進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-2907201414490400
論文名稱(中文) 隨機團體需求之訂位限制
論文名稱(英文) Booking Limit for Stochastic Group Demand in Railway
校院名稱 成功大學
系所名稱(中) 交通管理科學系
系所名稱(英) Department of Transportation & Communication Management Science
學年度 102
學期 2
出版年 103
研究生(中文) 蘇瑩瑩
研究生(英文) Ying-Ying Su
學號 R56014133
學位類別 碩士
語文別 中文
論文頁數 66頁
口試委員 指導教授-鄭永祥
口試委員-張有恆
口試委員-林東盈
口試委員-李治綱
口試委員-陳信雄
中文關鍵字 隨機團體需求  訂位限制  基因演算法  蒙地卡羅模擬法 
英文關鍵字 Stochastic demand  Booking limit  Genetic algorithm  Monte Carlo simulation 
學科別分類
中文摘要 本研究探討軌道運輸業團體旅客的訂位系統決策模式,軌道運輸的列車座位容量有限,團體旅客由於訂位需求提早出現、訂位人數具規模、團體票價優惠等特性與單人旅客不同,不可將相同旅次區間的單人旅客與團體旅客視為同質,因此,將團體旅客與單人旅客接受模式分開計算。
考量團體旅客需求隨機特性,結合蒙地卡羅模擬法模擬隨機團體需求,並運用基因演算法求解軌道系統O-D團體訂位數量限制,利用各O-D團體訂位數量限制保護高價值的團體旅客能被接受,此訂位數量可提供營運人員在售票時參考,取代過去以人工經驗判斷是否接受團體的模式。
本研究修改目前臺灣高鐵接受團體旅客需求的流程,加入各O-D訂位數量限制之考量,透過隨機需求模擬旅客訂位情況以驗證本研究模式,並比較兩者之期望收益,本研究結果發現,在O-D訂位數量的保護下,模式接受的團體組合不同,並且一般列車較高鐵模式增加了2%的團體收益。
英文摘要 According to the different characteristics of group passenger in railway industry, this study would like to investigate the passenger reservation system to improve the revenue of group passenger. We formulate a model to determine whether accept or reject the requisition of group passenger and replace the judgment of human-experienced. The paper proposes a method for solving stochastic group demand seat inventory control problems using a hybrid of a genetic algorithm in uncertain environments and the Monte Carlo simulation method. Computation results are analyzed by applying the model to a real-world Taiwan railway system. Analytical results demonstrate that a proper adjustment of the reservation system and accurate booking limit for each O-D pairs improves the revenue of group passenger by 2% for each train.
論文目次 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研究範圍 4
1.4研究架構 4
第二章 文獻回顧 6
2.1營收管理(revenue management)概論 6
2.1.1航空與鐵路營收管理之差異 6
2.1.2座位存貨管理 10
2.1.3小結 11
2.2 隨機旅客網路座位管理 11
2.3團體旅客需求(group customers)相關文獻 13
2.4求解方法 15
2.4.1基因演算法(Genetic Algorithm, GA) 16
2.4.2基因演算法結合蒙地卡羅模擬之應用 19
2.5小結 21
第三章 建構模式 22
3.1定義問題 22
3.1.1團體旅客座位存貨管理 22
3.2問題假設與限制 24
3.3模式建構 25
3.3.1符號說明 25
3.3.2數學模式 27
3.4小結 31
第四章 求解演算法 32
4.1求解演算法流程 32
4.1.1基因演算法程序 34
4.1.2蒙地卡羅演算法程序 40
4.2小結 41
第五章 數值測試 42
5.1數值測試說明 42
5.1.1高鐵現行接受團體流程 42
5.1.2本研究模式接受團體流程 44
5.2數值測試結果 46
5.3 小結 55
第六章 結論與建議 56
6.1研究結論 56
6.2研究建議 58
6.3研究貢獻與未來研究方向 59
參考文獻 61
參考文獻 1. Abe, I. (2007). Revenue management in the railway industry in Japan and Portuga:A stakeholder approach, Master of science in technology and policy, Massachusetts Institute of Technology.
2. Aghaee, M. P. and Khedmatlo, S. (2011). Designing a dynamic revenue management model(Case study on railway passenger transportation). Transportation Research Journal, 1(1), pp.61~74.
