Optimization of Container Train Service Route Based on Sea-Rail Intermodal Transportation
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摘要: 虑考虑海铁联运过程中影响集装箱班列开行的不确定因素, 结合班列服务客户各自固定需求时间窗的实际需求, 引入不确定规划区间来表示集装箱在客户节点的装卸箱服务时间, 同时将具有时效性要求的需求时间窗设置为软约束, 运用惩罚函数将其作为惩罚项整合到运输成本目标函数中, 选择合理的惩罚系数, 构建以运输成本低、运输时间少为目标的班列服务路径非线性多目标优化模型, 针对不确定变量, 采用机会约束规划转换模型得到考虑模糊时间的多目标路径优化模型, 通过加权求和将多目标合并转化为单目标问题, 并设计人工蜂群算法求解所构建的班列服务路径优化模型, 并以盐田港海铁联运为实例进行了模型检验和对比分析。结果表明: (1)在硬时间窗约束下运输时间减少了88%, 但成本增加了97%, 充分表明了软时间窗设置的优势; (2)考虑不同的运输目标时, 只考虑运输费用时, 运输时间增加了5.3%;只考虑运输时间时, 运输费用增加了67.8%。所建模型和算法能够很好的满足不同客户不同运输时效性的需求, 在运输费用方面具有明显的优越性。Abstract: Since uncertain factors are affecting the operation of container trains in the process of sea-rail intermodaltransportation.Combined with the customers’ demand for a fixed time window , the uncertain planning interval is introduced to represent the range of time in container loading and unloading at each customer node.Meanwhile,the demand time window with timeliness requirements is set as a soft constraint. The penalty function is integrated into theobjective function of the transportation cost as a penalty term. A reasonable penalty coefficient is selected to constructa multi-objective optimization model of the train service path combined with the low transportation cost and less transportation time. For uncertain variables,the chance-constrained programming transformation model is used to obtain amulti-objective path optimization model considering fuzzy time. Then, the multi-objective problem is transformed intoa single objective problem by weighted summation, and the artificial bee colony algorithm is designed to solve the constructed model.The results of sea-rail intermodal transportation in Yantian Port show that:① The transportation timeis reduced by 88% in the constraint of hard time windows, but the cost is increased by 97%,fully showing the advantage of soft time windows.② When only the transportation cost is considered,the transportation time increases by5.3%. When only the transportation time is considered , the transportation cost increases by 67.8%.These experimental results confirm that the proposed model reduces the transportation cost and meets the needs of different transportation timeliness of different customers.
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表 1 规划期内各场站的集装箱运输量和客户数
Table 1. Container transportation volume and number of customers at each station during the planning period
内陆集装箱场站 送箱需求客户/个 取箱需求客户/个 送箱需求(TEU) 取箱需求(TEU) 韶关 7 9 160 178 成都 8 9 200 224 武汉 8 9 104 114 
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表 2 50个客户的相关实验数据
Table 2. Relevant test data of 50 customers
序号 所属场站 运量 需求 需求时间窗 序号 所属场站 运量 需求 需求时间窗 1 成都 26 送 [13.85] 26 成都 24 取 [43, 115] 2 成都 30 送 [22, 94] 27 成都 14 取 [55, 127] 3 成都 29 送 [34, 106] 28 成都 23 取 [58.130] 4 成都 28 送 [40.112] 29 成都 32 取 [60.132] 5 成都 15 送 [47, 119] 30 成都 11 取 [67, 139] 6 成都 12 送 [55, 127] 31 成都 13 取 [68.140] 7 成都 21 送 [65, 137] 32 成都 13 取 [76.148] 8 武汉 25 送 [2, 74] 33 武汉 27 取 [32.104] 9 武汉 17 送 [12, 84] 34 武汉 22 取 [30, 102] 10 武汉 19 送 [41, 113] 35 武汉 16 取 [58, 130] 11 武汉 16 送 [46.118] 36 武汉 27 取 [90, 162] 12 武汉 27 送 [55, 127] 37 武汉 22 取 [92, 164] 13 武汉 18 送 [22.94] 38 武汉 取 26 [52, 124] 14 武汉 32 送 [26, 98] 39 武汉 21 取 [61, 133] 15 武汉 34 送 [47, 119] 40 武汉 27 取 [72.144] 16 韶关 30 送 [5.77] 41 武汉 20 取 [74.146] 17 韶关 27 送 [10, 82] 42 韶关 40 取 [32.104] 18 韶关 12 送 [79, 151] 43 韶关 40 取 [41, 113] 19 韶关 13 送 [84, 156] 44 韶关 15 取 [43, 115] 20 韶关 17 送 [84, 156] 45 韶关 22 取 [56, 128] 21 韶关 31 送 [87, 159] 46 韶关 12 取 [57, 129] 22 韶关 30 送 [96.168] 47 韶关 36 取 [74, 146] 23 韶关 30 送 [25, 97] 48 韶关 13 取 [89.161] 24 成都 24 送 [28, 100] 49 韶关 36 取 [51, 123] 25 成都 18 取 [31.103] 50 韶关 15 取 [40, 112]
