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研究生: 黃韜丞
Huang, Tao-Cheng
論文名稱: 地震災害最佳醫療及運輸資源佈署模型
Optimal Medical and Transportation Resource Allocation Model in Earthquakes
指導教授: 張國浩
Chang, Kuo-Hao
許鉅秉
Sheu, Jiuh-Biing
口試委員: 李香潔
Li, Hsiang-Chieh
陳彥銘
Chen, Yen-Ming
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: 74
中文關鍵詞: 人道物聯網地震災害兩階段數學模型分佈式穩健最佳化隨機最佳化
外文關鍵詞: Humanitarian Logistics, Earthquake Disaster, Two-Stage Model, Distributionally Robust Optimization, Stochastic Optimization
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  • 由於自然災害的隨機性,這篇描述了如何制定兩階段數學模型的過程,此模型旨在於災前優化醫療資源和運輸資源的佈署,以因應地震。考量到台灣的地理環境,地震是必會發生,而本文的目的為減少此災害造成的損傷和經濟成本,和建構了新的不確定集合來表示隨機性。這集合相較以前可以同時表示不同情境發生的可能性和其他隨機變數的範圍。
    本研究聚焦於災害的準備和反應階段,其中資源可以進一步劃分為兩種類型的資源:醫療資源,能直接緩解受難者的痛苦;運輸資源,雖然不能直接缓解,但能夠傳送其他資源至有需求的人們。為了展示情境的隨機性,本研究使用兩階段模型,而模型的第一階段對應到準備的緊急資源量,決定了將在第二階段能動用的資源。然後第二段是發生在地震後,所有未知的隨機變數都變成已知的確定變數,然後第決定是如何分配第一階段的資源,以最小化總經濟成本。
    模型定義完後,透過運用分佈式穩健最佳化的模型和提供的不確定集合還有分解演算法,計算最佳解。總之,兩階段模型可以同時考慮醫療資源和相較新的運輸資源的配置。


    In consideration of the stochasticity of natural disasters, we provide an in-depth description of formulating a two-stage mathematical model which optimizes both medical resources and transportation resource allocation in preparation for earthquakes. Given the geographic circumstances in Taiwan, the occurrence of earthquakes is almost certain, in which this paper aims prepare us for such event, and apply a novel method of expressing the randomness in ambiguity set, which contains the probabilities of different discrete events and the supports of random variables.
    The research focuses on the preparation and response phase of disaster management, of which can be further divided into two types of resources medical, able to alleviating the victims’ suffering, and transportation, unable to relieve the suffering by itself but is required for delivering the other resources to those in need. To present the randomness of the situation, a two-stage model is used. Stage One of the model corresponds to the preparation phase and determines the amount of resources which would be available. Stage Two is for after the earthquake strikes, thereby all random variables become certain and distribute resources prepared at stage one to minimize the total economic cost.
    With the model defined, the distributionally robust optimization (DRO) is utilized to gain for the optimal solution with the given the ambiguity set from a decomposition algorithm. To conclude, two-stage mathematical model can account for both medical resource allocation and the novel transportation resource allocation.

    摘要 I Abstract II Table of Contents III Table of Figures V Table of Tables VI Chapter One – Introduction 1 1.1 Background 1 1.2 Goals 4 1.3 Structure of Paper 7 Chapter Two – Literature Review 8 2.1 Humanitarian Logistics 8 2.2 Disaster Situation 10 2.2.1 Medical Demand 11 2.2.2 Transportation Challenges 12 2.3 Optimizing Randomness 15 2.3.1 Dynamic Programming 15 2.3.2 Stochastic Optimization 16 2.3.3 Distributionally Robust Optimization 16 Chapter Three – Mathematical Model 19 3.1 Problem Definition 19 3.2 As a Stochastic Optimization Model 23 3.2.1 Assumptions for Stochastic Optimization Model 24 3.2.2 Notations or Stochastic Optimization Model 24 3.2.3 Mathematical Model for Stochastic Optimization 27 3.3 As a Distributionally Robust Optimization Model 29 3.3.1 Assumptions for Distributionally Robust Optimization Model 30 3.3.2 Notations for Distributionally Robust Optimization Model 31 3.3.3 Mathematical Model for Distributionally Robust Optimization 33 3.3.4 Model Reformulations 38 3.3.5 Final Model 43 Chapter Four – Proposed Methodology 45 4.1 Simulation Optimization 45 4.1.1 Scenario Simulation 46 4.1.2 Heuristic Search 48 4.2 Decomposition Algorithm 48 Chapter Five – Case Study 50 5.1 Parameters for Tainan, Taiwan 50 5.1.1 Transportation Resources and Gathering Points 51 5.1.2 Medical Resources and Hospitals 52 5.1.3 Disaster Zones and Scenarios 53 5.1.4 All Input Values 54 5.2 Optimization Results 55 5.2.1 Analysis by Scenario 56 5.2.2 Sensitivity Analysis 59 5.3 Analysis of Results 64 Chapter Six – Conclusions 66 6.1 Contributions 66 6.2 Future Direction 66 References 68