3. Akartunali, K., Boland, N., Evans, I., Wallace, M., and Waterer, H. (2013). Airline planning benchmark problems—Part I:Characterising networks and demand using limited data. Computers and Operations Research, 40, pp.775~792.
4. Akartunali, K., Boland, N., Evans, I., Wallace, M., and Waterer, H. (2013). Airline planning benchmark problems—Part II:Passenger groups, utility and demand allocation. Computers and Operations Research, 40, pp.793~804.
5. Armstrong, A. and Meissner, J. (2010). Railway revenue management:Overview and models. Working Paper, The Department of Management Science Lancaster University Management School, UK.
6. Belobaba, P. P. (1987), Airline yield management:An overview of seat inventory control.  Transportation Science, 21, pp.63~73.
7. Bertsimas, D. and Boer, S. D. (2005), Simulation-Based Booking Limits for Airline Revenue Management. Operations Researc ,53(1), pp. 90~106.
8. Bharill, N. R. (2008), Revenue management in railway operations:A study of the Rajdhani Express, Indian railways Rajdhani Express, Indian railways. Transportation Research Part : Policy and Practice, 42(9),  pp.1195~1207.
9. Bitran, G. R. and Mondschein, S. V. (1995), An application of yield management to the hotel industry considering multiple day stays.  Operations Research, 43(3), pp.427~443.
10. Bodily, S. E., and Pfeifer, P. E. (1992), Overbooking decision rules. Omega, 20(1), pp.129~133.
11. Brumelle, S. L., Mcgill, J. I. (1993),  Airline seat allocation with multiple nested fare classes. Operation Research, 41, pp.1363~1386.
12. Chambers, L. (1995), Practical handbook of genetic algorithms:Applications. CRC Press, Boca Raton, FL.
13. Ciancimino, A., Inzerillo, G., Lucidi, S., and Palagi, L.(1999), A mathematical programming approach for the solution of the railway yield management problem. Transportation Science, 33(2), pp.168~181.
14. Clausen, T., Hjorth, A. N., Nielsen, M., and Pisinger, D.(2010), The off-line group seat reservation problem. European Journal of Operational Research, 207,  pp. 1244~1253.
15. Curry, R. E. (1990) Optimal airline seat allocation with fare classes nested by origins and destinations. Transportation Science, 24,  pp.193~204.
16. Dror, M., Trudeau, P., and Ladany, S. P. (1988) Network models for seat allocation on flights. Transportation Research Part B, 22(4), pp.239~250.
17. De Boer, S. V., Freling, R. and Piersma, N. (2002), Mathematical programming for network revenue management revisited.  European Journal of Operational Research, 137,  pp. 72~92.
18. Fintel, J. (1990), Gold among the silver. Restaurants USA, 10(3), pp.17~22.
19. Gillen, D. and Hasheminia, H. (2013),  Estimating the demand responses for different sizes of air passenger groups.  Transportation Research Part B, 49, pp.24~38.
20. Goldberg, E. D., (1999), Genetic algorithms in search optimization and machine learning. Addison-Wesley, Reading, MA.
21. Guadix, J., Cortés, P., Onieva, L., and Muñuzuri, J. (2010), Technology revenue management system for customer groups in hotels.  Journal of Business Research, 63, pp. 519~527.
22. Guerriero, F., Miglionico, G. and Olivito, F. (2014),  Strategic and operational decisions in restaurant revenue management.  European Journal of Operational Research, 237(3), pp.1119~1132.
23. Hood, ISA. (2000), MERLIN: Model to evaluate revenue and loading for intercity. (2nd ed.), Yield Management:Strategies for the Service Industries, Thomson London, pp.98~110.
24. Kall, P. and Wallace, S. W. (1994), Stochastic programming(2nd ed.).
25. Kimes, S. E. (1989), Yield management:A tool for capacity-constrained service firms. Journal of Operations Management, 8(4), pp.348~363.
26. Kimes, S. E. (2000), A strategic approach to yield management.  In:Ingold A, McMahon-Beattie U, Yeoman I, editors. pp.3~14.
27. Kraft, E. R., Srikar, B. N. and Phillips, R. L. (2000), Revenue management in railroad applications. Transportation Quarterly, 54(1), pp.157~176.
