In this research, we address the challenge of repairing a complex machine system with warm spares and multiple operational units. When a unit malfunctions, prompt repair is crucial. Our two-stage hierarchical repair facility comprises primary repairers responsible for the preparatory stage, handling routine maintenance and low-skilled repairs. In contrast, the execution stage involves a highly skilled secondary repairer dedicated to addressing critical issues. We focus on efficient work sequential allocation between these stages, optimizing work distribution for quicker resolution of critical problems. To analyze the system, we use the Matrix Recursive Method to determine steady-state probabilities. Our mathematical model, solved with a recursive method, allows us to calculate performance indices based on probability distributions. Additionally, we create a cost function and fine-tune decision parameters to minimize expected costs per unit of time. We employ Particle Swarm Optimization, a metaheuristic technique, to optimize these parameters for a cost-effective service system. This approach streamlines the repair process, enhancing overall efficiency. Our research aims to provide practical insights into managing complex machinery repairs, ensuring optimal resource utilization and cost-effectiveness.
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