Comprehensive passenger hub layout model of combined selection for capacity limitation and transportation mode
Article Text (Baidu Translation)
-
摘要: 分析了传统的综合客运枢纽布局优化模型, 同时增加运输模式与中转能力的约束条件, 提出了改进后的综合客运枢纽布局优化模型, 并设计了改进的遗传算法对其求解。应用LINGO软件进行有效性检验, 分别计算了8节点与50节点Solomon标准测试数据, 并将改进模型与经典算法进行比较。计算结果表明: 当应用LINGO软件计算8节点Solomon标准测试数据时, 平均运算时间为5 043s, 最优成本为1 952 418元, 应用遗传算法与MATLAB软件计算的平均运算时间为62s, 最优成本为1 955 900元; 当应用遗传算法与MATLAB软件计算50节点Solomon标准测试数据时, 平均运算时间为574s, 最优成本为8 500 600元; 当计算25节点的AP数据且枢纽节点数量为3时, 平均运算时间为612s, 最优成本为155 148元, 比经典算法降低了108元。可见, 改进模型有效。Abstract: The traditional optimization model of comprehensive passenger hub layout was analyzed, and the constraints of transportation mode and transfer capability were considered simultaneously. The improved optimization model of comprehensive passenger hub layout was proposed, and the improved genetic algorithm was introduced to solve the model. LINGO software was used to test the effectiveness, Solomon standard test data with 8 and 50 nodes were calculated respectively, and the improved model was compared with the classical algorithm. Calculation result shows that while calculating Solomon standard test data with 8 nodes, the average running time is 5 043 s and the optimal cost is 1 952 418 yuan by using LINGO software, the average running time is 62 s and the optimal cost is 1 955 900 yuan by using genetic algorithm and MATLAB software. While calculating Solomon standard test data with 50 nodes, the average running time is 574 s and the optimal cost is 8 500 600 yuan by using genetic algorithm and MATLAB software. While calculating AP data set with 25 nodes and hub node number is 3, the average running time is 612 s and the optimal cost is 155 148 yuan, the optimal cost decreases 108 yuancompared with the classical algorithm. So the improved model is effective. 6 tabs, 6 figs, 20 refs.
-
Table 1. Distances of 8city nodes
km Node 1 2 3 4 5 6 7 8 1 0.00 35.36 15.00 33.54 35.36 51.48 55.23 60.42 2 35.36 0.00 36.40 32.02 42.43 41.23 64.03 50.99 3 15.00 36.40 0.00 21.21 20.62 39.05 40.31 47.17 4 33.54 32.02 21.21 0.00 11.18 18.03 32.02 26.93 5 35.36 42.43 20.62 11.18 0.00 22.36 22.36 28.28 6 51.48 41.23 39.05 18.03 22.36 0.00 31.62 10.00 7 55.23 64.03 40.31 32.02 22.36 31.62 0.00 30.00 8 60.42 50.99 47.17 26.93 28.28 10.00 30.00 0.00 
下载: 导出CSV
Table 2. Passenger flow volumes of 8city nodes
104 person Node 1 2 3 4 5 6 7 8 1 0 100 100 100 100 100 100 10 2 150 0 80 18 80 120 100 20 3 150 130 0 18 80 12 100 100 4 150 10 110 0 80 120 100 100 5 150 130 15 15 0 120 80 90 6 150 130 15 15 110 0 80 60 7 150 130 110 80 110 110 0 50 8 15 15 110 110 110 110 110 0
下载: 导出CSV
Table 3. Transportation routes by using LINGO software
Node 1 2 3 4 5 6 7 8 1 — 1-5-2 direct direct direct 1-5-6 1-5-7 1-5-8 2 2-5-1 — 2-5-3 2-5-4 2-6-5 direct 2-6-7 2-6-8 3 direct 3-5-2 — direct direct direct 3-6-7 3-6-8 4 4-6-1 4-6-5-2 4-5-3 — direct 4-5-6 4-6-7 4-6-8 5 direct 5-6-2 direct direct — 5-4-6 direct direct 6 direct 6-5-2 6-4-3 direct direct — direct direct 7 7-6-1 7-6-2 7-5-6-3 direct direct direct — 7-5-8 8 8-6-1 8-6-2 8-4-3 8-5-4 direct direct 8-5-4-7 —
下载: 导出CSV
Table 4. Transportation routes by using genetic algorithm
Node 1 2 3 4 5 6 7 8 1 — 1-3-2 direct 1-3-4 1-3-5 1-3-6 1-3-5-7 1-3-6-8 2 2-3-1 — direct 2-3-4 2-3-5 2-3-6 2-3-7 2-3-8 3 direct direct — 3-5-4 direct direct direct direct 4 4-5-1 4-5-2 4-5-3 — direct 4-5-6 4-5-7 4-5-8 5 direct direct direct direct — direct direct direct 6 direct direct direct 6-5-4 direct — direct direct 7 7-5-1 7-5-2 7-5-3 7-5-3 direct 7-5-6 — 7-5-8 8 8-6-1 8-6-2 8-6-3 8-6-4 8-6-5 direct 8-6-7 —
下载: 导出CSV
Table 5. Result comparison
Method Average running time/s Optimal cost/yuan LINGO software 5 043 1 952 418 Genetic algorithm 62 1 955 900
下载: 导出CSV
Table 6. Comparison of results for two methods
Method Average running time/s Optimal cost/yuan Method 1 612.000 155 148 Method 2 0.185 155 256
下载: 导出CSV
-