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表 3 盐田港服务班列的运输成本、服务时间
Table 3. Transportation cost and service time of service trains of Yantian Port
费用/元 总成本 启运费用 服务段费用 变动费用 惩罚成本 5769353 1600000 586000 3562500 20853 时间/h 总时间 运行时间 服务时间 等待时间 延误时间 6777 363 125 1435 4854
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表 4 不考虑优化算法的求解结果
Table 4. Results without considering the optimization algorithm
费用/元 总成本 启运费用 服务段费用 变动费用 惩罚成本 8181600 2000000 916560 5056600 20844 时间/h 总时间 运行时间 服务时间 等待时间 延误时间 7003 572 125 1459 4847
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表 5 只考虑运输费用的求解结果
Table 5. Results only considering transportation costs
费用/元 总成本 启运费用 服务段费用 变动费用 惩罚成本 7391800 2000000 851480 4326900 213420 时间/h 总时间 运行时间 服务时间 等待时间 延误时间 7137 530 125 1530 4952
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表 6 只考虑运输时间的求解结果
Table 6. Results only considering transportation time
费用/元 总成本 启运费用 服务段费用 变动费用 惩罚成本 9683000 1800000 960360 6762400 160240 时间/h 总时间 运行时间 服务时间 等待时间 延误时间 5347 600 125 821 3801
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表 7 硬时间窗需求下求解结果
Table 7. The results are solved under the requirement of hard time window
费用/元 总成本 启运费用 服务段费用 变动费用 惩罚成本 11563741 2800000 998321 7765420 0 时间/h 总时间 运行时间 服务时间 等待时间 延误时间 812 688 125 0 0
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[1] 齐颖秀, 郎茂祥, 董楠, 等. 集装箱海铁联运全程物流服务发展对策研究[J]. 铁道运输与经济, 2019, 41 (5): 58-62. https://www.cnki.com.cn/Article/CJFDTOTAL-TDYS201905014.htmQI Yingxiu, LANG Maoxiang, DONG Nan, et al. Research on the development strategy of container sea rail intermodal logistics service[J]. Railway Transportation and Economy, 2019, 41 (5): 58-62. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDYS201905014.htm [2] 刘璘, 朱小林. 考虑总成本的冷藏集装箱海铁联运运输路径优化[J]. 华中师范大学学报(自然科学版), 2017, 51 (4): 504-509. https://www.cnki.com.cn/Article/CJFDTOTAL-HZSZ201704014.htmLIU Lin, ZHU Xiaolin. Optimization of sea rail intermodal transportation path for refrigerated containers considering total cost[J]. Journal of Central China Normal University (Natural Science Edition), 2017, 51 (4): 504-509. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HZSZ201704014.htm [3] BHATTA-CHARYA A, KUMAR S A, TIWARI M K, et al. An intermodal freight transport system for optimal supply chain logistics[J]. Transportation Research Part C: Emerging Technologies, 2014, 38(1): 73 84. http://www.onacademic.com/detail/journal_1000036137923210_d646.html [4] 刘畅, 关秀婷, 张金伟, 等. 考虑时间价值成本的中欧笔记本电脑多式联运路径优化研究[J]. 铁道科学与工程学报, 2019, 16(9): 2352-2359. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201909030.htmLIU Chang, GUAN Xiuting, ZHANG Jinwei, et al. Study on route optimization of China-EU laptop multimodal transportation considering time value cost[J]. Journal of Railway Science and Engineering, 2019, 16 (9): 2352-2359. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201909030.htm [5] 刘丹, 赵嵩正. 可持续多式联运网络设计的多目标优化模型及算法[J]. 系统工程, 2015, 33(8): 133-139, https://www.cnki.com.cn/Article/CJFDTOTAL-GCXT201508020.htmLIU Dan, ZHAO Songzheng. Multi-objective optimization model and algorithm for sustainable multimodal transport network design[J]. System Engineering, 2015, 33 (8): 133-139. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCXT201508020.htm [6] 李玉民, 郭晓燕, 杨露. 考虑多目标的中欧集装箱多式联运路径选择[J]. 