Hybrid Metaheuristic Framework for Multi-Objective Job Shop Scheduling: Balancing Makespan and Resource Utilization

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.