Stochastic Optimization Forests

نویسندگان

چکیده

We study contextual stochastic optimization problems, where we leverage rich auxiliary observations (e.g., product characteristics) to improve decision making with uncertain variables demand). show how train forest policies for this problem by growing trees that choose splits directly optimize the downstream quality rather than split prediction accuracy as in standard random algorithm. realize seemingly computationally intractable developing approximate splitting criteria use perturbation analysis eschew burdensome reoptimization every candidate split, so our method scales large-scale problems. prove consistently true risk and achieves asymptotic optimality. extensively validate empirically, demonstrating value of optimization-aware construction forests success efficient approximations. can reduce running time hundredfold while achieving performance close algorithms exactly reoptimize split. This paper was accepted Hamid Nazerzadeh, data science. Funding: work supported National Science Foundation [Grant 1846210]. Supplemental Material: The files online appendices are available at https://doi.org/10.1287/mnsc.2022.4458 .

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ژورنال

عنوان ژورنال: Management Science

سال: 2023

ISSN: ['0025-1909', '1526-5501']

DOI: https://doi.org/10.1287/mnsc.2022.4458