Heuristic approaches for solving Job-Shop Scheduling Problems | ||||
| ERJ. Engineering Research Journal | ||||
| Articles in Press, Accepted Manuscript, Available Online from 21 July 2025 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/erjm.2025.370872.1392 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Sarah M. Nasr   1; Sahar A. Sallam2; Rizk M. Rizk-Allah1
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| 1Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom 32511, Egypt | ||||
| 2Department of Basic Engineering Science, Higher Institute of Engineering and Technology, ElBagour, Menoufia, Egypt | ||||
| Abstract | ||||
| Solving job shop scheduling problem (JSSP) is one of the hardest problems for both optimization researchers and industry professionals. This paper aims to solve JSSP comparing between two heuristic approaches. The paper proposes a novel, simplified method for solving Job-Shop Scheduling Problems (using a simplified Discrete Particle Swarm Optimization (SDPSO) algorithm. we compared its performance with the Genetic Algorithm, highlighting differences in effectiveness and efficiency. Modifications in DPSO significantly improve solution quality and enable finding optimal solutions for various JSSP instances, outperforming traditional methods in efficiency and effectiveness. To validate the effectiveness of our strategy, Comprehensive studies on Lawrence instances, particularly 10*5 benchmark cases, validate the algorithm's superior performance. The method focuses on the JSSP problems, addressing solution quality. In our approach, we introduce advanced optimization techniques and refine the strategy for solving JSSP problems. Our contribution results in improved solution quality, faster computation, and a practical approach for real-world applications. | ||||
| Keywords | ||||
| Job-Shop Scheduling; Swarm Algorithms; Heuristics Algorithms | ||||
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