This thesis proposes novel methodologies for contextual and data-driven decision-making focusing on three application areas within supply chain and transportation systems. First, it studies a feature-based newsvendor problem and proposes an integrated approach to feature selection. Second, it introduces novel optimization models and methods for integrating contextual information into day-ahead route planning. Third, it proposes a data-driven optimization framework to analyze a mixed-service ride-hailing system under distribution shift.
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This thesis proposes novel methodologies for contextual and data-driven decision-making focusing on three application areas within supply chain and transportation systems. First, it studies a feature-based newsvendor problem and proposes an integrated approach to feature selection. Second, it introduces novel optimization models and methods for integrating contextual information into day-ahead route planning. Third, it proposes a data-driven optimization framework to analyze a mixed-service ride...
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