报告题目：Feature-Driven Robust Surgery Scheduling
报告时间：2024年05月11日，下午14:30-15:30
报告地点：必发9988集团B205
报告人：章宇 教授
邀请人：李文立 教授
内容简介：
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.
报告人简介：
章宇，西南财经大学“光华杰出学者计划”青年杰出教授、博导，国家级青年人才。东北大学本科、直博，新加坡国立大学联培博士、博后、访问学者。主要从事物流、供应链、交通、医疗等领域中服务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4项。在Operations Research (UTD24)、Mathematical Programming、Production and Operations Management (UTD24)、INFORMS Journal on Computing (UTD24)、Transportation Science等权威期刊发表学术论文20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、上海社科优秀成果二等奖、辽宁自科学术成果三等奖，单篇论文入选ESI高被引论文。受邀担任OR、POM、JOC、TS等学术期刊审稿人。兼任中国管理现代化研究会青年工作委员会秘书长、中国系统工程学会物流系统工程分会委员、中国运筹学会决策科学分会理事。为中远、中烟、重庆电力等做项目咨询。
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