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| Tekkaya, A. Erman |
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| Förster, Peter |
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| Mudimu, George T. |
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| Shibata, Lillian Marie |
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| Talabbeydokhti, Nasser |
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| Laffite, Ernesto Dante Rodriguez |
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| Schöpke, Benito |
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| Gobis, Anna |
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| Alfares, Hesham K. |
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| Münzel, Thomas |
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| Joy, Gemini Velleringatt |
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| Oubahman, Laila |
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| Filali, Youssef |
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| Philippi, Paula |
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| George, Alinda |
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| Lucia, Caterina De |
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| Avril, Ludovic |
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| Belachew, Zigyalew Gashaw |
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| Kassens-Noor, Eva | Darmstadt |
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| Cho, Seongchul |
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| Tonne, Cathryn |
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| Hosseinlou, Farhad |
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| Ganvit, Harsh |
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| Schmitt, Konrad Erich Kork |
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| Grimm, Daniel |
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Sethi, S. P.
in Cooperation with on an Cooperation-Score of 37%
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Publications (10/10 displayed)
- 2022Integrating equipment investment strategy with maintenance operations under uncertain failurescitations
- 2010An Incomplete Information Inventory Model with Presence of Inventories or Backorders as Only Observationscitations
- 2008Inventory Problems with Partially Observed Demands and Lost Salescitations
- 2008Optimal Advertising and Pricing in a New-Product Adoption Modelcitations
- 2006Optimality of Base-Stock and (s, S) Policies for Inventory Problems with Information Delayscitations
- 2005 $${cal K}$$ -Convexity^1 in $$Re^{n}$$ citations
- 2004Competitive Advertising Under Uncertainty: A Stochastic Differential Game Approachcitations
- 2003Optimal Pricing of a Product Diffusing in Rich and Poor Populationscitations
- 2003Harvesting Altruism in Open-Source Software Developmentcitations
- 2001Stochastic Multiproduct Inventory Models with Limited Storagecitations
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document
Inventory Problems with Partially Observed Demands and Lost Sales
Abstract
This paper considers the case of partially observed demand in the context of a multi-period inventory problem with lost sales. Demand in a period is observed if it is less than the inventory level in that period and the leftover inventory is carried over to the next period. Otherwise, only the event that it is larger than or equal to the inventory level is observed. These observations are used to update the demand distributions over time. The state of the resulting dynamic program consists of the current inventory level and the current demand distribution, which is infinite dimensional. The state evolution equation for the demand distribution becomes linear with the use of unnormalized probabilities. We study two demand cases. First, the demands evolve according to a Markov chain. Second, the demand distribution has an unknown parameter which is updated in the Bayesian manner. In both cases, we prove the existence of an optimal feedback ordering policy.
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