Fall 2005 Seminar Series

DATE: Thursday, September 29, 2005
LOCATION: E40-298
TIME: 4:15pm
Reception immediately following in the Philip M. Morse Reading Room, E40-106

SPEAKER:
Retsef Levi
Postdoctoral Fellow
IBM Watson Research Center

TITLE
Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models

ABSTRACT
We consider two fundamental inventory models, the single-period newsvendor problem and its multi-period extension, but under the assumption that the explicit demand distributions are not known and that the only information available is a set of independent samples drawn from the true distributions. Under the assumption that the demand distributions are given explicitly, these models are well-studied and usually are relatively easy to solve. However, in most real-life scenarios, the true demand distributions are not available or they are too complex to work with. Thus, a sampling-driven algorithmic framework is very attractive, both in practice and theoretically.

 

In the talk we shall describe how to compute sampling-based policies, that is, policies that are computed based only on observed samples of the demands without any access to, or assumptions on, the true demand distributions. Moreover, we establish bounds on the number of samples required to guarantee that with high probability, the expected cost of the sampling-based policies is arbitrarily close (i.e., with arbitrarily small relative error) compared to the expected cost of the optimal policies which have full access to the demand distributions. The bounds that we develop are general, easy to compute and surprisingly do not depend at all on the specific demand distributions.

 

Joint work with Robin Roundy and David Shmoys

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