Inverse Optimization for Routing Problems
This work addresses the challenge of replicating human driver routing behavior for real-world logistics, representing an incremental improvement with a novel method for a known bottleneck.
The authors tackled the problem of learning decision-makers' routing preferences by proposing an Inverse Optimization (IO) method tailored to routing problems, achieving a 2nd place ranking out of 48 models in the Amazon Last Mile Routing Research Challenge.
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 2nd 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.