English
讲座编号：jz-yjsb-2023-y009
讲座问题：基于患者特征信息的手术室调理鲁棒优化要领
主讲人：章宇
讲座时间：2023年5月26日 14:00
腾讯聚会：850 229 101
加入工具：电商与物流学院全体西席、研究生
主理单位：电商与物流学院、研究生院
主讲人简介：
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 ，新加坡国立大学团结作育博士。曾赴新加坡国立大学任研究员 ，并多次受邀会见。主要从事物流、供应链、交通、医疗运营治理的鲁棒优化与决议研究。主持和加入国家自然科学基金项目3项。在Operations Research ，Mathematical Programming ，Production and Operations Management, INFORMS Journal on Computing等期刊揭晓学术论文10余篇�；裰泄卫砜蒲в牍こ萄Щ嵊乓觳┦垦宦畚慕薄�Omega期刊最佳论文奖 ，单篇论文入选ESI高被引论文。受邀担当Operations Research ，INFORMS Journal on Computing ，Transportation Science等期刊审稿人 ，任中国运筹学会决议科学分会理事。
主讲内容：
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.
