Policies for the Stochastic Inventory Problem with Forecasting

dc.contributor.author	Hurley, Gavin J
dc.date.accessioned	2008-03-03T14:04:16Z
dc.date.available	2013-03-03T07:22:34Z
dc.date.issued	2008-03-03T14:04:16Z
dc.description.abstract	The design of effective inventory control policies for models with stochastic demands and forecast updates that evolve dynamically over time is a fundamental problem in supply chain management. In particular, this has been a very challenging theoretical and practical problem, even for models with a very simple forecast update mechanism. In this work, we present new algorithms for this problem and present extensive computational results that demonstrate their empirical performance. Our primary contribution to the study of this problem is a new policy iteration algorithm that yields a well-performing, computationally tractable approximation to the solution. In addition, we build on work of Levi et al. and extend their new Minimizing and Balancing policies for the problem. Furthermore, we perform an extensive computational investigation of all our new policies and compare their performance to the Myopic policy.	en_US
dc.identifier.other	bibid: 6397096
dc.identifier.uri	https://hdl.handle.net/1813/10133
dc.language.iso	en_US	en_US
dc.subject	Stochastic Inventory	en_US
dc.subject	Forecasting	en_US
dc.title	Policies for the Stochastic Inventory Problem with Forecasting	en_US
dc.type	dissertation or thesis	en_US

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