Dynamic demand fulfillment in spare parts networks with multiple customer classes |
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Authors: | H.G.H. Tiemessen M. Fleischmann G.J. van Houtum J.A.E.E. van Nunen E. Pratsini |
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Affiliation: | 1. Department of Mathematical and Computational Sciences, IBM Research, Zurich Research Laboratory, 8803 Rüschlikon, Switzerland;2. Business School, University of Mannheim, P.O. Box 10 34 62, 68131 Mannheim, Germany;3. School of Industrial Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands;4. Rotterdam School of Management, Erasmus University, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands |
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Abstract: | We study real-time demand fulfillment for networks consisting of multiple local warehouses, where spare parts of expensive technical systems are kept on stock for customers with different service contracts. Each service contract specifies a maximum response time in case of a failure and hourly penalty costs for contract violations. Part requests can be fulfilled from multiple local warehouses via a regular delivery, or from an external source with ample capacity via an expensive emergency delivery. The objective is to minimize delivery cost and penalty cost by smartly allocating items from the available network stock to arriving part requests. We propose a dynamic allocation rule that belongs to the class of one-step lookahead policies. To approximate the optimal relative cost, we develop an iterative calculation scheme that estimates the expected total cost over an infinite time horizon, assuming that future demands are fulfilled according to a simple static allocation rule. In a series of numerical experiments, we compare our dynamic allocation rule with the optimal allocation rule, and a simple but widely used static allocation rule. We show that the dynamic allocation rule has a small optimality gap and that it achieves an average cost reduction of 7.9% compared to the static allocation rule on a large test bed containing problem instances of real-life size. |
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Keywords: | Inventory Spare parts Dynamic demand fulfillment Multiple customer classes |
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