Bridging prediction and decision: Advances and challenges in data-driven optimization

作者：Yanzhi Wang, Jianxiao Wang, Haoran Zhang, Jie Song · 发表于：Nexus · 年份：2025 · DOI：10.1016/j.ynexs.2025.100057 · 被引用次数：27 · 研究领域：Big Data and Business Intelligence

Data-driven approaches have revolutionized traditional optimization methods by integrating prediction with decision-making. This review examines the theoretical foundations, strengths, recent advancements, and limitations of three key methods—sequential optimization, end-to-end learning, and direct learning—highlighting their practical applications in power grid scheduling, operations management, and intelligent autonomous control. A multidimensional comparison is presented, followed by a discussion of the challenges in data-centric methodology, optimization methodology, and decision-making application. This paper offers a methodological guide and outlines future directions for academia and industry to enhance decision-making in complex data environments. Broader context: As big data technologies advance and data volumes grow, effectively leveraging these resources for complex decision-making has become a critical challenge for academia and industry. This review examines the transformative impact of big data and intelligent systems on traditional optimization paradigms, highlighting the continuum of data-driven optimization from predictive modeling to decision implementation. Key methodologies such as ''sequential optimization,'' ''end-to-end learning,'' and ''direct learning'' are analyzed, offering both theoretical insights and practical implications. Notably, we discuss breakthroughs such as implicit differentiation techniques, surrogate loss functions, and perturbation me...