講座編號：jz-yjsb-2023-y009

講座題目：基于患者特征信息的手術室調度魯棒優化方法

主講人：章宇

講座時間：2023年5月26日 14:00

騰訊會議：850 229 101

參加對象：電商與物流學院全體教師、研究生

主辦單位：電商與物流學院、研究生院

主講人簡介：

章宇，西南財經大學大數據研究院教授、博導。東北大學本科、直博，新加坡國立大學聯合培養博士。曾赴新加坡國立大學任研究員，并多次受邀訪問。主要從事物流、供應鏈、交通、醫療運營管理的魯棒優化與決策研究。主持和參與國家自然科學基金項目3項。在Operations Research，Mathematical Programming，Production and Operations Management, INFORMS Journal on Computing等期刊發表學術論文10余篇。獲中國管理科學與工程學會優秀博士學位論文獎、Omega期刊最佳論文獎，單篇論文入選ESI高被引論文。受邀擔任Operations Research，INFORMS Journal on Computing，Transportation Science等期刊審稿人，任中國運籌學會決策科學分會理事。

主講內容：

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.