當災害發生時，例如地震，常會發生各地區對許多物資有不同需求程度及交通網路失效的問題。交通網路失效常造成交通阻塞，使運輸的旅行時間增長，而當物資運送時間越長，民眾焦慮感也愈高，也愈容易引起民怨，因此如何有效進行災後物資分配，成為災後應變階段的重要議題。
本研究針對現行的災後物資運輸模式進行改善，在考量災後物資運輸網路設計與交通網路失效程度下，建構一個地區收容中心相互支援的物資配送架構，可以減少地區收容中心之間的滿足程度落差外，也能降低災後物資分配的總反應時間，以達到公平分配及提升災後運輸作業效率的目的，並利用調整後的樣本平均近似法求得最佳運輸指派決策。最後針對不同的影響因子，進行兩種二水準部份因子實驗設計，提供決策者在不同災後情況下使用不同物資配送架構的考量方向，以利決策者進行災後物資運輸管理。

When disasters occur, such as earthquakes, different regions have different levels of demand for materials, and transportation network is failed due to the disaster. The failure of the transportation network often causes traffic congestion and increases the travel time of transportation. The longer the material delivery time, the higher the public anxiety, and the more likely it is to cause public grievances. Therefore, how to distribute materials effectively is an important topic for post-disaster response phase.
This research aims to improve the current post-disaster material transportation mode by taking into consideration of the post-disaster material transportation network design and the degree of failure of the transportation network, and constructing a material distribution structure that local relief centers could support each other. The structure could reduce the satisfaction gap between local relief centers, also reduce the total response time of the distribution assignment. The modified sample average approximation is used to find the best multi-material distribution assignment decision. Finally, two two-level fractional factorial designs are carried out with considering different factors. Provide decision-makers with consideration directions for using different material distribution structures in different post-disaster situations, so as to facilitate decision-makers in post-disaster material transportation management.

摘要 I
Abstract II
目錄 III
圖目錄 V
表目錄 VI
第一章　緒論 1
1.1研究背景與動機 1
1.2研究目的 2
1.3論文架構 5
第二章　文獻回顧 6
2.1人道主義後勤 6
2.2災後交通運輸問題 8
2.3旅行時間估計 11
2.4模擬最佳化 13
第三章　數學模型 17
3.1問題定義 17
3.2符號定義 18
3.3數學模型 19
第四章　研究方法 24
4.1物資運輸網路設計 24
4.2運輸網路初始化 27
4.3演算法流程 30
第五章　數值實驗 35
5.1測試網路 35
5.2數值設定 37
5.3數值測試 42
第六章　參數分析 47
6.1二水準部分因子實驗設計1 47
6.2二水準部分因子實驗設計2 56
第七章　結論與未來研究 61
參考文獻 63

李洋寧、劉淑燕、李沁妍、吳佳容、鄧敏政、柯孝勳、李中生（2014）。大臺北地區大規模地震衝擊情境分析報告II ：道路系統、水電設施、重要設施、情境綜整。NCDR 102-T15。國家災害防救科技中心。
陳禮仁（2020）。近年世界重大天然災害的回顧與省思。土木水利，47(2)，57–70。DOI: 10.6653/MoCICHE.202004_47(2).0010
Amaran, S., Sahinidis, N. V., Sharda, B., & Bury, S. J. (2016). Simulation optimization: a review of algorithms and applications. Annals of Operations Research, 240(1), 351–380.
Ammeri, A., Hachicha, W., Chabchoub, H., & Masmoudi, F. (2011). A comprehensive literature review of mono-objective simulation optimization methods. Advances in Production Engineering & Management, 6(4), 291–302.
Archetti, C., & Speranza, M. G. (2014). A survey on matheuristics for routing problems. EURO J Comput Optim, 2, 223–246.
Ballou, R. H. (2004). Business Logistics/supply Chain Management: Planning, Organizing, and Controlling the Supply Chain. Upper Saddle River, NJ: Pearson/Prentice Hall. ISBN: 9780130661845
Bayram, V. (2016). Optimization models for large scale network evacuation planning and management: A literature review. Surveys in Operations Research and Management Science, 21, 63–84.
Bell, W. J., Dalberto, L. M., Fisher, M. L., Greenfield, A. J., Jaikumar, R., Kedia, P., Mack, R., G., & Prutzman, P. J. (1983). Improving the distribution of industrial gases with an on-line computerized routing and scheduling optimizer. Interfaces, 13(6), 4–23.
Benson, M., Koenig, K. L., Schultz, C.H. (1996). Disaster triage: START, then SAVE—A new method of dynamic triage for victims of a catastrophic earthquake. Prehosp. Disa. Med. 11(2), 117–124.
Bozorgi-Amiri, A., Jabalameli, M. S., & Mirzapour Al-e-Hashem, S.M.J. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectrum, 35, 905–933.
BPR, U.S. (1964). Traffic assignment manual for application with a large, high speed computer. U.S. Department of Commerce, Bureau of Public Roads, Office of Planning, Urban Planning Division.
