博碩士論文 106322078 詳細資訊




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姓名 陳昇(Sean Chen)  查詢紙本館藏   畢業系所 土木工程學系
論文名稱 運輸系統於時空中運行之脆弱度分析
(Network vulnerability analysis in time-space dimension)
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摘要(中) 以往在學術界研究之網路問題以空間維度的探討居多,且針對網路之節線與節點作為主要研究對象,模型分析層面之參數變化調整以敏感度分析為主。有鑑於此,本研究導入時間維度概念,拓展網路問題,將空間面加上時間面,利用時空網路流動技巧建立最佳化模型,並研發出脆弱度分析,考量現實世界之時間維度存在價值,以運輸領域為例,運輸網路擁有尖離峰特性,並將分析對象從原本空間面的節線與節點,延伸到時空面的整體運輸系統,包含路線(arc)、場站(node)與運具(component)等等,不只是參數權重上的放大縮小,還可將某特定時點下所設計之節點或節線關閉,以系統最佳化之觀點得到運輸系統於不同時空下所產生的變化,使分析上能更精準的符合實務現況且在未來提供更佳的決策。
本研究研發脆弱度分析,並引用Yan & Chen (2002)之模式,其從長途客運業者立場,設計一整合旅次需求和班次供給之車隊排程模式,運用多重時空網路流動技巧建立模式,以定式旅客與車輛於時空中流動之情況,透過系統最佳化求得最佳之車隊排程與班次表以服務旅客。在求解方法上,不同於前研究以拉氏演算法求解,本研究利用C++程式語言配合數學規劃軟體CPLEX進行模式求解。最後為評估模式的實用績效,以國內一長途客運公司之營運資料進行實例測試,並針對不同變數與參數進行脆弱度分析,探討不同時空下,使用者所在乎之節線與節點對系統最佳化結果所產生的影響。
摘要(英) Back in the days, the network problems were mostly discussed in the spatial dimension academically, and the main research objects were arcs and nodes. The parameter adjustment of the model was mainly based on sensitivity analysis. In view of this, this study introduces the concept of time-space dimension, expands the network problem, uses the time-space network flow technique to establish an optimization model, and develops vulnerability analysis. Considering the value of the real-world, such as the peak moment characteristics of the transportation field, this study extends the research objects from arcs and nodes to the transportation system, including the routes (arcs) and stations (nodes) and cars (components). It is not only the change of the parameter weights, but also makes nodes or arcs can be closed which designed at a certain time point, and the changes of the transportation system in different time and space can be obtained from the viewpoint of system optimization, so that the analysis can be more accurate and provide better decisions for users in the future.
This study develops vulnerability analysis and refers to Yan & Chen (2002), a scheduling model and a solution algorithm for inter-city bus carriers. From the standpoint of inter-city bus company, it designs a fleet scheduling model that integrates passenger trip demands and bus trip supplies, and uses multiple time-space network flow techniques to establish a model to optimize in time-space dimension, and get the best fleet schedule to serve passengers. About solution method, different from the previous research, the Lagrangian algorithm is used to solve the problem, this study uses the C++ programming language and the mathematical programming software CPLEX to solve the model. Finally, for the practical performance to evaluate model, the case study is regarding the operation data of inter-city bus company in Taiwan, and the vulnerability analysis is carried out for different parameters and variables to explore the influence of the changes in different time and space on the system optimization results.
關鍵字(中) ★ 時間
★ 時空
★ 網路問題
★ 敏感度分析
★ 脆弱度分析
★ 多重時空網路
關鍵字(英) ★ Time
★ Time-sapce
★ Network problems
★ Sensitivity analysis
★ Vulnerability analysis
★ Time-space network
論文目次 摘 要 I
ABSTRACT II
誌 謝 III
目 錄 IV
圖目錄 VI
表目錄 VII
第一章、 緒論 1
1.1 研究背景與動機 1
1.2 研究目的與範圍 2
1.3 研究方法與流程 2
第二章、 文獻回顧 4
2.1 脆弱度定義與探討 4
2.2 敏感度分析介紹與應用 8
2.3 時空網路 8
2.4 文獻評析 11
第三章、 脆弱度理論 12
3.1 定義 12
3.2 應用領域 13
3.3 網路模式應用 14
3.4 小結 15
第四章、 測試範例 16
4.1 脆弱度理論於運輸網路之應用 16
4.2 模式架構 16
4.2.1 模式基本假設 16
4.2.2 車流與人流時空網路 18
4.2.3 符號說明 22
4.2.4 數學定式 22
4.3 模式構建 23
4.4 模式求解 24
4.5 小結 25
第五章、 脆弱度實例分析 26
5.1 脆弱度理論於長途客運之應用 26
5.2 實例資料分析與假設 26
5.2.1 場站與路線資料 28
5.2.2 車種資料 28
5.2.3 場站間行駛時間資料 29
5.2.4 起迄旅客需求量資料 30
5.2.5 成本資料 30
5.2.6 票價資料 31
5.3 實例輸入模式使用 31
5.3.1 問題規模 31
5.3.2 電腦演算環境 32
5.3.3 模式輸入資料 33
5.3.4 模式輸出資料 33
5.4 脆弱度分析 34
5.4.1 節線分析 34
5.4.2 節點分析 39
5.5 小結 41
第六章、 結論與建議 42
6.1 結論 42
6.2 建議 43
參考文獻 44

參考文獻 [1] 林士鈞. (2006). 定期貨櫃運輸船舶排程暨船期表建立之研究. 中央大學土木工程學系學位論文, 1-99.
