IEDA/ISOM Joint Seminar - Asymptotic Optimality of Base-Stock Policies for Perishable Inventory Systems

2:00pm - 3:30pm
Room 5562 (lift 27-28)

We consider periodic review perishable inventory systems with a fixed product lifetime. Unsatisfied demand can be either lost or backlogged. The objective is to minimize the long-run average holding, penalty, and outdating cost. The optimal policy for these systems is notoriously complex and computationally intractable because of the curse of dimensionality. Hence, various heuristic replenishment policies are proposed in the literature, including the base-stock policy which raises the total inventory level to a constant in each review period. Although various studies show near-optimal numerical performances of base-stock policies in the classic system with zero replenishment lead time and a first-in-first-out issuance policy, the results on their theoretical performances are very limited. In this paper, we first focus on this classic system and show that a simple base-stock policy is asymptotically optimal when any one of the product lifetime, demand population size, unit penalty cost, and unit outdating cost becomes large; moreover, its optimality gap converges to zero exponentially fast in the first two parameters. We then study two important extensions. For a system under a last-in-first-out or even an arbitrary issuance policy, we prove that a simple base-stock policy is asymptotically optimal with large product lifetime, large unit penalty costs, and large unit outdating costs, and for a backlogging system with positive lead times, we prove that our results continue to hold with large product lifetime, large demand population sizes, and large unit outdating costs. Finally, we provide a numerical study to demonstrate the performances of base-stock policies in these systems.

This is joint work with Xiting Gong and Xiuli Chao.

Event Format
Speakers / Performers:
Prof. Jinzhi BU
Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University

Jinzhi Bu is an Assistant Professor in the Department of Logistics and Maritime Studies at the Hong Kong Polytechnic University. Her research interests include stochastic modeling and optimization, statistical and machine learning, data-driven decision making, and their applications to supply chain management and revenue management. Prior to joining PolyU, she was a postdoctoral associate in Institute of Data, Systems, and Society at Massachusetts Institute of Technology. She obtained her Ph.D. degree from the Chinese University of Hong Kong and B.S. degree from Nanjing University.

Language
English
Recommended For
Faculty and staff
General public
PG students
Organizer
Department of Industrial Engineering & Decision Analytics
Department of Information Systems, Business Statistics & Operations Management
Post an event
Campus organizations are invited to add their events to the calendar.