Burghout, W., Koutsopoulos, H. N., & Andreasson, I. (2006). A discrete-event mesoscopic traffic simulation model for hybrid traffic simulation. 2006 IEEE Intelligent Transportation Systems Conference, Toronto, Canada, 1102–1107.
Cavdaroglu, B., Hammel, E., Mitchell, J. E., Sharkey, T. C., & Wallace, W. A. (2013). Integrating restoration and scheduling decisions for disrupted interdependent infrastructure systems. Annals of Operations Research, 203, 279–294.
Çelik, M. (2016). Network restoration and recovery in humanitarian operations: Framework, literature review, and research directions. Surveys in Operations Research and Management Science, 21(2), 47–61.
Chapman, A. G., & Mitchell, J. E. (2018). A fair division approach to humanitarian logistics inspired by conditional value-at-risk. Annals of Operations Research, 262 (1), 133–151.
Coelho, L. C., Cordeau, J.-F., & Laporte, G. (2014). Thirty Years of Inventory Routing. Transportation Science, 48(1),1–19.
Dahiya, G., Asakura, Y., & Nakanishi, W. (2020). A study of speed-density functional relations for varying spatiotemporal resolution using Zen Traffic Data. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 1–8.
Dantzig, G. B., & Ramser, J.H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91.
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269–271.
Drexl, M., & Schneider, M. (2015). A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, 241(2), 283–308.
Du, L., & Peeta, S. (2014). A Stochastic Optimization Model to Reduce Expected Post-Disaster Response Time Through Pre-Disaster Investment Decisions. Networks and Spatial Economics, 14, 271–295.
Falasca, M., & Zobel, C. W. (2011). A two-stage procurement model for humanitarian relief supply chains. J. Humanitarian Logist. Supply Chain Manag, 1(2), 151–169.
Farahani, R. Z., Lotfi, M. M., Baghaian, A., Ruiz, R., & Rezapour, S. (2020). Mass casualty management in disaster scene: A systematic review of OR&MS research in humanitarian operations. European Journal of Operational Research, 287, 787–819.
FMEA, U.S. (2020). Hazus Earthquake Model Technical Manual Hazus 4.2 SP3. Washington, D.C. Retrieved from https://www.fema.gov/sites/default/files/2020-10/fema_hazus_earthquake_technical_manual_4-2.pdf
Fu, M. C. (2002). Optimization for simulation: Theory vs. practice. INFORMS Journal on Computing, 14(3), 192–215.
Fu, M. C., Glover, F. W., & April, J. (2005, December). Simulation Optimization: A review, new developments, and applications. Proceedings of the 2005 winter simulation conference, Orlando, FL, 83–95.
Gaddam, H. K., & Rao, K. R. (2019). Speed-density functional relationship for heterogeneous traffic data: a statistical and theoretical investigation. Journal of Modern Transportation, 27(1), 61–74.
Gao, X., & Cao, C. (2020). Multi-commodity rebalancing and transportation planning considering traffic congestion and uncertainties in disaster response. Computers & Industrial Engineering, 149, 106782.
Grass, E. & Fischer, K. (2016). Two-stage stochastic programming in disaster management: A literature survey. Surveys in Operations Research and Management Science, 21, 85–100.
Greenshields, B. D., Channing, W. S., & Miller H. H. (1935). A study of traffic capacity. Highway Research Board, 14, 448–477.
Holguín-Veras, J., P´erez, N., Jaller, M., Van Wassenhove, L. N., & Aros-Vera, F. (2013). On the appropriate objective function for post-disaster humanitarian logistics models. Journal of Operations Management, 31(5), 262–280.
Jayakrishnan, R., Mahmassani, H. S., & Hu, T. Y. (1994). An evaluation tool for advanced traffic information and management systems in urban networks. Transportation Research Part C: Emerging Technologies, 3(2), 129–147.
Jotshi, A., Gong, Q., & Batta, R. (2009). Dispatching and routing of emergency vehicles in disaster mitigation using data fusion. Socio-Economic Planning Sciences, 43(1), 1–24.
Kleywegt, A. J., Shapiro, A., & Homem-de-Mello, T. (2001). The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization, 12(2), 479–502.
Klibi, W., Ichoua, S., & Martel, A. (2018). Prepositioning emergency supplies to support disaster relief: a case study using stochastic programming. INFOR: Information Systems and Operational Research, 56(1), 50–81.
Liu, M., Liu, X., Chu, F., Zheng, F., & Chu, C. (2019). Distributionally robust inventory routing problem to maximize the service level under limited budget. Transportation Research Part E: Logistics and Transportation Review, 126, 190–211.
Liu, Y., Cui, N., & Zhang, J. (2019). Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service. Transportation Research Part E: Logistics and Transportation Review, 128, 1–16.
Long, Y., Lee, L. H., & Chew, E. P. (2012). The sample average approximation method for empty container repositioning with uncertainties. European Journal of Operational Research, 222, 65–75.
