Hybrid Metaheuristic Framework for Multi‑Objective Job Shop Scheduling: Balancing Makespan and Resource Utilization
DOI:
https://doi.org/10.5281/zenodo.17355846Keywords:
Job Shop Scheduling, Multi‑Objective Optimization, Makespan Minimization, Resource Utilization, Hybrid Metaheuristics, Genetic Algorithm, Particle Swarm OptimizationAbstract
In manufacturing and service industries, job shop scheduling problems (JSSP) are central to efficient production planning. Traditional studies often focus solely on minimizing the makespan – the total time required to complete all jobs – without explicitly considering how effectively resources (machines, buffers, energy) are utilized. To address the dual challenge of throughput and resource efficiency, we propose a hybrid metaheuristic framework that simultaneously optimizes makespan and resource utilization. The proposed approach combines a multi‑objective genetic algorithm (MOGA) with a particle swarm optimization (PSO) based refinement to dynamically allocate resources and adjust job sequences. Experiments on benchmark datasets and simulated shop‑floor scenarios demonstrate that the hybrid model yields Pareto‑optimal solutions that reduce makespan and idle time while increasing machine utilization, outperforming conventional single‑objective heuristics.
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Copyright (c) 2025 K Sathyasundari, P Gowthaman
This work is licensed under a Creative Commons Attribution 4.0 International License.
Research Articles in 'International Journal of Engineering and Management Research' are Open Access articles published under the Creative Commons CC BY License Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. This license allows you to share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially.
