A sorting based efficient heuristic for pooled repair shop designs

In this paper, we address the assignment problem of skills to servers (e.g., repairmen) in a multi-server repair shop of a spare parts supply system. This type of assignment problems tends to be hard in general, due to the lack of analytical queuing models with skill-based item-server assignments. In this paper, we propose a joint skill-server assignment and inventory optimization heuristic based on “pooled” repair shop designs. The heuristic decomposes the repair shop problem into sub-systems based on some attributes of repairable items. Each subsystem is responsible for its group of repairable items with full cross-training of the subsystem servers. The pooled designs reduce the complexity of the problem and enable the use of queue-theoretical approximations to optimize the inventory and repair shop capacity. The conducted numerical experiments show that the pooled skill-server assignments optimized by the proposed heuristic can reduce the total costs by 4% when compared to the skill-server assignments obtained by Genetic Algorithm and Simulated Annealing based methods. Furthermore, in terms of cost and computation speed, the proposed heuristic shows better results than a Simulation-Optimization based skill-server assignment heuristic, which considers all possible assignments.