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Optimal policy for a multi-location inventory system with a quick response warehouse

Authors
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Eurandom
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Disciplines
  • Computer Science
  • Mathematics
  • Medicine

Abstract

EURANDOM PREPRINT SERIES 2011-013 Optimal Policy for a Multi-location Inventory System with a Quick Response Warehouse A.C.C. van Wijk, I.J.B.F. Adan, G.J. van Houtum ISSN 1389-2355 1 Optimal Policy for a Multi-location Inventory System with a Quick Response Warehouse A.C.C. van Wijk∗, I.J.B.F. Adan, G.J. van Houtum Eindhoven University of Technology, Eindhoven, The Netherlands March 18, 2011 Abstract We study a multi-location inventory problem with a so-called quick response warehouse. In case of a stock-out at a local warehouse, the demand might be satisfied by a stock transfer from the quick response warehouse. We derive the optimal policy for when to accept and when to reject such a demand at the quick response warehouse. We also derive conditions under which it is always optimal to accept these demands. Furthermore, we conduct a numerical study and consider model variations. Keywords: inventory, quick response warehouse, lateral transshipment, optimal policy struc- ture. 1 Introduction In this paper we study a multi-location inventory model, with the special feature of a so-called Quick Response (QR) warehouse. When a local warehouse is out-of-stock, a part can be trans- shipped from this QR warehouse. In this way the demand is satisfied much more quickly compared to an emergency shipment from outside the network. A relevant application of this is found in spare parts inventory models, where ready-for-use parts are kept on stock for the critical component of advanced technical systems. Examples of these include the key manufacturing machines in production lines, trucks for a transportation company, ∗Corresponding author: P.O. Box 513, 5600MB Eindhoven, The Netherlands, [email protected] 1 and expensive medical equipment in a hospital. Upon break-down of a system, it demands a spare part. During this time, the system is down at very high costs because of loss of production/revenue. So, in order to reduce down time, it is im

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