铁道科学与工程学报, 2017, 14(10): 2239-2248. U doi: 10.3969/j.issn.1672-7029.2017.10.027Yumin, GUO Xiaoyan, YANG Lu. Route selection of Ghina-Europe container multimodal transport considering multi-objectives[J], Journal of Railway Science and Engineering, 2017, 14(10): 2239-2248. (in Chinese) doi: 10.3969/j.issn.1672-7029.2017.10.027 [7] ZILIASKOPOULOS A, WARDELL W. An intermodal optimum path algorithm for multimodal networks with dynamic arc travel times and switching delays[J]. European Journal of Operational Research, 2000, 125(3) : 486-502. doi: 10.1016/S0377-2217(99)00388-4 [8] YANG H L, DONG F, DI L. Segmented procurement optimization of container multimodal transport service based on convergent combination[J]. Journal of Transportation Systems Engineering & Information Technology, 2014, 14(4): 17-22. [9] 户佐安, 孙燕, 薛锋. 基于海铁联运的港口集装箱运输集卡路径选择[J]. 交通运输工程与信息学报, 2020, 18(2): 68-74. doi: 10.3969/j.issn.1672-4747.2020.02.008HU zuoan, SUN Yan, XUE Feng. Container truck routing based on sea rail intermodal Transportation[J]. Joumal of Transportation Engineering and Information, 2020, 18(2): 68-74. (in Chinese) doi: 10.3969/j.issn.1672-4747.2020.02.008 [10] 靳志宏, 王小寒, 任刚, 等. 共享堆场协议下海铁联运集装箱堆场分配优化[J]. 中国航海, 2020, 43(3): 105-11L doi: 10.3969/j.issn.1000-4653.2020.03.019JIN Zhihong, WANG Xiaohan, REN Gang, et al. Optimization of container yard allocation for sea rail intermodal transportation under shared yard agreement[J]. China Navigation, 2020, 43 (3): 105-lll. (in Chinese) doi: 10.3969/j.issn.1000-4653.2020.03.019 [11] 武慧荣, 朱晓宁, 邓红星. 集装箱海铁联运港口混堆堆场箱区均衡分配模型[J]. 重庆交通大学学报(自然科学版), 2018, 37(4) : 109-115. doi: 10.3969/j.issn.1674-0696.2018.04.17WU Huirong, ZHU Xiaoning, DENG Hongxing. Equilibrium distribution model of container yard in container sea rail intermodal port[J]. Journal of Chongqing Jiaotong University (Natural Science Edition), 2018, 37 (4): 109-115. (in Chinese) doi: 10.3969/j.issn.1674-0696.2018.04.17 [12] GARCIA-ALONSO L, SANCHEZ-SORIANO J. Analysis of the evolution of the inland traffic distribution and provincial hinterland share of the Spanish port system[J]. Transport Reviews, 2010, 30(3): 275-297. doi: 10.1080/01441640902985983 [13] 殷亚, 张惠珍. 求解带硬时间窗的多目标车辆路径问题的多种混合蝙蝠算法[J]计算机应用研究, 2017, 34(12): 3632-3636. doi: 10.3969/j.issn.1001-3695.2017.12.025YIN Ya, ZHANG Huizhen. Multiple hybrid bat algorithms for solving multi-objective vehicle routing problem with hard time Avindows[J]. Computer Application Research, 2017, 34(12): 3632-3636. (in Chinese) doi: 10.3969/j.issn.1001-3695.2017.12.025 [14] CETIN S, A heuristic algorithm for vehicle routing problems with simultaneous pick-up and delivery and hard time windows[J]. Open Journal of Social Sciences, 2015, 3(3): 35-41. doi: 10.4236/jss.2015.33008 [15] FAZAYELI S, EYDI A, KAMALADADI I N. Location-routing problem in multimodal transportation network with time windows and fuzzy demands: presenting a two-part genetic algorithm[J]. Computers & Industrial Engineering, 2018, 119 (5)〖233-246. [16] 汤银英, 戴炜东, 陈思. 考虑多节点时间窗差异的集装箱多式联运路径选择研究[J]. 交通运输工程与信息学报, 2020, 18(1): 34-42. doi: 10.3969/j.issn.1672-4747.2020.01.005TANG Yinying, DAI Weidong, CHEN Si. Research on container multimodal transport route selection considering the difference of multi node time window[J]. Journal of Transportation Engineering and Information, 2020, 18 (1): 34-42. (in Chinese) doi: 10.3969/j.issn.1672-4747.2020.01.005 [17] 刘宝碇, 赵瑞清, 王纲. 不确定规划及应用[M]. 北京: 清华大学出版社, 2003.LIU Baoding, ZHAO Ruiqing, WANG Gang. Uncertain planning and application[M]. Beijing: Tsinghua University Press, 2003. (in Chinese) [18] 江铭炎, 袁东风. 人工蜂群算法及其应用[M]. 北京: 科学出版社, 2014JIANG Mingyan, YUAN Dongfeng. Artificial bee colony algorithm and its application[M]. Beijing: Science Press, 2014. (in Chinese) [19] 王梦楠. 基于蚁群算法的集装箱港口海铁联运班列服务网络优化[D]. 大连: 大连海事大学, 2018.WANG Mengnan. Optimization of container port sea-rail combined transport service network based on ant colony algorithm[D]. Dalian: DaIian Maritime University, 2018. (in Chinese) -
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