This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, such as the neglect of data-driven estimation for vital production parameters, exclusive generation of point forecasts without considering model uncertainty, and lacking explanations regarding the sources of such uncertainty. Our approach employs Quantile Regression Forests for generating interval predictions, alongside both local and global variants of SHapley Additive Explanations for the examined predictive process monitoring problem. The practical applicability of the proposed methodology is substantiated through a real-world production planning case study, emphasizing the potential of prescriptive analytics in refining decision-making procedures. This paper accentuates the imperative of addressing these challenges to fully harness the extensive and rich data resources accessible for well-informed decision-making.

翻译：本文提出了一种全面的多阶段机器学习方法，有效整合信息系统与人工智能，以增强运筹学领域内的决策流程。所提出的框架巧妙解决了现有方案的常见局限，例如忽略对关键生产参数的数据驱动估计、仅生成点预测而不考虑模型不确定性，以及缺乏对不确定性来源的阐释。我们的方法采用分位数回归森林来生成区间预测，并结合SHapley加法解释的局部与全局变体，针对所研究的预测过程监控问题展开分析。通过一个真实的生产规划案例研究，验证了所提方法的实际适用性，强调了规范分析在优化决策流程中的潜力。本文着重指出，为充分利用可用于明智决策的广泛而丰富的数据资源，解决这些挑战至关重要。