Learning Drivers’ Preferences in Delivery Route Planning
an Inverse Optimization Approach
P. van Beek (TU Delft - Mechanical Engineering)
P. Mohajerin Mohajerin Esfahani – Mentor (TU Delft - Team Peyman Mohajerin Esfahani)
P. Zattoni Scroccaro – Mentor (TU Delft - Team Peyman Mohajerin Esfahani)
B. Atasoy – Graduation committee member (TU Delft - Transport Engineering and Logistics)
Azita Dabiri – Graduation committee member (TU Delft - Team Azita Dabiri)
Ke Ren – Graduation committee member
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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.