講座編號(hào)：jz-yjsb-2023-y009
講座題目：基于患者特征信息的手術(shù)室調(diào)度魯棒優(yōu)化方法
主講人：章宇
講座時(shí)間：2023年5月26日 14:00
騰訊會(huì)議：850 229 101
參加對(duì)象：電商與物流學(xué)院全體教師、研究生
主辦單位：電商與物流學(xué)院、研究生院
主講人簡(jiǎn)介：
章宇，西南財(cái)經(jīng)大學(xué)大數(shù)據(jù)研究院教授、博導(dǎo)。東北大學(xué)本科、直博，新加坡國(guó)立大學(xué)聯(lián)合培養(yǎng)博士。曾赴新加坡國(guó)立大學(xué)任研究員，并多次受邀訪問(wèn)。主要從事物流、供應(yīng)鏈、交通、醫(yī)療運(yùn)營(yíng)管理的魯棒優(yōu)化與決策研究。主持和參與國(guó)家自然科學(xué)基金項(xiàng)目3項(xiàng)。在Operations Research，Mathematical Programming，Production and Operations Management, INFORMS Journal on Computing等期刊發(fā)表學(xué)術(shù)論文10余篇。獲中國(guó)管理科學(xué)與工程學(xué)會(huì)優(yōu)秀博士學(xué)位論文獎(jiǎng)、Omega期刊最佳論文獎(jiǎng)，單篇論文入選ESI高被引論文。受邀擔(dān)任Operations Research，INFORMS Journal on Computing，Transportation Science等期刊審稿人，任中國(guó)運(yùn)籌學(xué)會(huì)決策科學(xué)分會(huì)理事。
主講內(nèi)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.