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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