Description:

Junyu Cao

University of California, Berkeley

Title: Last-Mile Shared Delivery: A Discrete Sequential Packing Approach

Abstract: We propose a model for optimizing the last-mile delivery of n packages from a distribution center to their final recipients, using a strategy that combines the use of ride- sharing platforms (e.g., Uber or Lyft) with traditional in-house van delivery systems. The main objective is to compute the optimal reward offered to private drivers for each of the n packages such that the total expected cost of delivering all packages is minimized. Our technical approach is based on the formulation of a discrete sequential packing problem, in which bundles of packages are picked up from the warehouse at random times during the interval [0, T] where T is a time threshold. Our theoretical results include both exact and asymptotic expressions for the expected number of packages that are picked up by time T. They are closely related to the classical Rényi’s parking/packing problem. Our proposed framework is scalable with the number of packages.

Biography: Junyu Cao is a fifth-year Ph.D. Candidate in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. Her current research interests lie in 1) data-driven stochastic modeling, with applications to the sharing economy; 2) machine learning and sequential decision making, with a focus on recommendation systems and revenue management. She is one of the 16 students worldwide who received the 2019 IBM fellowship. Her recent papers were accepted by Mathematics of Operations Research, Random Structures & Algorithms, ICML, AAAI, and Transportation Research Part C.