Time: January 12, 2022 (Wednesday) 10:30-12:00 am
Location: Main Building 216
Speaker: Associate Professor Liang Yong, School of Economics and Management, Tsinghua University
Speaker Profile:
Liang Yong is currently working in the School of Economics and Management, Department of Management Science and Engineering, Tsinghua University. He graduated from UC Berkeley with a Ph.D. in Industrial Engineering and Operations Research. He graduated from Purdue University and Tsinghua University with a master's degree and a bachelor's degree, respectively, and obtained a master's degree and a bachelor's degree in nuclear engineering and engineering physics. 13-14 worked for Google in charge of data center optimization. His current research interests include data-driven optimization theory; optimization theory and its application in operations management; dynamic mechanism design, etc.
Introduction to the report:
Problem definition: Observing the retail industry inevitably evolving into omnichannel, we study an offline-channel planning problem that helps an omnichannel retailer make store location and location-dependent assortment decisions in its offline channel to maximize profit across both online and offline channels, given that customers’ purchase decisions depend on not only their preferences across products but also, their valuation discrepancies across channels, as well as the hassle costs incurred. Academic/practical relevance: The proposed model and the solution approach extend the literature on retail-channel management, omnichannel assortment planning, and the broader field of smart retailing/cities.
Methodology: We derive parameterized models to capture customers’ channel choice and product choice behaviors and customize a corresponding parameter estimation approach employing the expectation-maximization method. To solve the proposed optimization model, we develop a tractable mixed integer second-order conic programming reformulation and explore the structural properties of the reformulation to derive strengthening cuts in closed form.
Results: We numerically validate the efficacy of the proposed solution approach and demonstrate the parameter estimation approach. We further draw managerial insights from the numerical studies using real data sets. Managerial implications: We verify that omnichannel retailers should provide location-dependent offline assortments. In addition, our benchmark studies reveal the necessity and significance of jointly determining offline store locations and assortments, as well as of incorporating the online channel while making offline-channel planning decisions.
(Organizer: Department of Management Engineering, Research and Academic Exchange Center)
