Computation of Approximate Optimal Policies in a Partially Observed Inventory Model with Rain Checks

Automatica, Forthcoming

36 Pages Posted: 10 Sep 2009 Last revised: 16 Apr 2020

See all articles by Alain Bensoussan

Alain Bensoussan

University of Texas at Dallas - Naveen Jindal School of Management

M. Çakanyıldırım

University of Texas at Dallas - Naveen Jindal School of Management

Suresh Sethi

University of Texas at Dallas - Naveen Jindal School of Management

Ruixia (Sandy) Shi

University of Richmond - Robins School of Business, Management Department

Multiple version iconThere are 2 versions of this paper

Date Written: September 8, 2009

Abstract

This paper proposes a new methodology to solve partially observed inventory problems. Generally, these problems have infinitedimensional states that are conditional distribution of the inventory level. Our methodology involves linearizing the state transitions via unnormalized probabilities. It then uses an appropriate functional basis to represent the state. Considering the speed and stability of computations, we choose truncated Chebyshev polynomials as the basis. We use Fast Fourier Transforms along with an appropriate discretization of inventory levels to speed up the computations. These main ideas are to obtain an iterative algorithm to solve a partially observed inventory model with rain checks. In this model, the inventory manager (IM) does not know the inventory level when it is positive. Otherwise, the IM fully observes it. This model provides a context to illustrate our methodology, which applies to other such models. Although this model has been studied mathematically in the literature, the use of our algorithm provides a numerical approximation of the optimal order quantities.

These are compared to the orders released under a base mean-stock policy, where the IM replaces the unobserved inventory level with its mean and applies the well-known base stock policy. We show numerically that the optimal order quantity is very close to the base mean-stock order quantity, when the variance of the inventory distribution is small. When the mean of the inventory distribution is large, the optimal order quantity is more than the base mean-stock quantity, and it is the other way around when the mean is small or negative. These insights are explained via uncertainty and information effects and their interplay. We expect this interplay to show up in other partially observed inventory models.

Keywords: partially observed inventory, rain checks, Chebyshev polynomials, Fast Fourier Transforms, incomplete inventory information. rain checks, inventory models, partially observed systems, dynamic programming

JEL Classification: C61, C63, M11

Suggested Citation

Bensoussan, Alain and Cakanyildirim, Metin and Sethi, Suresh and Shi, Ruixia, Computation of Approximate Optimal Policies in a Partially Observed Inventory Model with Rain Checks (September 8, 2009). Automatica, Forthcoming, Available at SSRN: https://ssrn.com/abstract=1470519

Alain Bensoussan

University of Texas at Dallas - Naveen Jindal School of Management ( email )

800 West Campbell Rd
SM 30
Richardson, TX 75080-3021
United States
9728836117 (Phone)

HOME PAGE: http://www.utdallas.edu/~axb046100/

Metin Cakanyildirim

University of Texas at Dallas - Naveen Jindal School of Management ( email )

P.O. Box 830688
Richardson, TX 75083-0688
United States

Suresh Sethi

University of Texas at Dallas - Naveen Jindal School of Management ( email )

800 W. Campbell Road, SM30
Richardson, TX 75080-3021
United States

Ruixia Shi (Contact Author)

University of Richmond - Robins School of Business, Management Department ( email )

1 Gateway Road
University of Richmond
Richmond, VA 23173
United States

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