An Operation Scheme for Regular Train Services for Transporting Containers Considering Carbon Emission Cost
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摘要: 针对我国目前快递公路运输方式占比过高，而导致的道路交通需求过大、运输成本、碳排放过高等问题，研究了“双碳”目标下铁路集装箱快递班列方案。考虑运输距离、快递量、快递网点数量、物流产业占GDP比重等因素，运用熵权法确定集装箱快递班列始发站和到达站。根据公路直达运输和调运至铁路车站2种形式，构建包括始发站、调运站和到达站的铁路集装箱快递班列运输网络。为确定集装箱快递运输直达方案、调运方案及采用的运输方式，建立了铁路集装箱快递班列开行方案的整数规划模型。为确定集装箱快递班列合理的列车编组数量及铁路经济运距，该模型以运输成本、调运成本和碳排放成本最小为目标，不仅综合了快递运量、时间约束以及列车开行条件等因素，而且考虑了快递货物的调运流程。此外，该模型还引入了碳排放系数、碳交易价格等要素，以计算碳排放成本。以长三角地区快递货流集散为例进行了实证分析，结果表明：铁路集装箱快递班列开行方案以直达运输为主，调运方式为辅；运输方式按照载货量及铁路经济运距划分铁路运输为主，公路运输为辅；列车合理编组范围为25~40辆，且列车编组数量过高和过低均不具有优势；设定铁路运输速度120 km/h的条件下，铁路经济运距以400 km为宜；科学设计时间窗约束亦能优化铁路集装箱快递班列开行方案。与现行公路运输相比，本研究所得方案的运输成本和碳排放成本均明显降低，运输时效性亦能够得到保证。Abstract: Currently, a high proportion of the express delivery service in China is carried out through road transportation, which has led to the following issues, including an excessive traffic demand, a high transportation cost, and high carbon emission. Aiming to address these issues, an operation scheme for regular train services for transporting containers (RTS-TC) under"dual carbon"goals is studied. Considering the factors such as transportation distance, express delivery volume, the number of express delivery outlets, and the contribution of the logistics industry to overall GDP, an entropy weighting method is employed to determine the origin and destination stations for RTS-TC. Based on two types of transportation, highway transit only and transfer to railway stations, a transportation network for RTS-CT is developed, including the origins, transfers, and arrival stations. To determine the scheme for direct transit, the transfer scheme, and the corresponding transportation mode, an integer programming model is developed for the operation scheme of RTS-TC. To determine the reasonable number of RTS-TC formations and the railway economic distance, the model minimizes the transportation costs, transfer costs, and carbon emission costs. The optimization considers the factors such as the express delivery volume, time constraints, and train operation conditions, as well as the transfer process of express goods. Additionally, the model incorporates elements such as the carbon emission coefficient and carbon trading prices to calculate the carbon emission costs. A case study is conducted using the express freight flow distribution in the Yangtze River Delta region. The results show that the operation scheme for RTS-TC primarily adopts the mode of direct transportation with the transfer mode as a secondary option. According to the cargo capacity and railway economic distance, the railway transportation is preferred over the road transportation. The reasonable number of wagons for RTS-TC formations is between 25 and 40, as an excessively large or small number of RTS-TC formations is not advantageous. Under the condition of a railway speed of 120 km/h, a railway economic distance of 400 km is considered optimal. The constraint of a scientifically designed time window can also further optimize the operation scheme for RTS-TC. Compared to the current road transportation, the proposed schemes in this research significantly reduce the transportation costs and the carbon emission costs, while ensuring the transportation timeliness.
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表 1 2020年中国快递业务概况
Table 1. Overview of China's express business in 2020
快递业务指标 指标值 快递业务量/亿件 830 快递业务收入/亿元 8 750 公路 70~75 快递企业干线运输方式占比/% 航空 25~29 铁路 ＜1 注：数据资料来源于国家邮政局。 
