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.
Christoph Molnar
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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.