Inverse Optimization for Routing Problems

Pedro Zattoni Scroccaro (), Piet van Beek (), Peyman Mohajerin Esfahani () and Bilge Atasoy ()
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Pedro Zattoni Scroccaro: Delft Center for Systems and Control, Delft University of Technology, 2628CD Delft, Netherlands
Piet van Beek: Delft Center for Systems and Control, Delft University of Technology, 2628CD Delft, Netherlands
Peyman Mohajerin Esfahani: Delft Center for Systems and Control, Delft University of Technology, 2628CD Delft, Netherlands
Bilge Atasoy: Department of Maritime and Transport Technology, Delft University of Technology, 2628CD Delft, Netherlands

Transportation Science, 2025, vol. 59, issue 2, 301-321

Abstract: We propose a method for learning decision makers’ behavior in routing problems using inverse optimization (IO). The IO framework falls into the supervised learning category and builds on the premise that the target behavior is an optimizer of an unknown cost function. This cost function is to be learned through historical data, and in the context of routing problems, can be interpreted as the routing preferences of the decision makers. In this view, the main contributions of this study are to propose an IO methodology with a hypothesis function, loss function, and stochastic first-order algorithm tailored to routing problems. We further test our IO approach in the Amazon Last Mile Routing Research Challenge, where the goal is to learn models that replicate the routing preferences of human drivers, using thousands of real-world routing examples. Our final IO-learned routing model achieves a score that ranks second compared with the 48 models that qualified for the final round of the challenge. Our examples and results showcase the flexibility and real-world potential of the proposed IO methodology to learn from decision-makers’ decisions in routing problems.

Keywords: inverse optimization; optimization algorithms; routing problems (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:59:y:2025:i:2:p:301-321

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