「急性缺血性中風」為一種由血管阻塞引起的嚴重神經系統疾病，導致大腦缺氧，並可能造成失語、癱瘓甚至死亡等嚴重後果。在醫學領域，這種情況已成為一門重要且值得注意的健康議題。由於中風的嚴重後果以及病患於綜合中風中心（CSC）之等待時間的高度變異性，對於決策者來說，選擇將病患派送往哪間中風中心（CSC）是一個棘手的問題。本篇論文探討了綜合中風中心 (CSC) 之選擇問題，即決定將病人送往哪間中風中心。此決策的目標是最小化時間之總和，包括了運輸時間以及病患在中風中心的等待時間。基於真實數據，我們建構了等待時間之分布模糊集，其中包含平均值、平均絕對偏差和支撐域。這些敘述性統計數據是由中風中心的記錄中估計而得。
在本篇論文中，我們協助風險規避的決策者建構一個分佈穩健模型，該模型根據模糊集中最壞情況之機率分佈來評估等待時間。由於此模型在數學上難以直接求解，我們使用對偶定理將該模型轉換為一個混整數線性規劃模型以便求解。除此之外，實驗數據表明，我們的模型於最壞情況以及上十分位數的表現優於現有的隨機規劃。
關鍵字：分佈穩健優化，中風醫療，風險規避，模糊集

"Acute ischemic stroke" is a severe neurological disorder caused by vascular obstruction, leading to cerebral hypoxia and potentially resulting in aphasia, paralysis, or even death. In the medical field, this condition has become a significant and noteworthy health issue. Due to the serious consequences of stroke and the high variability of waiting time in comprehensive stroke center (CSC), it is a tough question for the decision-maker to decide which CSC to send the stroke patient to. This paper considers a comprehensive stroke center (CSC) selection (CSCS) problem, determining which CSC the patient should be sent to. The solution is made by minimizing the total time, including both transportation time and waiting time in CSC. According to real-life waiting time’s data, we construct an ambiguity set of distributions, that includes mean values, mean absolute deviations, and a support set. These descriptive statistics can be estimated from CSC experience data. To sum up, we assist risk-averse managers in developing a distributionally robust model for the CSCS problem, which the waiting times are evaluated according to the worst-case probability distribution in the ambiguity set. Owing to its complex calculation, we reformulated the problem as a mixed integer linear programming model by duality theorem. In additional, computational experiments demonstrate that our model outperforms existing stochastic programming in both worst-case and upper-decile performance.
Keywords: Distributionally Robust Optimization, Stroke Care, Risk-averse, Ambiguity Set

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