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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