Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/6117
標題: 單線鐵路系統貨運列車時刻表的改善
Improvement of Freight Train Timetable for Single-Track Railway System
作者: 簡先弘
Chien, Hsien-Hung
關鍵字: railway;鐵路;freight train timetable;fuzzy inference system;scheduling;貨運列車時刻表;模糊推論系統;調度
出版社: 電機工程學系所
引用: References [1] Caprara, A., Monaci, M., Toth, P. and Guida, P. L., “A Lagrangian Heuristic for a Real-World Train Timtabling Problem,” Discrete Applied Mathematics, Vol. 154, No.5, pp. 738-753, 2006. [2] Lin, C. T. and Lee, C. S. G, Neural, Fuzzy inference systems: A Neuro-Fuzzy Synergism to Intelligent Systems, American: Hall, 1996. [3] Sinotech Engineering Consultants, Inc., “A study of TRA train scheduling optimization,” a project outsourced by Institute of Transportation, MOTC (MOTC-IOT-94-EBB004), 2006. [4] Zhang, S. T. and Chen, Y. C.,“ Simulation for influence of train failure on railway traffic flow and research on train operation adjusting strategies using cellular automata,” Statistical Mechanics and its Applications, Vol. 390, No.21, pp. 3710-3718, 2011. [5] Dorfman, M. J. and Medanic, J., “Scheduling Trains on a Railway Network Using a Discrete Event Model of Railway Traffic,” Transportation Research Part B, Vol. 38, No. 1, pp. 81-98, 2004. [6] Valentina, C. and Alberto, C. and Paolo, T., “Scheduling extra freight trains on railway networks,” Transportation Research Part B, Vol. 44, No. 2, pp. 215-231, 2010. [7] Carey, M. and Crawford, I., “Scheduling trains on a network of busy complex stations,” Transportation Research Part B: Methodological, Vol.41, No. 2, pp. 159-178, 2007. [8] Dessouky, M. M., Lu, Q. and Leachman, R.C., “Using simulation modeling to assess rail track infrastructure in densely trafficked metropolitan areas,” Simulation, Vol.1, No. 8, pp.725-731, 2002. [9] Vansteenwegen, P. and Oudheusden, D. V., “Decreasing the Passenger Waiting Time for an Intercity Rail Network,” Transportation Research, Part B, Vol. 41, No. 4, pp. 478-492, 2007. [10] Lee, Y., Chen, C.Y., “A heuristic for the train pathing and timetabling problem,” Transportation Research Part B: Methodological, Vol. 43, No. 8, pp. 837-851, 2009. [11] Caprara, A., Monaci, M., Toth, P. and Guida P. L., “A Lagrangian Heuristic for a Real-World Train Timtabling Problem,” Discrete Applied Mathematics, Vol. 154, No.5, pp. 738-753, 2006. [12] Cameron G. Walker, Jody N. Snowdon, David M. Ryan ,“Simultaneous disruption recovery of a train timetable and crew roster in real time,” Computers and Operations Research, Vol. 32,No. 8, pp. 2077-2094, 2005. [13] Burdett, R. L. and Kozan, E., “A Disjunctive Grapy Model and Framework for Constructing New Train Schedules,” European Journal of Operation Research, Vol. 200, No. 1, pp. 85-98, 2010. [14] D'Ariano, A., Pacciarelli, D. and Pranzo, M.,“A branch and bound algorithm for scheduling trains in a railway network,” European Journal of Operational Research, Vol. 183, No. 2, pp. 643-657, 2007. [15] Burdett, R. L. and Kozan, E., “Techniques for Inserting Additional Trains into Existing Timetables,” Transportation Research, Part B, Vol. 43, No. 8, pp. 821-836, 2009. [16] Kuo, A., Miller-Hooks, E. , Mahmassani, H. S., “Freight Train Scheduling with Elastic Demand,” Transportation Research, Part E, Vol. 46, No. 6, pp. 1057-1070, 2010. [17] Mu, S., and Dessouky, M., “Scheduling freight trains traveling on complex networks,” Transportation Research Part B: