Feature-driven robust surgery scheduling

• 成果类型: Article
• 署名作者: Wang, Yu; Zhang, Yu; Zhou, Minglong; Tang, Jiafu
• 署名单位: Northeastern University - China; Southwestern University of Finance & Economics - China; Fudan University; Dongbei University of Finance & Economics; Dongbei University of Finance & Economics
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13949
• 发表日期: 2023
• 页码: 1921-1938
• 关键词: Machine learning; overtime riskiness index; patient feature; robust optimization; surgery scheduling

摘要

Patient features such as gender, age, and underlying disease are crucial to improving the model fidelity of surgery duration. In this paper, we study a robust surgery scheduling problem augmented by patient feature segmentation. We focus on the surgery-to-operating room allocations for elective patients and future emergencies. Using feature data, we classify patients into different types using machine learning methods and characterize the uncertain surgery duration via a feature-based cluster-wise ambiguity set. We propose a feature-driven adaptive robust optimization model that minimizes an overtime riskiness index, which helps mitigate both the magnitude and probability of working overtime. The model can be reformulated as a second-order conic programming problem. From the reformulation, we find that minimizing the overtime riskiness index is equivalent to minimizing a Fano factor. This makes our robust optimization model easily interpretable to healthcare practitioners. To efficiently solve the problem, we develop a branch-and-cut algorithm and introduce symmetry-breaking constraints. Numerical experiments demonstrate that our model outperforms benchmark models in a variety of performance metrics.