博碩士論文 100382010 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:38 、訪客IP:3.141.202.187
姓名 陳怡君(Yi-Chun Chen)  查詢紙本館藏   畢業系所 土木工程學系
論文名稱 颱風擾動期間航班動態調整之研究
(Dynamic Flight Schedule Adjustment for Typhoon Interruption)
相關論文
★ 橋梁檢測人力機具排班最佳化之研究★ 勤業務專責分工下消防人員每日勤務排班最佳模式之研究
★ 司機員排班作業最佳化模式之研究★ 科學園區廢水場實驗室檢驗員任務指派 最佳化模式之研究
★ 倉儲地坪粉光工程之最佳化模式研究★ 生下水道工程工作井佈設作業機組指派最佳化之研究
★ 急診室臨時性短期護理人力 指派最佳化之探討★ 專案監造人力調派最佳化模式研究
★ 地質鑽探工程人機作業管理最佳化研究★ 職業棒球球隊球員組合最佳化之研究
★ 鑽堡於卵礫石層施作機具調派最佳化模式之研究★ 職業安全衛生查核人員人力指派最佳化研究
★ 救災機具預置最佳化之探討★ 水電工程出工數最佳化之研究
★ 石門水庫服務台及票站人員排班最佳化之研究★ 空調附屬設備機組維護保養排程最佳化之研究
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 航班經常遭受突發的擾動事件影響,導致無法依原訂的班表準時起降,造成該航機後續銜接任務以及後面航班連帶受到影響。台灣位處於西太平洋颱風侵襲路徑區,因此以颱風擾動事件最為頻繁且影響甚鉅。實務上,在面對颱風擾動時,航空公司多以人為經驗並佐以氣象預報資料研擬航班的重新指派計畫,然而此作法不僅無效率且缺乏整體航空網路考量,僅能獲得可行但策略不佳的結果。以往航班排程文獻,多為規劃階段問題,此種規劃問題與具時效性的擾動事件之即時調整問題不同。而針對臨時性擾動事件的航班即時調整相關文獻,除了忽略旅客疏運部分,在擾動時間上則簡化為一確定時間,然而颱風的擾動時間為不確定,因此其模式難以直接應用於本研究問題。緣此,本研究考量颱風擾動時段為明確與不確定的情況下,以總營運成本最小化為目標,發展確定性與隨機性兩種航班重新指派模式,提供航空公司決策者在面臨颱風擾動事件時,進行航班調整之參考。
本研究分為兩個部分。第一部分中,根據航空公司航班重新指派實務營運情況及限制,構建颱風擾動時段明確下之確定性航班重新指派模式;第二部分中,則針對颱風擾動時段在不明確的情形下,構建隨機性航班重新指派模式。此外,此兩部分的模式亦分別進行多階段動態決策測試,以顯示兩模式之彈性與實用性。本研究利用網路流動技巧及數學規劃方法構建所有模式,並加上實際營運時的相關限制。此等模式可定式為一含額外限制之整數網路流動問題,屬NP-hard問題,當實務問題過大時,難以在合理時間內求得最佳解。因此,本研究根據問題特性,以分割再組合策略,發展分割式啟發解演算法,以有效地求解大型實務問題。此兩模式均利用C++程式語言撰寫,並以CPLEX數學規劃軟體求解。最後,進行實際範例測試並提出結論與建議。
摘要(英) Flight schedules are often affected by unexpected interruption events, causing aircraft to be unable to take off or land on time, also resulting in connection delays as well as later flights. Taiwan is located in the Western Pacific on the path of typhoons, so one of the worst and the most frequent interruption events are typhoons. In practice, when it is known that a typhoon is approaching, decision makers make adjustments to flight schedules and fleet routes based on experience and the aviation weather forecast data, which is neither efficient nor effective. Moreover, the manual adjustment of airline network operations may not be considered systematically, usually leading to feasible but inferior reassignments. The primary focus in past studies of flight scheduling problems has been on the planning stage. However the planning problem, which does not need to be solved immediately, is different from real-time flight schedule adjustment problems in response to typhoon events. Although there have been some studies discussing real-time flight schedule adjustment problems, the problem of passenger transportation has been neglected and the duration of the determined disruption has been over-simplified. In fact, the duration of typhoon is uncertain, so the current models cannot be applied. Therefore, in this study, we develop a deterministic flight rescheduling model and a stochastic flight rescheduling model, aimed at the minimization of the total operating cost. These models should be useful tools to assist decision makers to make adjustment to flight schedules and fleet routes in response to typhoon disruption events.
