Speaker：章宇 教授 西南财经大学

Date：Oct.13 2022 10:00

Tencent meeting：133 458 836

PW：461434

Abstract:

Patient features such as gender, age and underlying disease are crucial to improving model fidelity of surgery durations. In this paper, we study a robust surgery scheduling problem augmented by patient feature segmentation. We consider particularly the surgery-to-OR allocations for elective patients and future emergency patients. 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 to mitigate both the magnitude and probability of overtime. The model can be reformulated as a second-order cone optimization problem. From the reformulation, we elucidate that minimizing the overtime riskiness index is equivalent to minimizing a Fano factor. This renders our robust optimization model easily interpretable to healthcare practitioners. To efficiently solve the second-order cone optimization problem, we develop a branch-and-cut algorithm. Numerical experiments demonstrate that our model outperforms benchmark models in a variety of performance metric.

Bio:

章宇，西南财经大学教授、博导。东北大学本科、直博，新加坡国立大学联合培养博士、博后，并多次受邀访问。主要从事物流、供应链、交通、医疗服务运营管理中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 Research，Mathematical Programming，INFORMS Journal on Computing等期刊发表学术论文10余篇。获2019年管理科学与工程学会优秀博士学位论文奖，获2020年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 Research，Transportation Science，SIAM Journal on Optimization 等期刊匿名审稿人，担任中国运筹学会决策科学分会理事。