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讲座时间:2023526 14:00

腾讯会议:850 229 101




章宇,西南财经大学大数据研究院教授、博导。东北大学本科、直博,新加坡国立大学联合培养博士。曾赴新加坡国立大学任研究员,并多次受邀访问。主要从事物流、供应链、交通、医疗运营管理的鲁棒优化与决策研究。主持和参与国家自然科学基金项目3项。在Operations ResearchMathematical ProgrammingProduction and Operations Management, INFORMS Journal on Computing等期刊发表学术论文10余篇。获中国管理科学与工程学会优秀博士学位论文奖、Omega期刊最佳论文奖,单篇论文入选ESI高被引论文。受邀担任Operations ResearchINFORMS Journal on ComputingTransportation Science等期刊审稿人,任中国运筹学会决策科学分会理事。


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.