This dissertation is divided into two parts. In the first essay, we consider the practices, current operations and limitations of an airline carrier for the construction of a deterministic flight rescheduling model dependent upon a precise typhoon disruption period. In the second essay, we construct a stochastic flight rescheduling model given an uncertain typhoon disruption period. In addition, a dynamic application using a multi-stage decision-making process is adopted to illustrate the flexibility and practicality of the two models. Network flow techniques and mathematical programming are utilized to develop all the models, coupled with the constraints used in practice. All the models are formulated as integer network flow problems with side constraints, and are characterized as NP-hard. To efficiently solve the realistically large problems that occur in practice, a solution algorithm based on the divide and conquer approach is developed. The models are written using the C++ computer language, coupled with the CPLEX mathematical programming solver, to solve the problems. Finally, numerical tests are performed, and some conclusions and suggestions for future research are given.
關鍵字(中) ★ 颱風擾動事件
★ 航班重新指派
★ 即時調整
★ 動態決策應用
★ 含額外限制之整數網路流動問題
★ 分割式啟發解演算法
關鍵字(英) ★ typhoon disruption events
★ flight rescheduling
★ immediate planning problem
★ dynamic decision-making
★ integer network flow problem with side constraints
★ heuristic algorithm
論文目次 摘要 i
Abstract ii
誌謝 iii
Contents iv
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
Chapter 2 Essay 1: Flight Rescheduling and Fleet Rerouting for Typhoon Disruption Events under Deterministic Periods 5
2.0 Abstract 5
2.1 Introduction 5
2.2 Problem description 8
2.3 The model 10
2.3.1 The flight-flow time-space network 11
2.3.2 The passenger-flow time-space network 16
2.3.3 The model formulation 20
2.3.4 The strategies for model application 22
2.3.5 Verification of the model 26
2.4 Solution algorithm 28
2.5 Numerical tests 29
2.5.1 Input data 30
2.5.2 Test results 31
2.5.3 Sensitivity analyses 35
2.5.4 Tests of other scenarios 39
2.5.5 Dynamic application 44
2.6 Conclusion 46
Chapter 3 Essay 2: Flight Rescheduling and Fleet Rerouting for Typhoon Events under Uncertain Disruption Periods 48
3.0 Abstract 48
3.1 Introduction 48
3.2 The model 52
3.2.1 The stochastic flight-flow time-space network 53
3.2.2 The stochastic passenger-flow time-space network 58
3.2.3 Design of an unanticipated risk cost for flights and passengers 63
3.2.4 The model formulation 68
3.2.5 Systematic strategies for model application 70
3.2.6 Verification of the model 74
3.3 Solution algorithm 77
3.4 Numerical tests 78
3.4.1 Input data 79
3.4.2 Test results 81
3.4.3 Comparison of the results for the deterministic and the stochastic models 85
3.4.4 Sensitivity analyses 89
3.4.5 Tests of other scenarios 93
3.4.6 Dynamic application 94
3.5 Conclusion 98
Chapter 4 Conclusions, Suggestions, and Contributions 100
4.1 Conclusions 100
4.2 Suggestions for future research 102
4.3 Contributions 102
References 104
Appendix 1 Other scenarios for sub-problems in 4 stages 107
Appendix 2 Input data for the flights and airports 109
Appendix 3 Passenger delivery cost 114
參考文獻 1. Ahmadbeygi, S., Cohn, A., and Lapp, M. (2010), “Decreasing airline delay propagation by re-allocating scheduled slack,” IIE Transactions, Vol. 42, pp. 478-489.
