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Data-driven decision-making: Paradigms, methods, and challenges
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AI
DOI：10.1007/s42524-026-5384-z.png)
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Abstract
En 中文
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
Journal
IF:
7.7
Papers:
434
Citations:
1.5K