    政府資料開放平臺. (n.d.). 臺南市醫療院所一般病床數. https://data.gov.tw/dataset/142996

    吳杰穎、江宜錦,2008,〈臺灣天然災害統計指標體系建構與分析〉,《地理學報》,51 期:頁65-84。

    Adida, E., DeLaurentis, P. C., & Lawley, M. A., (2011). Hospital stockpiling for disaster planning. IIE Transactions 43(5), 348-362.

    AFP. (2018). Taiwan developer detained over Deadly Quake Building collapse. SHINE. https://www.shine.cn/news/nation/1802281018/

    Ahmadi, M., Seifi, A., & Tootooni, B. (2015). A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district. Transportation Research Part E: Logistics and Transportation Review 75, 145-163

    Ahmed, S., & Shapiro, A. (2020). Distributionally robust stochastic programming models for resource allocation in disaster management. Operations Research, 68(4), 1039-1056.

    Alem, D., Clark, A., & Moreno, A. (2016). Stochastic network models for logistics planning in disaster relief. European Journal of Operational Research 255, 187-206.

    American Red Cross. (n.d.). Make a first aid kit. https://www.redcross.org/get-help/how-to-prepare-for-emergencies/anatomy-of-a-first-aid-kit.html

    Asian Disaster Reduction Center. (2022). Natural Disaster Databook 2022 An Analytical Overview. Asian Disaster Reduction Center (ADRC).

    Aydin, N. Y., Duzgun, H. S., Heinimann, H. R., Wenzel, F., & Gnyawali, K. R. (2018). Framework for improving the resilience and recovery of transportation networks under geohazard risks. International Journal of Disaster Risk Reduction, 31, 832–843.

    Aydin, N. Y., Duzgun, H. S., Wenzel, F., & Heinimann, H. R. (2017). Integration of
    stress testing with graph theory to assess the resilience of urban road networks under seismic hazards. Natural Hazards, 91(1), 37–68.

    Balal, E., Valdez, G., Miramontes, J., & Cheu, R. L. (2019). Comparative evaluation of measures for urban highway network resilience due to traffic incidents. International Journal of Transportation Science and Technology. 8(3), 304-317.

    Baird, M. E. (2010). The “Phases” of Emergency Management. Vanderbilt Center for Transportation Research, 15-19 & 28-32.

    Barry, A. & Anderson, K. (2021). Weather constraints on global drone flyability. Nature, 21, 91325. doi:10.1038/s41598-021-91325-w

    Ben-Tal, A., Chung, B. D., Mandala, S. R., & Yao, T. (2011). Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains. Transportation Research Part B: Methodological, 45(8), 1177–1189. doi:10.1016/j.trb.2010.09.002

    Ben-Tal, A., Chung, B. D., Mandala, S. R., & Yao, T. (2013). Robust optimization for emergency evacuation planning under uncertainty. Management Science, 59(10), 2347-2364.

    Bertsimas, D., Brown, D. B., & Caramanis, C. (2018). Robust and adaptive optimization approaches for shelter location and allocation in disaster management. Annals of Operations Research, 272(1), 15-34.

    Bozorgi-Amiri, A., & Khorsi, M. (2015). A dynamic multi-objective location–routing model for relief logistic planning under uncertainty on demand, travel time, and cost parameters. The International Journal of Advanced Manufacturing Technology, 85(5-8), 1633–1648.

    Chang, L., Peng, F., Ouyang, Y., Elnashai, A. S., & Spencer, B. F. (2012). Bridge
    Seismic Retrofit Program Planning to Maximize Postearthquake Transportation Network Capacity. Journal of Infrastructure Systems, 18(2), 75–88.

    Eom, H. B., & Lee, S. M., (1990). A survey of decision support system applications. Interfaces, 20(3), 65-79.

    Gory, G. A., & Scott Morton, M. S. (1971). A framework for management information systems. Sloan Management Review, 13(1), 55-70.

    Geis, Donald E. (2000). By Design: The Disaster Resistant and Quality-of-Life Community. Natural Hazards Review, 1(3), 151-160.

    Guha-Sapir, D., Hoyois, P., Wallemacq, P., & Below, R. (2017). Annual disaster statistical review 2016 the numbers and trends. The Centre for Research on the Epidemiology of Disasters (CRED).

