County, Taiwan 2. Leading Scientists of the World 2009 3. Who's Who in the World 2013-2021 4. Who's Who in Science and Engineering 2016-2017 5. Who's Who in Asia 2017-2018 6. 福建省福州市台湾人才库成员 7. 中国海外杰青汇中华交流团(CSP)成员 8. 福建工程学院「本科优秀毕业设计(论文)指导教师奖」
DOI number:10.1504/IJCSM.2025.148202
Journal:International Journal of Computing Science and Mathematics
Key Words:JSSP; job shop scheduling problem; slime mould algorithm; OBL; opposition-based learning; metaheuristic; scheduling optimisation.
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.
Indexed by:Journal paper
Discipline:interdisciplinary subject
Document Type:J
Volume:21
Issue:4
Page Number:289-302
ISSN No.:1752-5063
Translation or Not:no
Date of Publication:2025-08-28
Included Journals:EI
Links to published journals:https://www.inderscience.com/info/inarticle.php?artid=148202
First Author:Trong-The Nguyen
Co-author:Yingping Zeng,Jinchen Yuan,Thi-Kien Dao
Correspondence Author:Chia-Hung Wang
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