Data-driven decision-making: Paradigms, methods, and challenges

Abstract

Data-driven decision-making plays an increasingly important role in engineering management and complex operational systems under uncertainty and dynamic environments. This article reviews the major paradigms in data-driven optimization, including offline learning and stochastic optimization, robust and distributionally robust optimization under small-data regimes, and adaptive online and reinforcement learning approaches. We examine the methodological foundations of these paradigms and discuss their applications in engineering management contexts. Finally, we highlight emerging research directions at the intersection of artificial intelligence and decision-making.

Keywords

data-driven optimization / stochastic optimization / distributionally robust optimization / online learning / reinforcement learning / AI for decision-making

RIGHTS & PERMISSIONS

Higher Education Press 2026

Just Accepted

This article has successfully passed peer review and final editorial review, and will soon enter typesetting, proofreading and other publishing processes. The currently displayed version is the accepted final manuscript. The officially published version will be updated with format, DOI and citation information upon launch. We recommend that you pay attention to subsequent journal notifications and preferentially cite the officially published version. Thank you for your support and cooperation.