28. Kuah, C. T. , Wong, K. W., and Wong, W. P. (2012),  Monte carlo data envelopment analysis with genetic algorithm for knowledge management performance measurement. Expert Systems with Applications, 39, pp.9348~9358.
29. Littlewood, K. (1972), Forecasting and control of passengers booking. AFIFORS SYMP., PROC, pp.95~117.
30. Liu, Z. Y., (2011), Probit-based stochastic user equilibrium and their applications in congestion pricing., Ph.D. Thesis, National University Singapore.
31. Liu, Z. Y., Meng, Q. (2013), Distributed computing approaches for large-scale probit-based stochastic user equilibrium problem. Journal of Advanced Transportation, 47(6), pp.553~571.
32. Liu, Z. Y., Meng, Q, Wang, S. (2013), Speed-based toll design for cordon-based congestion pricing scheme.  Transportation Research Part C, 31 , pp.83~98.
33. Mcgill, J. I. and Van Ryzin, G. J. (1999), Revenue management:research overview and prospects. Transportation Science, 33(2), pp.233~256.
34. Modarres, M. and Sharifyazdi, M. (2009), Revenue management approach to stochastic capacity allocation problem. European Journal of Operational Research, 192, pp. 442~459.
35. Netessine, S. and Shumsky, R. (2003), Introdution to the theory and practice of yield management. INFORMS Transportation on Education, 3(1), pp.34~44.
36. Obeng, K. and Sakano, R. (2012), Airline fare and seat management strategies with demand dependency. Journal of Air Transport Management, 24, pp.42~48.
37. Pak, K. and Piersma, N. (2002), Overview of OR techniques of airline revenue management. Statistica Neerlandica, 56(4),  pp.479~495.
38. Rosenfeld, J. (1986), Demographics on vacation. American Demographics, 8(1), pp.38~41.
39. Schwartz, Z., Stewart, W. and Backlund, E. A. (2012), Visitation at capacity-constrained tourism destinations:Exploring revenue management at a national park. Tourism Management, 33, pp.500~508.
40. Sibdari, S., Lin, K. Y. and Chellapan, S. (2008), Multiproduct revenue management: An empirical study of Auto Train at Amtrak. Journal of Revenue and Pricing Management, 7(2),  pp.172~184.
41. Smith, B. C., Leimkuhler, J. F. and Darrow, R. M.(1992), Yield Management at American Airlines. Interfaces, 22(1), pp.8~31.
42. Svrcek, T. (1991) Modeling airline group passenger demand for revenue optimization, Massachusetts Institute of Technology, M.I.T. Libraries Theses Collection.
43. Talluri, K. T. and Van Ryzin, G. J. (2004) The Theory and Practice of Revenue Management, Kluwer.
44. Tsai, T. H. (2014), A self-learning advanced booking model for railway arrival forecasting. Transportation Research Part C, 39,  pp.80~93.
45. Wang, X. and Wang, F. (2007), Dynamic network yield management.,  Transportation Research Part B, 41, pp.410~425.
46. Weatherford, L. R., and Bodily, S. E. (1992), A taxonomy and research overview of perishable-asset revenue management:yield management, overbooking, and pricing. Operations Research, 40(5), pp.831~844.
47. Williamson, E. L. (1992) Airline network seat inventory control:Methodologies and revenue impacts., Massachusetts Institute of Technology, M.I.T.Libraries Theses Collection.
48. Wong, J. T., Koppelman, F. S., and Daskin, M. S. (1993) Flexible assignment approach to itinerary seat allocation. Transportation Research Part B, 27B(1), pp.33~48.
49. Yoshitomi, Y. and Yamaguchi, R. (2003), A genetic algorithm and the Monte Carlo method for stochastic job-shop scheduling.  International Transportations In Operational Research, 10, pp.577~596.
50. You, P. S. (2008), An efficient computational approach for railway booking problems. European Journal of Operational Research, 185(5), pp. 811~824.
51. 陳嘉珮,「運用基因演算法及最佳化資源分配法求解隨機需求之長期車輛問題」,國立交通大學運輸科技與管理研究所碩士論文,民國一百年。
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2019-08-07起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw