考慮COVID-19患者嚴重程度迅速變化的運輸車輛指派和醫院選擇模型：基於院前分流

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研究生：	蔡寅傑 Tsai, Yin-Chieh
論文名稱：	考慮COVID-19患者嚴重程度迅速變化的運輸車輛指派和醫院選擇模型：基於院前分流 Transport Vehicles Assignment and Hospitals Selection for COVID-19 Patients Considering Rapid Change of Severity: A Pre-hospital Triage Model
指導教授：	李雨青 Lee, Yu-Ching
口試委員:	郭佳瑋 Kuo, Chia-Wei 吳浩庠 Wu, Hao-Hsiang
學位類別：	碩士 Master
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
論文出版年：	2024
畢業學年度：	112
語文別：	英文
論文頁數：	95
中文關鍵詞：	新冠肺炎、院前分流、醫院選擇、車輛指派、二元整數規劃、最佳化模型
外文關鍵詞：	coronavirus disease, pre-hospital triage, hospital selection, vehicle assignment, binary integer programming, optimization model
相關次數：	點閱：72 下載：2
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查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

在2022年，台灣面臨新型冠狀病毒（COVID-19）的衝擊，對整個醫療體系造成極大的壓力。起初衛生福利部使用人工指派救護車和防疫計程車，將確診新型冠狀病毒患者運送到防疫旅館或醫院進行隔離或治療。然而，至四月起每日通報的確診病患數量急速增加，人工指派變得難以應付且不切實際。
為了解決人工指派無法應付的難題，本研究提出了一個車輛指派和醫院選擇的最佳化模型，旨在透過數學模型分配運輸車輛和目的地給通報衛生福利部之確診新型冠狀病毒患者，並在送醫前估計患者可能的病情嚴重程度之機率來分流患者，以考慮患者可能需要轉院至更高階的醫院情況等。該模型的目標包括：(一) 最小化特定時段內所有確診患者得到確切治療的預期時間。(二) 最大化能夠在特定時間內得到確切治療的患者人數。我們分別對台北市和屏東縣的實際數據透過全因子實驗進行了模型可行性驗證，同時透過調整參數以解決不同環境情景下的結果，分析驗證了該模型在不同情景下的有效性。
本研究的實驗結果可以作為衛生福利部和其他相關機構進行醫療車輛資源規劃和目的地選擇的參考依據。

In 2022, Taiwan faced the impact of the novel coronavirus 2019 (COVID-19), placing immense pressure on the entire healthcare system. Initially, the Ministry of Health and Welfare employed manual assignment of ambulances and epidemic-prevention taxis to transport confirmed COVID-19 patients to epidemic-prevention hotels or hospitals for isolation or treatment. However, by April, the daily reported number of confirmed cases surged, making manual assignment difficult to manage and impractical.
To address the challenges posed by manual assignment, this study proposes an optimization model for vehicle assignment and hospital selection. The aim is to allocate transportation vehicles and destinations to COVID-19 patients reported to the Ministry of Health and Welfare through a mathematical model. Additionally, the model estimates the probabilities of patients' severity before transport to facilitate patient triage, considering the possibility of patients needing to be transferred to higher-level hospitals. The objectives of the model include: (1) Minimizing the expected time for all confirmed patients to receive definitive treatment within a specific time period. (2) Maximizing the number of patients who can receive definitive treatment within a specific time frame. We conducted full factorial experiments using actual data from Taipei City and Pingtung County to validate the feasibility of the model. Additionally, we analyzed the effectiveness of the model under different scenarios by adjusting parameters.
The experimental results of this study can serve as a reference for the Ministry of Health and Welfare and other relevant agencies in planning healthcare vehicle resources and selecting destinations.

中文摘要……………………………………………………………………………i
Abstract………………………………………………………………………………ii
Contents ……………………………………………………………………………iii
Chapter 1 Introduction ………………………………………………………………1
Chapter 2 Literature Review………………………………………………………7
2.1 Prediction of Covid-19 Patient’s Severity…………………………………7
2.2 Operation Research Models for Patients Triage…………………………8
2.3 Emergency Vehicle Assignment Models..………………………………10
Chapter 3 Methodology……………………………………………………………12
3.1 Assumptions of The Model………………………………………………15
3.2 Descriptions of The Model…………………………………………………17
3.2.1 Fundamental Constraints…………………………………………19
3.2.2 Objectives and Constraints of Considering All Scenarios with the Emergency Time Window T……………………………………………21
3.2.3 Objectives and Constraints of Considering Specific Scenario with the Specific Scenario Time Window t…………………………………29
3.3 Overall Binary Integer Programming Model……………………………35
Chapter 4 Numerical Experiments………………………………………………39
4.1 Data Synthetization and Collection………………………………………39
4.1.1 Patient’s Data………………………………………………………39
4.1.2 Transport Data……………………………………………………42
4.1.3 Destination Data…………………………………………………44
4.2 User-control Parameters Setting …………………………………………44
4.3 Full Factorial Design of Experiments……………………………………47
4.3.1 Design of Experiments……………………………………………48
4.3.2 Experiment Results………………………………………………50
4.3.3 Effect of Factors Analysis…………………………………………51
4.3.3.1 Response Y_1: Expected Number of Patients Receiving Definitive Treatment or Securing a Room for Isolation…………51
4.3.3.2 Response Y_2: Expected Time for All Patients……………54
4.3.3.3 Response Y_3: Overall Objective of The Model……………57
4.4 Effectiveness of Adjustment Weight (ε)…………………………………60
Chapter 5 Conclusion and Future Work…………………………………………64
Appendix A.1……………………………………………………………….………67
Appendix A.2….……………………………………………………………………77
Appendix B.1………………………………………………………………………87
Appendix B.2.………………………………………………………………………89
Appendix B.3.………………………………………………………………………91
References …………………………………………………………………………93

                                

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