A Multiperiod Newsvendor Problem with Partially Observed Demand

Authors:
Alain Bensoussan;Metin Çakanyıldırım;Suresh P. Sethi
Affiliations:
International Center for Decision and Risk Analysis, School of Management, P.O. Box 830688, University of Texas at Dallas, Richardson, Texas 75083;School of Management, P.O. Box 830688, University of Texas at Dallas, Richardson, Texas 75083;Center for Intelligent Supply Networks, School of Management, P.O. Box 830688, University of Texas at Dallas, Richardson, Texas 75083
Venue:
Mathematics of Operations Research
Year:
2007

Citing 0
Cited 5

Technical Note---A Note on “The Censored Newsvendor and the Optimal Acquisition of Information”

Operations Research
Achieving a Long-Term Service Target with Periodic Demand Signals: A Newsvendor Framework

Manufacturing & Service Operations Management
A Censored-Data Multiperiod Inventory Problem with Newsvendor Demand Distributions

Manufacturing & Service Operations Management
Optimal learning of transition probabilities in the two-agent newsvendor problem

Proceedings of the Winter Simulation Conference
Salesforce Contracting Under Demand Censorship

Manufacturing & Service Operations Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider a newsvendor problem with partially observed Markovian demand. Demand is observed if it is less than the inventory. Otherwise, only the event that it is larger than or equal to the inventory is observed. These observations are used to update the demand distribution from one period to the next. The state of the resulting dynamic programming equation is the current demand distribution, which is generally infinite dimensional. We use unnormalized probabilities to convert the nonlinear state transition equation to a linear one. This helps in proving the existence of an optimal feedback ordering policy. So as to learn more about the demand, the optimal order is set to exceed the myopic optimal order. The optimal cost decreases as the demand distribution decreases in the hazard rate order. In a special case with finitely many demand values, we characterize a near-optimal solution by establishing that the value function is piecewise linear.