Xinwei Chen

Xinwei Chen, Ph.D. student, Applied Mathematical and Computational Sciences

Enhances same-day delivery services

Hometown:  Fushun, China
Faculty mentor/advisor: Barrett Thomas, Professor of Business Analytics, Senior Associate Dean, Tippie College of Business
What is your degree program and expected graduate date? I am a Ph.D. candidate in Applied Mathematical and Computational Sciences (AMCS) and anticipated graduating in spring 2021.
Please describe your research: My research is concerned with mitigating the unfairness caused by geographical bias in the decision-making for same-day delivery. Due to the uncertainty in same-day delivery orders, e.g., timing and location of orders, customers that are farthest away from the depot are less likely to receive service when delivery resources are constrained. I propose a reinforcement learning approach that ensures customers from different geographical regions have an equal chance of receiving the service.
In simple terms, why does this research matter? Following the public criticism that Amazon excluded certain minority neighborhoods from their same-day delivery map, more attention has been paid to customer satisfaction and fairness of e-commerce, particularly amid the COVID-19 pandemic. My research provides online retailers with a tool to incorporate fairness in their service. Furthermore, this method can also be applied to other services that require fast response to customer requests such as ridesharing.
How soon after starting at the University of Iowa were you able to participate in research? I started my research at the end of my second year in graduate school.
How has being involved in research made you more successful at the University of Iowa? My research on industrial applications gave me opportunities to transform the knowledge I acquired in the classroom to the skill of solving real-world problems. It also broadened my view of innovative algorithms and emerging modes of business in last-mile logistics.
What are your career goals and/or plans after graduation? I am currently on job market and looking for a faculty position in academia.
Does your research have connections to or implications for COVID-19? Please explain. Systems that do not consider fairness have an especially negative societal impact: customers who do not live in the “right place” do not have an equal chance to receive deliveries from online shopping. Amid the pandemic, such a scenario is particularly problematic for the immuno-compromised and elderly, as these populations are most at risk during in-person shopping. In my research, I test the proposed method when the demand is doubled to mimic the scenario of a pandemic, and the results show it can still maintain fairness in service opportunities for customers.

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