Combinatorial optimization augmented machine learning for contextual multi-stage problems

DS4DM Coffee Talk
Combinatorial optimization augmented machine learning for contextual multi-stage problems
Feb 22, 2024
Maximilian Schiffer, TUM School of Management, Technical University of Munich, Germany
Combinatorial optimization augmented machine learning (COAML) is a novel field that combines methods from machine learning and operations research to tackle contextual data-driven problems that involve both uncertainty and combinatorics. These problems arise frequently in industrial processes, where firms seek to leverage large and noisy data sets to optimize their operations. COAML typically involves embedding combinatorial optimization layers into neural networks and training them with decision-aware learning techniques. This talk provides an overview of the underlying paradigm, algorithmic pipelines, and foundations based on selected application cases. Particularly, I will demonstrate the effectiveness of COAML on contextual and dynamic stochastic optimization problems, as evidenced by its winning performance on the 2022 EUROMeetsNeurIPS dynamic vehicle routing challenge.