[1] O'KELLY M E. The location of interacting hub facilities[J]. Transportation Science, 1986, 20(2): 92-106. doi: 10.1287/trsc.20.2.92 [2] MARIN A, CANOVAS L, LANDETE M. New formulations for the uncapacitated multiple allocation hub location problem[J]. European Journal of Operational Research, 2006, 172(1): 274-292. doi: 10.1016/j.ejor.2004.09.047 [3] GELAREH S, NICKLE S. Hub location problems in transportation networks[J]. Transportation Research Part E: Logistics and Transportation Review, 2011, 47(6): 1092-1111. doi: 10.1016/j.tre.2011.04.009 [4] ISHFAQ R, SOX C R. Hub location-allocation in intermodal logistic networks[J]. European Journal of Operational Research, 2011, 210(2): 213-230. doi: 10.1016/j.ejor.2010.09.017 [5] JAILLET P, GAO Song, YU Gang. Airline network design and hub location problems[J]. Location Science, 1996, 4(3): 195-212. doi: 10.1016/S0966-8349(96)00016-2 [6] YUAN Hong, LU Hua-pu. Study on model and method of comprehensive transportation terminal planning[J]. Journal of Highway and Transportation Research and Development, 2001, 18(3): 101-105. [7] TOPCUOGLU H, CORUT F, ERMIS M. Solving the uncapacitated hub location problem using genetic algorithms[J]. Computers and Operations Research, 2005, 32(4): 967-984. doi: 10.1016/j.cor.2003.09.008 [8] GELAREH S, NICKEL S. Liner shipping hub network design in a competitive environment[J]. Transportation Research Part E: Logistics and Transportation Review, 2010, 46(6): 991-1004. doi: 10.1016/j.tre.2010.05.005 [9] CHEN Qiang. VLSN algorithm based hub location and service frequencies determinations in intermodal freight transportation network[D]. Beijing: Beijing Jiaotong University, 2009. [10] LIU Qiang, LU Hua-pu, WANG Qing-yun. Bi-level programming model for regional integrated transportation hub layout[J]. Journal of Southeast University: Natural Science Edition, 2010, 40(6): 1358-1363. [11] LIN C C, LIN J Y, CHEN Y C. The capacitated p-hub median problem with integral constraints: an application to a Chinese air cargo network[J]. Applied Mathematical Modelling, 2012, 36(6): 2777-2787. doi: 10.1016/j.apm.2011.09.063 [12] HAO He-rui. Theory and technology of location and planning on road passenger transportation terminal[D]. Beijing: Beijing Jiaotong University, 2010. [13] ISHFAQ R, SOX C R. Intermodal logistics: the interplay of financial, operational and service issues[J]. Transportation Research Part E: Logistics and Transportation Review, 2010, 46(6): 926-949. doi: 10.1016/j.tre.2010.02.003 [14] WANG Lai-jun, HU Da-wei, SHI Zhong-ke. Model and genetic algorithms applying to a type of constrained facility location problem[J]. Journal of Chang'an University: Natural Science Edition, 2006, 26(6): 65-68. [15] KRATICA J, STANIMIROVIC Z, TOSIC D. Two genetic algorithms for solving the uncapacitated single allocation p-hub median problem[J]. European Journal of Operational Research, 2007, 182(1): 15-28. doi: 10.1016/j.ejor.2006.06.056 [16] YUAN Hua-zhi, LIU Jing. How to develop the logistics industry in Shaanxi Province[J]. Logistics Technology, 2008, 27(10): 63-66. [17] LI De-gang, HUO Ya-min, LUO Xia. Research on post-evaluation of highway main hub general planning[J]. China Journal of Highway and Transport, 2005, 18(2): 84-89. [18] YAO Zhi-gang, ZHOU Wei, WANG Yuan-qing, et al. Comparison of operation models of intercity bus hub[J]. Journal of Chang'an University: Natural Science Edition, 2006, 26(1): 71-74. [19] ZHOU Wei, WANG Hua-lan. Relation model between transportation development and social division of work based on Markov chain[J]. Journal of Chang'an University: Social Science Edition, 2006, 8(3): 1-3, 7. [20] FENG Zhong-xiang, LIU Hao-xue, ZHANG Jing-feng. Selection model of trip modes for rural population[J]. Journal of Traffic and Transportation Engineering, 2010, 10(3): 77-83. doi: 10.19818/j.cnki.1671-1637.2010.03.014 -
点击查看大图
图(6) / 表(6)
计量
- 文章访问数: 868
- HTML全文浏览量: 118
- PDF下载量: 835
- 被引次数: 0
