Hybrid Metaheuristic Framework for Multi‑Objective Job Shop Scheduling: Balancing Makespan and Resource Utilization - Sathyasundari, K; Gowthaman, P (International Journal of Engineering and Management Research, 2025)
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.