2. Bertsimas, D. and Paterson, S. S. (1998), “The air traffic flow management problem with enroute capacities,” Operations Research, Vol. 46, pp. 406-422.
3. Bratu, S. and Barnhart, C. (2006), “Flight operations recovery: New approaches considering passenger recovery,” Journal of Scheduling, Vol. 9, No. 3, pp. 279-298.
4. Clarke, M. (1998), “Irregular airline operations: a review of the state-of-the-practice in airline operations control center,” Journal of Air Transport Management, Vol. 4, pp. 67-76.
5. Desaulniers, G., Desrosiers, J., Dumas Y., Solomon, M. M., and Soumis, F. (1997), “Daily Aircraft Routing and Scheduling,” Management Science, Vol. 43, pp. 841-855.
6. Filar, J. A., Manyen, P., and White, K. (2001), “How airlines and airports recover from schedule perturbations: a survey,” Annals of Operations Research, Vol. 108, pp. 315-333.
7. Garey, M.R. and Johnson, D.S. (1979), Computers and Intractability: A Guide to the Theory of NP-completeness, W.H. Freeman & Company, San Francisco.
8. Jafari, N. and Zegordi, S. H. (2011), “Simultaneous recovery model for aircraft and passengers,” Journal of Franklin Institute, Vol. 348, pp. 1638-1655.
9. Jarrah, A. I. Z., Yu, G., Krishnamurthy, N., and Rakshit, A. (1993), “A decision support framework for airline flight cancellations and delays,” Transportation Science, Vol. 27, No. 3, pp. 266-280.
10. Kotnyek, B. and Richetta, O. (2006), “Equitable models for the stochastic ground-holding problem under collaborative decision making,” Transportation Science, Vol. 40, No. 2, pp. 133-146.
11. Levin, A. (1969), “Some fleet routing and scheduling problems for air transportation systems,” Flight Transportation Laboratory Report R-68-5, Massachusetts Institute of Technology, Cambridge, MA.
12. Mathaisel, D. F. X. (1996), “Decision support for airline system operations control and irregular operations,” Computers and Operations Research, Vol. 23, No. 11, pp. 1083-1098.
13. Pita, J.P., Adler, N., and Antunes, A.P. (2014), “Socially-oriented flight schedule and fleet assignment model with an application to Norway,” Transportation Research Part B, Vol. 61, pp. 17-32.
14. Pita, J.P., Barnhart, C., and Antunes, A.P. (2012), “Integrated Flight Scheduling and Fleet Assignment Under Airport Congestion,” Transportation Science, Vol. 47, No. 4, pp. 477-492.
15. Richetta, O. and Odoni, A.R. (1993), “Solving optimally the static ground-holding policy problem in air traffic control,” Transportation Science, Vol. 27, No. 3, pp. 228-238.
16. Richetta, O. and Odoni, A. R. (1994), “Dynamic solutions to the ground-holding problem in air traffic control,” Transportation Research Part A, Vol. 28, No. 3, pp. 167-185.
17. Sama, M., D’Ariano, A., and Pacciarelli, D. (2013), “Rolling horizon approach for aircraft scheduling in the terminal control area of busy airports,” Procedia - Social and Behavioral Sciences, Vol. 80, pp. 531-552.
18. Silvente, J., Kopanos, G.M., Pistikopoulos, E.N., and Espuna, A. (2015), “A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids,” Applied Energy, Vol. 155, pp. 485-501.
19. Simpson, R. W. (1969), “Scheduling and routing models for airline systems,” Flight Transportation Laboratory Report R-68-3, Massachusetts Institute of Technology, Cambridge, MA.
20. Stojkovi?, G., Soumis, F., Desrosiers, J., and Solomon, M. M. (2002), “An optimization model for a real-time flight scheduling problem,” Transportation Research Part A, Vol. 36, pp. 779-788.