    Haidari, L. A., Brown, S. T., Ferguson, M. (2024). Drone applications for emergency and urgent care: A systematic review. Prehospital and Disaster Medicine, 39(2), 123-130. doi:10.1017/S1049023X21000563

    Hu, S., Han, C., Dong, Z. S., & Meng, L. (2019). A multi-stage stochastic programming model for relief distribution considering the state of road network. Transportation Research Part B: Methodological, 123, 64–87.

    Karlaftis, M. G., Kepaptsoglou, K. L., & Lambropoulos, S. (2007). Fund Allocation for Transportation Network Recovery Following Natural Disasters. Journal of Urban Planning and Development, 133(1), 82–89.

    Klibi, W., Ichoua, S., & Martel, A. (2017). Prepositioning emergency supplies to support disaster relief: a case study using stochastic programming. INFOR: Information Systems and Operational Research, 56(1), 50–81.

    Konstantinidou, M. A., Kepaptsoglou, K. L., & Karlaftis, M. G. (2014). Transportation Network Post-Disaster Planning and Management: A Review Part I: Post-Disaster Transportation Network Performance. International Journal of Transportation Vol.2, No.3(2014), 1-16.

    Lettieri, Emanuele & Masella, Cristina & Radaelli, Giovanni. (2009). Disaster management: Findings from a systematic review. Disaster Prevention and Management. 18. 117-136. 10.1108/09653560910953207.

    Li, S., & Teo, K. L. (2018). Post-disaster multi-period road network repair: work scheduling and relief logistics optimization. Annals of Operations Research.

    Li, Y & Liu, Y. (2023) Distributionally robust optimization for collaborative emergency response network design. Transportation Research Part E: Logistics and Transportation Review 176 103221.

    Liu, Y., Raj, A., & Darzi, A. (2023). Effect of topography and weather on delivery of automatic external defibrillators by drones. Nature, 21, 10112. doi:10.1038/s41598-021-10112

    Marcianò, F. A., Musolino, G., & Vitetta, A. (2015). Signal setting optimization on urban road transport networks: The case of emergency evacuation. Safety Science, 72, 209–220.

    MCEER. (2006). MCEER'S Resilience Framework, MCEER (October 15, 2009).

    Mesar, T., Lessig, A. & King, D. (2018). Use of Drone Technology for Delivery of Medical Supplies During Prolonged Field Care. Journal of special operations medicine : a peer reviewed journal for SOF medical professionals. 18. 34-35.

    Mete, H. O. and Z. B. Zabinsky (2010). "Stochastic optimization of medical supply location and distribution in disaster management." International Journal of Production Economics 126(1): 76-84.

    Mileti, D. S. (1999). Disasters by Design: A Reassessment of Natural Hazards in the United States. Washington: Joseph Henry Press.

    Newman, J. P., Maier, H. R., van Delden, H., Zecchin, A. C., Dandy, G. C., Riddell, G., & Newland, C. (2014). Literature review on decision support systems for optimising long-term natural hazard mitigation policy and project portfolios. The University of Adelaide, Report, (2014.009), 13-14.

    Nikbakhsh, E., & Zanjirani Farahani, R. (2011). Humanitarian Logistics Planning in Disaster Relief Operations. Logistics Operations and Management, 291–332. doi:10.1016/b978-0-12-385202-1.00015-3

    Niyazi, M., & Behnamian, J. (2021). Dynamic Programming for Multi-Crew Scheduling of the Emergency Repair of Network. Journal of System Management 6(4), pp. 27-48

    Nezhadroshan, A. M., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2020). A scenario-based possibilistic-stochastic programming approach to address resilient humanitarian logistics considering travel time and resilience levels of facilities. International Journal of Systems Science: Operations & Logistics, 1–27.

    Oksuz, M. K., & Satoglu, S. I. (2019). A two-stage stochastic model for location planning of temporary medical centers for disaster response. International Journal of Disaster Risk Reduction, 101426.

    Oluwafemi, John & Ofuyatan, Olatokunbo & O.M.Sadiq, & Oyebisi, Solomon & Abolarin, John & Babaremu, Kunle. (2018). Review of world earthquakes. International Journal of Civil Engineering and Technology. 9. 440-464.

    Osumo, Chrisantos & Omwenga, Jane Queen. (2024). Humanitarian Logistics and Emergency Response in Humanitarian Organizations in Kenya.. International Journal of Social Science and Humanities Research (IJSSHR) ISSN 2959-7056 (o); 2959-7048 (p). 2. 237-246. 10.61108/ijsshr.v2i1.85.

    Paul, J. A. & MacDonald L. (2016). Location and capacity allocations decisions to mitigate the impacts of unexpected disasters. European Journal of Operational Research 251(1): 252-263.

    Paul, J. A. & Zhang, M. (2019). Supply location and transportation planning for hurricanes: A two-stage stochastic programming framework. European Journal of Operational Research 274(1): 108-125

    Rawls, C. G. & Turnquist, M. A. (2012). Pre-positioning and dynamic delivery planning for short-term response following a natural disaster. Socio-Economic Planning Sciences, 46, 46-54.

