Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/98611
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Title: Ergodic control for a mean reverting inventory model
Authors: Liu, J
Yiu, KFC 
Bensoussan, A
Issue Date: Jul-2018
Source: Journal of industrial and management optimization, July 2018, v. 14, no. 3, p. 857-876
Abstract: In this paper, an inventory control problem with a mean reverting inventory model is considered. The demand is assumed to follow a continuous diffusion process and a mean-reverting process which will take into account of the demand dependent of the inventory level. By choosing when and how much to stock, the objective is to minimize the long-run average cost, which consists of transaction cost for each replenishment, holding and shortage costs associated with the inventory level. An approach for deriving the average cost value of infinite time horizon is developed. By applying the theory of stochastic impulse control, we show that a unique (s, S) policy is indeed optimal. The main contribution of this work is to present a method to derive the (s, S) policy and hence the minimal long-run average cost.
Keywords: Dynamic programming
Inventory policy
Mean reverting model
Ergodic control
Stochastic impulse control
Publisher: AIMS Press
Journal: Journal of industrial and management optimization 
ISSN: 1547-5816
EISSN: 1553-166X
DOI: 10.3934/jimo.2017079
Rights: © American Institute of Mathematical Sciences
This article has been published in a revised form in Journal of Industrial and Management Optimization http://dx.doi.org/10.3934/jimo.2017079. This version is free to download for private research and study only. Not for redistribution, re-sale or use in derivative works.
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