Lu, C.-C., Ying, K.-C., & Chen, H.-J. (2016). Real-time relief distribution in the aftermath of disasters – A rolling horizon approach. Transportation Research Part E: Logistics and Transportation Review, 93, 1–20.
May A. D. Jr., & Keller H. E. M. (1967). Non-integer car-following models. Highway Research Record, 199, 19–32.
McLoughlin, D. (1985). A framework for integrated emergency management. Public Administration Review, 45(1), 165–172.
Miller-Hooks, E., Zhang, X., & Faturechi, R. (2012). Measuring and maximizing resilience of freight transportation networks. Computers & Operations Research, 39(7), 1633–1643.
Moshref-Javadi, M., & Lee, S. (2016). The customer-centric, multi-commodity vehicle routing problem with split delivery. Expert Systems with Applications, 56, 335–348.
Najafi, M., Eshghi, K., & Leeuw, S. D. (2014). A dynamic dispatching and routing model to plan/ re-plan logistics activities in response to an earthquake. OR Spectrum, 36, 323–356.
Noyan, N., Balcik, B., & Atakan, S. (2016). A stochastic optimization model for designing last mile relief networks. Transportation Science, 50(3), 1092–1113.
Özdamar, L., Aksu, D. T., & Ergüneş, B. (2014). Coordinating debris cleanup operations in post disaster road networks. Socio-Economic Planning Sciences, 48(4), 249–262.
Peeta, S., Salman, F. S., Günneç, D., & Viswanath, K. (2010). Pre-disaster investment decisions for strengthening a highway network. Computers & Operations Research, 37, 1708–1719.
Prodhon, C., & Prins, C. (2014). A survey of recent research on location-routing problems. European Journal of Operational Research, 238(1), 1–17.
Rios, L. M., & Sahinidis, N. V. (2013). Derivative-free optimization: A review of algorithms and comparison of software implementations. Journal of Global Optimization, 56, 1247–1293.
Sabbaghtorkan, M., Batta, R., & He, Q. (2020). Prepositioning of assets and supplies in disaster operations management: Review and research gap identification. European Journal of Operational Research, 284, 1–19.
Sahebjamnia, N., Torabi, S. A., & Mansouri, S. A. (2017). A hybrid decision support system for managing humanitarian relief chains. Decision Support Systems, 95, 12–26.
Sakiani, R., Seifi, A., & Khorshiddoust., R. R. (2020). Inventory routing and dynamic redistribution of relief goods in post-disaster operations. Computers & Industrial Engineering, 140, 106219.
Santoso, T., Ahmed, S., Goetschalckx, M., & Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167, 96–115.
Shahparvari, S., & Abbasi, B. (2017). Robust stochastic vehicle routing and scheduling for bushfire emergency evacuation: An Australian case study. Transportation Research Part A, 104, 32–49.
Shapiro, A. (1991). Asymptotic analysis of stochastic programs. Annals of Operation Research, 19, 761–773.
Shapiro, R., & Heskett, J. (1985). Logistics strategy: cases and concepts. Minnesota: West Pub. Co. ISBN: 9780314852977
Sheu, J.-B. (2007). An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transportation Research Part E: Logistics and Transportation Review, 43(6), 687–709.
Verweij, B., & Shapiro, A. (2003). The sample average approximation method for applied to stochastic routing problems. Computational Optimization and Applications, 24, 289–333.
Wang, L. (2020). A two-stage stochastic programming framework for evacuation planning in disaster responses. Computers & Industrial Engineering, 145, 106458.
Weisbrod, G., Vary, D., & Treyz, G. (2001). Economic implications of congestion. NCHRP Report, 463.
Yan, S., Lin, C.-K., & Chen, S.-Y. (2014). Logistical support scheduling under stochastic travel times given an emergency repair work schedule. Computers & Industrial Engineering, 67, 20–35.
Yen, J. Y. (1971). Finding the K shortest loopless paths in a network, Management Science, 17, 712–716.
Yi, W., Özdamar, L. (2007). A dynamic logistics coordination model for evacuation and support in disaster response activities. European Journal of Operational Research, 179(3), 1177–1193.
Yücel, E., Salman, F., & Arsik, I. (2018). Improving post-disaster road network accessibility by strengthening links against failures. European Journal of Operational Research, 269, 406–422.
Zhang, Y., Qi, M., Lin, W.-H., & Miao, L., (2015). A metaheuristic approach to the reliable location routing problem under disruptions. Transportation Research Part E: Logistics and Transportation Review, 83, 90–110.
Zhong, S., Cheng, R., Jiang, Y., Wang, Z., Larsen, A., & Nielsen, O. A. (2020). Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand. Transportation Research Part E: Logistics and Transportation Review, 141, 102015.
Zhou, Y., Liu, J., Zhang, Y., & Gan, X. (2017). A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems. Transportation Research Part E: Logistics and Transportation Review, 99, 77–95.