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Inverse Optimization for Routing Problems
March 1, 2024, 5:44 a.m. | Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani, Bilge Atasoy
cs.LG updates on arXiv.org arxiv.org
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 …
abstract arxiv behavior cost cs.lg decision framework function makers math.oc optimization routing supervised learning through type
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