A rule-augmented inverse optimization framework jointly learns expert cost preferences and interpretable decision rules, improving out-of-sample route prediction on the Amazon last-mile routing challenge.
Amazon Last-Mile Delivery Trajectory Prediction Using Hierarchical TSP with Customized Cost Matrix
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abstract
In response to the Amazon Last-Mile Routing Challenge, Team Permission Denied proposes a hierarchical Travelling Salesman Problem (TSP) optimization with a customized cost matrix. The higher level TSP solves for the zone sequence while the lower level TSP solves the intra-zonal stop sequence. The cost matrix is modified to account for routing patterns beyond the shortest travel time. Lastly, some post-processing is done to edit the sequence to match commonly observed routing patterns, such as when travel times are similar, drivers usually start with stops with more packages than those with fewer packages. The model is tested on 1223 routes that are randomly selected out of the training set and the score is 0.0381. On the 13 routes in the given model apply set, the score was 0.0375.
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An inverse mixed-integer optimization framework for learning interpretable models of expert decision making
A rule-augmented inverse optimization framework jointly learns expert cost preferences and interpretable decision rules, improving out-of-sample route prediction on the Amazon last-mile routing challenge.