Title | ||
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Simulation based particle swarm optimization of cross-training policies in spare parts supply systems |
Abstract | ||
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We study a single location supply system for repairable spare parts. The system consists of a multi-server repair shop and inventory with ready-to-use spare parts. When a failed part is received, a new (or as-good-as-new) replacement part is sent back, and the failed part is forwarded to the repairshop. In the case of unavailability of spare parts, failed requests are backordered and fulfilled when a ready-for-use part of the same type is received from the repairshop. The repair shop has several multi-skilled parallel servers (technicians) that are capable of handling certain types of spares. In this paper, we propose a Particle Swarm Optimization heuristic combined with Discrete-Event Simulation for optimizing the cross-training policy (skill assignment scheme) while minimizing the total system cost (consisting of inventory costs, backorder penalty cost, server cost and skill cost). |
Year | DOI | Venue |
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2017 | 10.1109/ICACI.2017.7974486 | 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI) |
Keywords | Field | DocType |
Particle Swarm Optimization,simulation,multi-skilled server,heuristic skill Allocation,maintenance logistics | Particle swarm optimization,Heuristic,Spare part,Computer science,Server,Unavailability,Probability distribution,Linear programming,Maintenance engineering,Reliability engineering | Conference |
ISBN | Citations | PageRank |
978-1-5090-4727-7 | 1 | 0.35 |
References | Authors | |
10 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Andrei Sleptchenko | 1 | 83 | 10.64 |
Tarek ElMekkawy | 2 | 7 | 1.80 |
Hasan Hüseyin Turan | 3 | 10 | 5.22 |
Shaligram Pokharel | 4 | 114 | 11.78 |