Methodological, Vol. 45, No. 7, pp. 1103-1123, 2011. [18] Isaai,M. T. and Singh, M. G., “An Object-Oriented, Constraint-Based Heuristic for a Class of Passenger-Train Scheduling Problems,” IEEE Transactions on Systems, Man, and Cybernetics, Part C, Vol. 30, No. 1, pp. 12-21, 2000. [19] Zhou, X. and Zhong, M., “Single-track train timetabling with guaranteed optimality: Branch-and-bound algorithms with enhanced lower bounds,” Transportation Research-B, Vol.41, No. 3, pp.320-341, 2007. [20] Yang, L., Li, K. and Gao, Z., “Train Timetable Problem on a Single-Line Railway With Fuzzy Passenger Demand,” IEEE Transactions on fuzzy inference systems, Vol. 17, No. 3, pp. 617-629, 2009. [21] Cheng, Y. H. and Yang, L. A., “A Fuzzy Petri Nets Approach for Railway Traffic Control in Case of Abnormality: Evidence from Taiwan Railway System,” Expert Systems with Applications, Vol. 36, No. 4, pp. 8040-8048, 2009. [22] Elamvazuthi, I., Vasant, P. and Webb, J.,“The Application of Mamdani Fuzzy Model for Auto Zoom Function of a Digital Camera,” International Journal of Computer Science and Information Security, Vol. 6, No. 3, pp244-249, 2009.
摘要: 
傳統鐵路系統大部份以人工或半人工方式排定列車班表。列車班表問題是複雜的組合最佳化問題,當面臨大型且複雜的列車班表問題時,傳統的數學運算方法難以求得確切的最佳解,或是求解過程需花費很長的計算時間。目前許多國家仍使用單線鐵路,如何改進單線鐵路的人工排班,將有助於提升整體鐵路系統的性能。
本論文提出提升單線鐵路貨物列車班表性能的方法,利用號誌系統「閉塞區間」的概念,以矩陣型式表示列車在站間與站內軌道的佔用情形,呈現列車運行圖的全貌。藉由調整列車出發時間、停站時間及減少不必要的停站,以縮短班表的平均列車旅程時間及單一列車旅程時間。首先,將實際班表轉換為矩陣型式並儲存。其次,找出站間與站內的所有衝突並加以化解 ,以產生可行班表。最後,配合模糊推論系統調整貨車班次出發時間範圍,並檢查班次在擁塞車站及時段是否可以進行客貨車停站時間調整,以減少列車超時。以專家經驗配合模糊推論方法,使班表優化且更具彈性,並可在合理時間內提升列車排班效率,除了節省人力外,更能提高班表營運效能。

In conventional railway systems, train timetables are generally scheduled manually or semi-manually. Scheduling is a complex problem of combinational optimization; when it is challenged by large-scale and complex timetable problems, specific optimal solutions are difficult to achieve by traditional mathematical computations; or, otherwise, the solving process takes lengthy time. So far, single-track railways are still used in many developing countries. Improvement on the manual scheduling in such railways will help uplift the overall system performance.
The method of improving performance of freight train timetable for single-track railways is proposed in the thesis. Using the concept of fixed-block signaling system, we express the occupation of inter- and intra-station tracks by trains by matrix to represent the train blocking time diagram in entirety. Train departure time, dwell time and unnecessary stopping are adjusted to shorten the average train travel time and single train travel time in the timetable. First, a real timetable was converted to the matrix form. Second, the conflicts between successive stations and inside the station were spotted and solved to generate a feasible timetable. Third, a fuzzy logic system was used to adjust range of the train departure time and check was made to determine whether the dwell time can be adjusted for passenger and freight trains at congested stations and time interval to minimize the train waiting time. By working with expert experience combining fuzzy inference method, efficiency of the timetable was improved and became more flexible. Update of the timetable performance means improvement of train operation management.
URI: http://hdl.handle.net/11455/6117
其他識別: U0005-0402201213551300
Appears in Collections:電機工程學系所

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.