[2] 郭宗其. (2016). 電動機車電池交換站選定既電池數量配置最適化. 中央大學土木工程學系學位論文, 1-80.
[3] 莊政達. (2016). 「道路施工交通維持計畫書」.
[4] 湯慶輝, & 黃新達. (2014). 救災直升機汰換與駕駛員換裝訓練時程之研究. 運輸學刊, 26(1), 1-33.
[5] 盧華安, & 簡秉民. (2010). 定期航商海上貨櫃運送路徑策略性規劃之研究. 運輸計劃季刊, 39(2), 191-221.
[6] Alderson, D. L., Brown, G. G., Carlyle, W. M., & Cox Jr, L. A. (2013). Sometimes there is no" most-vital" arc: Assessing and improving the operational resilience of systems. Military Operations Research, 18(1), 21-37.
[7] Ball, M. O., Golden, B. L., & Vohra, R. V. (1989). Finding the most vital arcs in a network. Operations Research Letters, 8(2), 73-76.
[8] Hong, T., & Purucker, S. T. (2018). Spatiotemporal sensitivity analysis of vertical transport of pesticides in soil. Environmental Modelling & Software, 105, 24-38.
[9] Jun, W., & Yue-jin, T. (2005). Finding the most vital node by node contraction in communication networks. In Proceedings. 2005 International Conference on Communications, Circuits and Systems, 2005. (Vol. 2). IEEE.
[10] Murray-Tuite, P. M., & Mahmassani, H. S. (2004). Methodology for determining vulnerable links in a transportation network. Transportation Research Record, 1882(1), 88-96.
[11] Murray, A. T., Matisziw, T. C., & Grubesic, T. H. (2008). A methodological overview of network vulnerability analysis. Growth and Change, 39(4), 573-592.
[12] M’cleod, L., Vecsler, R., Shi, Y., Levitskaya, E., Kulkarni, S., Malinchik, S., & Sobolevsky, S. (2017). Vulnerability of Transportation Networks: The New York City Subway System under Simultaneous Disruptive Events. Procedia computer science, 119, 42-50.
[13] Shekhtman, L. M., Danziger, M. M., Vaknin, D., & Havlin, S. (2018). Robustness of spatial networks and networks of networks. Comptes Rendus Physique, 19(4), 233-243.
[14] Santos, J. C., Tarrit, K., Sejfia, A., Mirakhorli, M., & Galster, M. (2019). An empirical study of tactical vulnerabilities. Journal of Systems and Software, 149, 263-284.
[15] Thiault, L., Marshall, P., Gelcich, S., Collin, A., Chlous, F., & Claudet, J. (2018). Space and time matter in social-ecological vulnerability assessments. Marine Policy, 88, 213-221.
[16] Wei, D., Zhang, X., & Mahadevan, S. (2018). Measuring the vulnerability of community structure in complex networks. Reliability Engineering & System Safety, 174, 41-52.
[17] Wang, S., & Liu, J. (2019). Community robustness and its enhancement in interdependent networks. Applied Soft Computing, 77, 665-677.
[18] Xu, X., Chen, A., & Yang, C. (2017). An optimization approach for deriving upper and lower bounds of transportation network vulnerability under simultaneous disruptions of multiple links. Transportation research procedia, 23, 645-663.
[19] Xiao, S., & Lu, Z. (2017). Structural reliability sensitivity analysis based on classification of model output. Aerospace Science and Technology, 71, 52-61.
[20] Xu, X., Chen, A., Jansuwan, S., Yang, C., & Ryu, S. (2018). Transportation network redundancy: Complementary measures and computational methods. Transportation Research Part B: Methodological, 114, 68-85.
[21] Xu, L., Lu, Z., Li, L., Shi, Y., & Zhao, G. (2018). Sensitivity analysis of correlated outputs and its application to a dynamic model. Environmental Modelling & Software, 105, 39-53.
[22] Yan, S., & Chen, H. L. (2002). A scheduling model and a solution algorithm for inter-city bus carriers. Transportation Research Part A: Policy and Practice, 36(9), 805-825.
[23] Yan, S., & Shih, Y. L. (2007). A time‐space network model for work team scheduling after a major disaster. Journal of the chinese institute of engineers, 30(1), 63-75.
[24] Yan, S., Hsiao, F. Y., Guo, J., & Chen, Y. C. (2011). Effective aircraft maintenance schedule adjustment following incidents. Transportation planning and technology, 34(8), 727-745.
[25] Yan, S., Chen, C. Y., & Chang, S. C. (2014). A car-pooling model and solution method with stochastic vehicle travel times. IEEE Transactions on Intelligent Transportation Systems, 15(1), 47-61.
[26] Yan, S., Wang, S. S., & Chang, Y. H. (2014). Cash transportation vehicle routing and scheduling under stochastic travel times. Engineering Optimization, 46(3), 289-307.
[27] Yan, S., Chen, Y. C., & Chang, C. F. (2017). Optimal Truck Dispatching For Construction Waste Conveyance. International Journal of Advanced Research in Engineering (ISSN Online: 2412-4362), 3(1), 9-15.
[28] Zhong, H., Wang, J., Yip, T. L., & Gu, Y. (2018). An innovative gravity-based approach to assess vulnerability of a Hazmat road transportation network: A case study of Guangzhou, China. Transportation Research Part D: Transport and Environment, 62, 659-671.
指導教授 顏上堯(Shang-Yao Yen) 審核日期 2019-7-15
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