A Prescriptive Machine-Learning Framework to the Price-Setting Newsvendor Problem

Abstract

The approach to data-driven optimization described in this paper was developed when the authors were part of an IBM project team working with the U.S. Department of Energy, Pacific National Laboratory, and various energy utility partners on an initiative to develop a smart energy distribution infrastructure. Within this broader scope and based on the data collected in some initial controlled experiments, the paper specifically addresses the design and optimization of real-time price incentives to consumers to manage their electricity demand and determine the energy capacity to be provisioned by the utility. This latter problem fits into the well-known price-setting newsvendor problem framework, and our goal was to replace the simplistic methods in the literature by more realistic data-driven methods to take into account the data-collection capabilities and the modeling complexity of real-world applications. Our aspirations for the paper are (1) to introduce data-driven, distribution-free approaches to decision-making problems and (2) to motivate scalable conditional value-at-risk regression-based approaches for these problems.