Mousavi presents “Stochastic Last-mile Delivery with Crowd-shipping and Mobile Depots” at INFORMS

Title slide of presentationKianoush Mousavi, PhD candidate at the University of Toronto, presented “Stochastic Last-mile Delivery with Crowd-shipping and Mobile Depots” on November 13, 2020 at the Institute for Operations Research and the Management Sciences (INFORMS) 2020 Annual Meeting.

View the videorecording on YouTube: Kianoush Mousavi “Stochastic Last-mile Delivery with Crowd-shipping and Mobile Depots.”

Abstract – This study proposes a two-tier last-mile delivery model that optimally selects mobile depot locations in advance of full information about the availability of crowd-shippers, and then transfers packages to crowd-shippers for the final shipment to the customers. Uncertainty in crowd-shipper availability is incorporated by modeling the problem as a two-stage stochastic integer program. Enhanced decomposition solution algorithms including branch-and-cut and cut-and-project frameworks are developed. A risk-averse approach is compared against a risk-neutral approach by assessing conditional-value-at-risk. A detailed computational study based on the City of Toronto is conducted.

head shot of Kianoush Mousavi
Kianoush Mousavi

Kianoush Mousavi is doing his PhD in transportation planning under the supervision of Professor Matthew Roorda and co-supervision of Professor Merve Bodur at the University of Toronto. Kianoush’s main research interest is on the application of mathematical programming in transportation problems. He is doing his PhD thesis on crowd-shipping business models for improving last-mile delivery with a focus on decision making under uncertainty. Kianoush is also participating in the off-peak delivery project for the Region of Peel as the primary research analyst at U of T.