Title:

The Search for Parking for Commercial Last-Mile Delivery in Urban Environments (or should they?)

Abstract: 

Parking is a time-consuming, and thus expensive, part of last-mile delivery in urban environments.  To build insights into driver parking behavior, we introduce the Stochastic Parking Problem (SPP) to model the search process for parking where delivery drivers may choose to park at available parking spots or double park at unavailable parking spots at the risk of receiving a fine.  To this end, the Stochastic Parking Problem (SPP) models the parking search process as a Markov Decision Process. We characterize the structure of the optimal parking policy for the SPP based on the probability that each parking spot is available and the expected cost of double parking. Further, we provide a polynomial-time algorithm to find the optimal policy for the SPP.  We utilize the optimal policy for the SPP to derive managerial insights regarding how drivers should approach the parking search process. In doing so, we identify sufficient enforcement levels to eliminate double parking from optimal parking decisions for last-mile delivery drivers.

Bio:

Ann Melissa Campbell is the Clement T. and Sylvia H. Hanson Family Chair in Manufacturing Productivity in the Department of Business Analytics at the Henry B. Tippie College of Business.  Her research focuses on freight transportation, especially on problems related to new and emerging business models.  She is a recipient of the NSF CAREER Award and serves as an Area Editor for Transportation Science.  As department chair, she led the department’s efforts to win the 2021 INFORMS UPS George D. Smith Prize for excellence in analytics education.  Since 2022, she has chaired the annual FutureBAProf workshop focused on educating PhD students and postdocs about academic careers in business schools.