21. Stolletz, R. and Zamorano E. (2014), “A rolling planning horizon heristic for scheduling agents with different qualifications,” Transportation Research Part E, Vol. 68, pp. 39-52.
22. Tang, C. H., Yan, S., and Chen, Y. H. (2008), “An integrated model and solution algorithms for passenger, cargo, and combi flight scheduling,” Transportation Research Part E, Vol. 44, pp. 1004-1024.
23. Thengvall, B. G., Bard, J. F., and Yu, G. (2000), “Balancing user preferences for aircraft schedule recovery during irregular operations,” IIE Transactions, Vol. 32, No. 3, pp. 181-193.
24. Thengvall, B. G., Yu, G., and Bard, J. F. (2001), “Multiple fleet aircraft schedule recovery following hub closures,” Transportation Research Part A, Vol. 35, pp. 389-308.
25. Vranas, P. B., Bertsimas, D. J., and Odoni, A. R. (1994), “The multi-airport ground holding problem in air traffic control,” Operations Research, Vol. 42, pp. 249-261.
26. Yan, S. Chen, C.H., Chen, Y.C., and Liao, J.W. (2016a), “Dynamic Transit Scheduling of Medical Goods in Regular Operations,” Journal of the Chinese Institute of Civil and Hydraulic Engineering, Vol. 28, No. 3, pp. 175-184. (in Chinese)
27. Yan, S., Chen, C.H., and Tsao C.H. (2009), “Dynamic Transit Scheduling of Medical Goods for Demands Being Disturbed in Short-Term Operations,” Transportation Planning Journal, Vol. 38, No. 3, pp. 297-322. (in Chinese)
28. Yan, S., Chen, S. C., and Chen, C. H. (2006a), “Air cargo fleet routing and timetable setting with multiple on-time demands,” Transportation Research Part E, Vol. 42, pp. 409-430.
29. Yan, S., Chen, Y.C., and Chang, C.F. (2017), “Optimal Truck Dispatching For Construction Waste Conveyance,” International Journal of Advanced Research in Engineering, Vol. 3, No. 1, pp. 9-15.
30. Yan, S. and Lin, C. (1997a), “Airline scheduling for the temporary closure of airports,” Transportation Science, Vol. 31, No. 1, pp. 72-82.
31. Yan, S. and Lin, C. (1997b), “Multi-fleet scheduling models for the temporary closure of airports,” Journal of the Chinese Institute of Civil and Hydraulic Engineering, Vol. 9, No. 4, pp. 679-685.
32. Yan, S., Lu, C.C., and Wang, M.H. (2018), “Stochastic Fleet Deployment Models for Public Bicycle Rental Systems,” International Journal of Sustainable Transportation, Vol. 12, No. 1, pp. 39-52.
33. Yan, S. and Tseng, C. H. (2002), “A passenger demand model for airline flight scheduling and fleet routing,” Computers and Operations Research, Vol. 29, pp. 1559–1581.
34. Yan, S., Wang, W.C., Chang, G.W., and Lin, H.C. (2016b), “Effective Ready Mixed Concrete Supply Adjustments with Inoperative Mixers under Stochastic Travel Times,” Transportation Letters, Vol. 8 No. 5, pp. 286-300.
35. Yan, S. and Yang, D.H. (1996), “A decision support framework for handling schedule perturbation,” Transportation Research Part B, Vol. 30, No. 6, pp. 405-419.
36. Yan, S. and Young, H. F. (1996), “Decision support framework for multi-fleet routing and multi-stop flight scheduling,” Transportation Research Part A, Vol. 30, No. 5, pp. 379-398.
37. Zhang, D., Yu, C., Desai, J., Lau, H.Y.K, and Srivathsan, S. (2017), “A time-space network flow approach to dynamic repositioning in bicycle sharing systems,” Transportation Research Part B, Vol. 103, pp. 188-207.
指導教授 顏上堯(Shangyao Yan) 審核日期 2018-8-16
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明