PhD Research Proposal Presentation: Mr. Quan Zhou
Mr. Quan Zhou, a doctoral student at 海角社区 in the area of Operations Management will be presenting his research proposal entitled:
Essays on E-commerce Order Fulfillment and Customer Behavior
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Monday, September 16, 2024 at 10:00am 鈥� 12:00pm
Student Committee Chair: Professor听Mehmet Gumus
Please note that the presentation will be conducted virtually on Zoom. If you wish to attend the presentation, kindly reach out to the PhD Office for the Zoom link.
ABSTRACT:
This research, with its practical implications, seeks to optimize small and medium-sized businesses (SMBs) operations by understanding customer behavior, designing company-consumer online shopping interfaces, and developing effective fulfillment policies. The optimization problems encountered in these settings often do not yield tractable optimal solutions due to the stochastic and multi-dimensional nature of demand. Therefore, my research takes an approximation approach.
In the first paper, we explore the optimization of the middle-mile fulfillment process in the context of e-commerce. In collaboration with a prominent e-commerce retailer in North America specializing in electronics and computer products, we developed a stochastic optimization problem to demonstrate how an efficient middle mile can alleviate strain on the critical last mile, leading to cost reduction and improved performance. We introduce a Lagrangian relaxation-based policy (referred to as tLR) as a heuristic approach for fulfillment decisions and prove its performance guarantee. Our study highlights the benefits of multi-period fulfillment windows and the cost-reducing capabilities of the tLR policy. We conclude by emphasizing the importance of dynamic fulfillment strategies and the considerations that e-commerce companies should consider when selecting their fulfillment policies.
In the second paper, we explore the joint optimization of the order fulfillment process with personalized delivery options in the context of e-commerce. Customers can choose from a customized set of fulfillment options to proceed with the purchase or leave with no purchase. Fulfillment assignments of purchased orders are determined periodically. We model customer behavior with a general discrete choice model and formulate the joint optimization as a stochastic dynamic program. We propose a tractable deterministic approximation and develop a computationally efficient heuristic with a provable performance guarantee. Using real datasets collected from our industrial partner, we demonstrate the value of personalizing fulfillment options for the customers and jointly optimizing the options with fulfillment assignments.
