Abstract: | In the first part of this presentation, we consider a distributionally robust inventory model in which the sequence of demands must take the form of a martingale with given mean and support. We explicitly compute the minimax optimal policy (and worst-case distribution) in closed form, by combining ideas from convex analysis, probability, and dynamic programming. We prove that at optimality the worst-case demand distribution corresponds to the setting in which inventory may become obsolete at a random time, a scenario of practical interest. We also compare to the analogous setting in which demand is independent across periods, and identify interesting differences between these two models. This is joint work with David A. Goldberg (Georgia Tech). In the second part of this presentation, we consider the following dual-sourcing inventory problem: one supplier is reliable but has a longer lead time; the other one is not always reliable but has a shorter lead time. It is motivated by a real-world problem at Walmart.com and the lead time differences of many import items could be as large as 12 weeks. We propose a so-called Tailored-Base Surge (TBS) policy and prove that it is asymptotically optimal as the lead time difference grows. We test TBS by using data from Walmart.com. Our result shows that Tailored-Base Surge outperforms other heuristics such as dual-index and single-sourcing base-stock policies. Now we are in the process of implementing TBS policy at Walmart.com. This is joint work with @WalmartLabs. |
Date: | 9 December 2016 |
Time: | 11:00am - 12:00noon |
Speaker: |
Dr Linwei Xin University of Illinois at Urbana–Champaign |
Venue: | Room 7-207, 7/F, Academic 3 |
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