Operations Academic Seminar

Finding Optimal base-stock policy for perishable inventory systems with demand learning

Perishable inventory system is an important class of stochastic inventory system that is notoriously difficult to analyze and solve, and the best base-stock policy has been shown both theoretically and numerically to perform nearly optimal.  We develop non-parametric learning algorithm that finds the best base-stock when demand distribution is not known a priori, and show that the algorithms achieve the best possible convergence rates.  Numerical results demonstrate that the learning algorithm performs very well.  This is a joint work with Huanan Zhang and Cong Shi.

Fee: [Yes/No/Varies]

Contact Information:
Tedda Nathan
Dept. Administrator. Dept of Operations
216-368-2040 [Phone]
216-368-6250 [Fax]

Today from 1 p.m. to 2:30 p.m.
Peter B. Lewis Building, 418
11119 Bellflower Road
Cleveland, OH 44106-7235
United States
Speaker(s): Xiulu Chao, Univ. of Michigan
Chao paper
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