A Network Decomposition Approach for Approximating the Steady-State Behavior of Markovian Multi-Echelon Reparable Item Inventory Systems

Donald Gross, Leonidas C. Kioussis and Douglas R. Miller
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Donald Gross: Department of Operations Research, The George Washington University, Washington, DC 20052
Leonidas C. Kioussis: Department of Operations Research, The George Washington University, Washington, DC 20052
Douglas R. Miller: Department of Operations Research, The George Washington University, Washington, DC 20052

Management Science, 1987, vol. 33, issue 11, 1453-1468

Abstract: We develop a method for obtaining approximate steady-state probabilities for large multi-echelon reparable item inventory systems modeled as non-Jacksonian Markovian networks with finite state space. The approximation involves decomposing the network model into smaller overlapping local subnetwork models, solving them in "isolation" and iterating back and forth among the subnetwork models until convergence is obtained. Numerical results show that the method is quite accurate and efficient for this application.

Keywords: Markov networks; computational probability; multi-echelon inventory systems; reparable items (search for similar items in EconPapers)
Date: 1987
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:33:y:1987:i:11:p:1453-1468

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