    Rezaeia, H., Zare, K., Bashirib, M., & Fakhrzada M. B. (2018). Re-configuration of the Relief Network Considering Uncertain Demand and Link Failure in an Earthquake: A Multi-stage Stochastic Programming. IJE Transection C: Aspects, 31, 6, 932-942

    Santoso, T. (2004). A stochastic programming approach for supply chain network design under uncertainty. Journal of Volcanology and Geothermal Research. doi:10.1016/s0377-2217(04)00229-2

    Sayarshad, H. R., Du, X., & Gao, H. O. (2020). Dynamic post-disaster debris clearance problem with re-positioning of clearance equipment items under partially observable information. Transportation Research Part B: Methodological, 138, 352–372.

    Shao, Z., Ma, Z., Liu S., & Lv T. (2018). Optimization of a Traffic Control Scheme for a Post-Disaster Urban Road Network. Sustainability 2018, 10(1), 68 Xzhoa

    Shao, J., Wang, X., Liang, C., & Holguín-Veras, J. (2019). Research progress on deprivation costs in humanitarian logistics. International Journal of Disaster Risk Reduction, 101343. doi:10.1016/j.ijdrr.2019.101343

    Shehadeh, K. S. & Tucker, E. L. (2022) Stochastic optimization models for location and inventory prepositioning of disaster relief supplies. Transportation Research Part C: Emerging Technologies 144 103871

    Sheu, J.-B. (2007). Microscopic modeling and control logic for incident-responsive automatic vehicle movements in single-automated-lane highway systems. European Journal of Operational Research, 182(2), 640–662. doi:10.1016/j.ejor.2006.08.053

    Speranza, M.Grazia & Archetti, C.. (2014). A survey on matheuristics for routing problems. EURO Journal on Computational Optimization. 2. 10.1007/s13675-014-0030-7.

    The World Bank (2005). Natural Disaster Hotspots. A Global Risk Analysis.

    Tsia. (2016). Over 100 missing, 14 dead as strong quake rattles Taiwan. The Associated Press. https://phys.org/news/2016-02-dead-strong-quake-rattles-taiwan.html

    Vitoriano, Begoña & Rodríguez, J. & Tirado, Gregorio & Martín-Campo, F. Javier & Ortuño, M.Teresa & Montero, Javier. (2015). Intelligent Decision-Making Models for Disaster Management. Human and Ecological Risk Assessment: An International Journal. 21. 1341-1360. 10.1080/10807039.2014.957947.

    Wang, D., Yang, K., Yang, L., & Dong, J. (2023). Two-stage distributionally robust optimization for disaster relief logistics under option contract and demand ambiguity. Transportation Research Part E: Logistics and Transportation Review 170. 103025

    Wang, Q., & Nie, X. (2019). A Stochastic Programming Model for Emergency Supply Planning Considering Traffic Congestion. IISE Transactions.

    Wang, Y., & Zhou, E. (2023). Data-driven optimal computing budget allocation under input uncertainty. arXiv. https://arxiv.org/abs/2209.11809

    Wu, Y., Hou, G., & Chen, S. (2021). Post-earthquake resilience assessment and long-term restoration prioritization of transportation network. Reliability Engineering & System Safety, 211, 107612. doi:10.1016/j.ress.2021.107612

    Xu, X., Qi, Y., & Hua, Z. (2010). Forecasting demand of commodities after natural disasters. Expert Systems with Applications 37(6): 4313-4317

    Yu, L., Zhang, C., Yang, H., & Miao, L. (2018). Novel methods for resource allocation in humanitarian logistics considering human suffering, Computers & Industrial Engineering, Volume 119, Pages 1-20, ISSN 0360-8352,

    Zhang, N., & Alipour, A. (2020). Two-Stage Model for Optimized Mitigation and Recovery of Bridge Network with Final Goal of Resilience. Transportation Research Record, 2674(10), 114-123. https://doi.org/10.1177/0361198120935450

    Zhao, L., Xiao, Y., & Wang, X. (2021). Distributionally robust optimization for emergency logistics network design under uncertainty. Journal of Humanitarian Logistics and Supply Chain Management, 11(2), 213-231.

    Zhou, Y., Sheu, J.-B., & Wang, J. (2017). Robustness Assessment of Urban Road Network with Consideration of Multiple Hazard Events. Risk Analysis, 37(8), 1477–1494.

    Zhou, Y., Wang, J., & Yang, H. (2019). Resilience of Transportation Systems: Concepts and Comprehensive Review. IEEE Transactions on Intelligent Transportation Systems, 1–15. doi:10.1109/tits.2018.2883766

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