Presentation on YouTube.

We present a contextual framework for learning routing experiences in last-mile delivery. The objective of the framework is to generate routes similar to historic high-quality ones as classified by the operational experts by considering the unstructured features of the last-mile delivery operations. The framework encompasses descriptive, prescriptive and predictive analytics. In the descriptive analytics, we extract rules and preferences of high-quality routes from the data. In the predictive analytics stage, we investigate different derivative-free algorithms for learning the preferences in order to improve the effectiveness of the methods. We develop a label-guided algorithm, which captures any hidden preferences that are not obtained in the descriptive analytics stage. We then use prescriptive methods to generate the routes. Our approach allows us to blend the advantages of all facets of data science in a single collaborative framework, which is effective in learning the preferences and generating high-quality routes. A preliminary version of our descriptive method received the third-place award in the 2021 Amazon Last-Mile Routing Research Challenge.

Short Bio: Okan is an assistant professor of operations research at HEC Montreal and a member of CIRRELT and GERAD. He received his Ph.D. degree in industrial engineering from Bilkent University. His research interests focus on the design and management of large-scale networks and disruptive technologies in transportation. He is a recipient of Prix Nouveau Chercheur at HEC Montreal and an Honorable Mention in INFORMS Transportation Science & Logistics Section Best Paper Award. His work has been published in leading journals in the field of operations research, and he currently serves on the editorial board of Transportation Research Part B.