本研究論文旨在開發一種優化不確定需求下資源配置的穩健框架,專注於應對重大突發公共衛生事件期間醫療用品隨機需求的調度問題。採用蒙特卡羅決策樹演算法模型,結合需求不確定性建模、成本預期建模,並引入時間窗約束,實施蒙特卡羅樹搜索中的貪婪策略以增強決策過程,從而優化不確定條件下的資源調度方案。在模型構建方面,以武漢20家定點醫院作為研究案例,模擬了新冠肺炎疫情期間醫療物資的隨機需求情況。基於帶時間窗約束的隨機需求車輛路徑問題,建立了將配送成本和時間成本相結合的目標函數,並引入"提前補貨"策略以避免路徑失效,從而減少因治療點需求大於車輛剩餘物資而產生的額外補貨成本。在數值實驗環節,利用MATLAB軟體對演算法進行編程實現,基於構建的武漢地區案例對模型及演算法進行了驗證。實驗結果顯示，相較於傳統方法，本文提出的結合演算法能夠在處理緊急情況下的資源分配問題時取得更優的表現，特別是在時間窗約束下的隨機需求場景中，顯著提升了醫療物資的調度效率和供應及時性。該研究為應對緊急情況下醫療用品管理提供了結構化的方法論,不僅豐富了該領域的理論基礎,更為面臨隨機需求挑戰的醫療保健系統提供了可增強其準備和回應能力的科學依據及決策支持。

This research paper aims to develop a robust framework for optimizing resource allocation under uncertain demand, focusing on the scheduling problem of stochastic demand for medical supplies during major public health emergencies. A Monte Carlo decision tree algorithm model is adopted, combining demand uncertainty modeling and expected cost modeling, and introducing time window constraints. A greedy strategy is implemented in the Monte Carlo tree search to enhance the decision-making process, thereby optimizing resource scheduling schemes under uncertain conditions.In terms of model construction, 20 designated hospitals in Wuhan are used as a case study, simulating the stochastic demand for medical supplies during the COVID-19 pandemic. Based on the vehicle routing problem with stochastic demand and time window constraints, an objective function combining delivery cost and time cost is established, and an "early replenishment" strategy is introduced to avoid route failure, thereby reducing additional replenishment costs caused by the treatment point's demand exceeding the vehicle's remaining supplies.In the numerical experiment section, the algorithm is programmed and implemented using MATLAB software, and the model and algorithm are validated based on the constructed Wuhan case study. The experimental results show that, compared to traditional methods, the combined algorithm proposed in this paper can achieve better performance in handling resource allocation problems under emergency situations. Specifically, in stochastic demand scenarios with time window constraints, it significantly improves the scheduling efficiency and timeliness of medical supplies.This study provides a structured methodology for managing medical supplies in emergency situations, enriching the theoretical foundation in this field. It also offers scientific basis and decision support for healthcare systems facing stochastic demand challenges, enhancing their preparedness and response capabilities.

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