Operations Academic Seminarhttps://weatherhead.case.edu/events/2018/03/23/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.
Dept. Administrator. Dept of Operations
||Today from 1 p.m. to 2:30 p.m.
||Peter B. Lewis Building, 418
11119 Bellflower Road
Cleveland, OH 44106-7235
||Speaker(s): Xiulu Chao, Univ. of Michigan
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