Learning Drivers’ Preferences in Delivery Route Planning

an Inverse Optimization Approach

Master thesis (2022)

Authors

P. van Beek Mechanical Engineering

Contributors

P. Mohajerin Esfahani Team Peyman Mohajerin Esfahani - Mechanical, Maritime and Materials Engineering (supervisor 1)
P. Zattoni Scroccaro Team Peyman Mohajerin Esfahani - Mechanical, Maritime and Materials Engineering (supervisor 1)
B. Atasoy Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 2)
A. Dabiri Team Azita Dabiri - Mechanical, Maritime and Materials Engineering (supervisor 2)
Ke Ren (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2022 Piet van Beek
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Published Date

10-11-2022

Language

English

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Faculty

Mechanical Engineering

Abstract

Optimizing delivery routes is a well-researched topic, however, most of the classical approaches do not incorporate preferences of drivers, as those approaches focus on minimizing the time or distance of the routes. As a result, the actual driven route of an experienced driver often deviates from the proposed route since the drivers have tacit knowledge about the real-life conditions of the road network. Amazon proposed a challenge to learn a delivery route planning strategy from historically driven routes and thus incorporate this tacit knowledge.
In this thesis, we will tackle the challenge using data-driven inverse optimization to learn the zone sequencing patterns of drivers. The zone sequences of expert drivers are assumed to be the solutions to a traveling salesman problem (TSP) in which the weights represent the preference of a driver to use a certain edge. The values of the weights will be learned through inverse optimization. Our final approach achieves a score that ranks 4th out of the 48 models that qualified for the final round of the challenge.