Optimizing Makespan and Buffer Allocation for Enhanced Job Shop Scheduling Efficiency: A Hybrid Metaheuristic Approach
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866902164220149760 |
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| author | K.Sathyasundari P.Gowthaman |
| author_facet | K.Sathyasundari P.Gowthaman |
| contents | <p>In today’s highly competitive manufacturing landscape, optimizing job shop scheduling has become vital for maximizing operational efficiency and minimizing production delays. This study explores the dual challenge of makespan minimization and efficient buffer management within job shop environments. By integrating a hybrid metaheuristic approach combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), we propose an intelligent scheduling framework that not only reduces the overall makespan but also enhances the utilization of intermediate buffers across machines. The experimental results, validated through benchmark datasets and real-time shop floor simulations, demonstrate significant improvements in throughput, machine utilization, and flow consistency. Our approach outperforms traditional heuristics by dynamically adjusting buffer capacities and job sequencing based on system feedback, paving the way for more resilient and adaptive manufacturing operations.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16734859 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Optimizing Makespan and Buffer Allocation for Enhanced Job Shop Scheduling Efficiency: A Hybrid Metaheuristic Approach K.Sathyasundari P.Gowthaman job shop scheduling makespan minimization buffer management hybrid metaheuristics genetic algorithm particle swarm optimization <p>In today’s highly competitive manufacturing landscape, optimizing job shop scheduling has become vital for maximizing operational efficiency and minimizing production delays. This study explores the dual challenge of makespan minimization and efficient buffer management within job shop environments. By integrating a hybrid metaheuristic approach combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), we propose an intelligent scheduling framework that not only reduces the overall makespan but also enhances the utilization of intermediate buffers across machines. The experimental results, validated through benchmark datasets and real-time shop floor simulations, demonstrate significant improvements in throughput, machine utilization, and flow consistency. Our approach outperforms traditional heuristics by dynamically adjusting buffer capacities and job sequencing based on system feedback, paving the way for more resilient and adaptive manufacturing operations.</p> |
| title | Optimizing Makespan and Buffer Allocation for Enhanced Job Shop Scheduling Efficiency: A Hybrid Metaheuristic Approach |
| topic | job shop scheduling makespan minimization buffer management hybrid metaheuristics genetic algorithm particle swarm optimization |
| url | https://doi.org/10.5281/zenodo.16734859 |