Title: Joint Inventory Allocation and Order Fulfillment in Online Retail
Abstract: We consider inventory allocation and fulfillment decisions faced by an online retailer. We have a set of products with given numbers of units to be allocated at different fulfillment centers with capacity constraints. Once we make the allocation decisions, we face random demands over the selling horizon for the products from different demand regions. In response to each demand, we choose a fulfillment center to use to serve the demand. Our goal is to decide where to place the units to maximize the total expected profit from the sales over a finite selling horizon. We give a general approximation framework for this joint inventory allocation and fulfillment problem. Our framework is based on constructing a surrogate function that upper bounds the total expected profit obtained by the optimal policy, and lower bounds the total expected profit obtained by an approximate policy. We make the inventory allocation decisions by maximizing the surrogate subject to capacity constraints at the fulfillment centers, whereas we make the fulfillment decisions by following the approximate policy. We show that we can obtain a performance guarantee by using this general framework. We use synthetically generated datasets, as well as datasets based on an online retailer, to test the practical performance of our framework.
Bio: Paat Rusmevichientong is the Justin Dart Professor of Operations Management and Professor of Data Sciences and Operations in the Marshall School of Business at the University of Southern California. Prior to joining the Marshall School, he was a faculty in the School of Operations Research and Information Engineering at Cornell University. His research interests focus on revenue management, choice modeling, pricing, assortment optimization, and large-scale dynamic programming. From 2003 through 2004, he worked in the data mining and personalization group at Amazon.com. He received BA (1997) in Mathematics from University of California, Berkeley, and MS (1999) and PhD (2003) in Operations Research from Stanford University. He is a member of INFORMS.
He has been a recipient of the University of California Regents' and Chancellor's Scholarship (1995-1997), the UC Berkeley Dorothea Klumpke Roberts Prize in Mathematics (1997), the Stanford Graduate Fellowship (1997-2001), the INFORMS George B. Dantzig Dissertation Award (First Prize, 2003), an Honorable Mention in the INFORMS Junior Faculty Interest Group Paper Competition (2006), the NSF CAREER Award (2008), the Cornell Sonny Yau '72 Excellence in Teaching Award (2008), the Cornell ORIE Professor of the Year Award (2011, 2009), the Operations Research Meritorious Service Award (2012), the USC Marshall Dean's Award for Research Excellence (2013), the USC Marshall Golden Apple Award for Excellence in Teaching Undergraduates (2013), the Evan C. Thompson Teaching and Learning Innovation Award (2015), the INFORMS Revenue Management and Pricing Section Prize (2019), the USC Marshall Golden Apple Award for Excellence in Teaching MBA Electives (2022, 2016), and the Evan C. Thompson Faculty Mentoring and Leadership Award (2022).