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表 2 相关参数
Table 2. Relevant parameters
符号 含义 集合 $I $ 快递班列始发站点集合，i ∈ I $J $ 快递班列到达站点集合，j ∈ J $K $ 快递班列调运站点集合，k ∈ K 参数 $c_r $ 单位铁路运输成本/（元/TEU） $c_w $ 单位公路运输成本/（元/TEU） $Q_{i j} $ 始发站i至到达站j的快递运量/TEU，i ∈ I，j ∈ J $q_{i j} $ 始发站i至到达站j未发出的快递余量/TEU，i ∈ I，j ∈ J $q_{i k j} $ 始发站i至到达站j经过调运站k调运的快递量/TEU，i ∈ I，k ∈ K，j ∈ J $a_{k j} $ 发生调运后未被运出的快递余量/TEU，i ∈ I，j ∈ J $\beta_r $ 铁路碳排放参数/[kg/（TEU·km）] $\beta_w $ 公路碳排放参数/[kg/（TEU·km）] $p $ 碳交易价格/元 $T_j $ 运至到达站j的所有快递的时间窗约束/h $n_{i j} $ 非负整数，始发站i至到达站j开行满载列车的数量，i ∈ I，j ∈ J $m_{i j}$ 非负整数，始发站 i 至到达站 j 开行满足最低发车标准的列车的数量，i ∈ I， j ∈ J $u_{k j} $ 非负整数，调运站k至到达站j开行满载列车的数量，k ∈ K，j ∈ J $s_{k j} $ 非负整数，调运站k至到达站j开行满足最低发车标准的列车的数量，k ∈ K，j ∈ J $t_{i j}^r， t_{i j}^w $ 始发站i至到达站j采用铁路或公路运输的单位运输时间/（h/TEU） $t_{i k}^r， t_{i k}^w $ 始发站i至到达站k采用铁路或公路运输的单位运输时间/（h/TEU） $t_{k j}^r， t_{k j}^w $ 始发站k至到达站j采用铁路或公路运输的单位运输时间/（h/TEU） $t_r， t_w $ 单位运量在铁路或公路运输中的换装时间/（h/TEU） 决策变量 $ R_{i k} $ 始发站i至调运站k的快递是否通过铁路运输，若采用，Rik = 1；否则，Rik = 0，i ∈ I，k ∈ K $W_{i k} $ 始发站i至调运站k的快递是否通过公路运输，若采用，Wik = 1；否则，Wik = 0，i ∈ I，k ∈ K $\lambda_1， \lambda_2 $ 运输成本和碳排放成本的目标权重 $q_{\max } $ 单位列车最大编组载货量/TEU $q_{\min }$ 单位列车最小编组载货量/TEU
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表 3 始发站指标权重
Table 3. Index weight of origin station
省市 快递量 铁路里程 快递网点数量 物流产业占GDP比重 安徽省 0.3960 0.1362 0.1962 0.2716 江苏省 0.2144 0.1799 0.2809 0.3247 浙江省 0.3847 0.1870 0.2122 0.2162 上海市 0.4711 0.1173 0.2409 0.1708
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表 4 始发站备选城市评分
Table 4. Alternative city score of origin station
省市 城市评分排名 排名最高城市/行政区 1 2 3 4 5 安徽省 1 0.2237 0.1777 0.0826 0.0793 合肥 江苏省 0.8108 0.6842 0.5701 0.3093 0 南京 浙江省 0.8528 0.3750 0.3198 0.2408 0.1729 金华 上海市 0.6027 0.5412 0.5311 0.4840 0.3502 嘉定区
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表 5 到达站指标权重
Table 5. Index weight of arrival station
省市 快递量 铁路里程 快递网点数量 物流产业占GDP比重 上海 0.2098 0.1641 0.3869 0.2391 南京 0.3054 0.1856 0.3146 0.1944 金华 0.2693 0.1410 0.3645 0.2252 合肥 0.2641 0.1757 0.3463 0.2140
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表 6 到达站备选城市评分
Table 6. Alternative city score of arrival station
省市 上海 南京 金华 合肥 综合评分 武汉 0.5380 0.4305 0.3679 0.4144 1.7508 北京 0.9302 0.9107 0.9180 0.9020 3.6609 广州 0.6246 0.5261 0.6648 0.5762 2.3917 成都 0.2887 0.2935 0.3847 0.3231 1.2900 长沙 0.1978 0.1509 0.1611 0.1634 0.6732 郑州 0.2168 0.2245 0.2137 0.2310 0.8860 青岛 0.3192 0.2110 0.1392 0.1245 0.7939 沈阳 0.0797 0.0668 0.0691 0.0712 0.2868 西安 0.1832 0.1604 0.1967 0.1853 0.7256
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表 7 指标权重设计
Table 7. The design of index weights
方案编号 运输成本权重 碳排放成本权重 总成本/元 1 0.1 0.9 36771.24 2 0.3 0.7 76208.16 3 0.5 0.5 115457.2 4 0.7 0.3 154706.3 5 0.9 0.1 199237.6
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表 8 直达运输路径最优解
Table 8. Optimal solution for direct transportation path
方案编号 始发站 到达站 运输方式 货运量/TEU 铁路班列编组/辆 运输成本/元 碳排放成本/元 时间$/ \mathrm{h}$ 1 上海 武汉 铁路 50 25 2556.45 567.7 9.93 2 金华 北京 铁路 160 $40 \times 2$ 13915.20 3875.20 23.09 3 金华 广州 铁路 148 $40+34$ 11359.30 3041.70 20.3
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表 9 始发站—调运站路径最优解
Table 9. Optimal solution for the path from the origins to transfer stations
方案编号 始发站 调运站 运输方式 货运量/TEU 到达站 铁路调运班列编组/辆 运输成本/元 碳排放成本/元 时间/h 1 南京 合肥 公路 7 武汉 2242.80 52.33 3.32 2 合肥 上海 铁路 24 北京 12 936.00 155.90 5.31 3 合肥 金华 铁路 2 北京 1 88.30 17.70 6.04 4 南京 金华 铁路 14 北京 7 609.34 120.74 6.50 5 上海 合肥 铁路 33 广州 17 1287.00 214.37 6.19 6 南京 合肥 公路 9 广州 2883.60 67.28 3.42
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表 10 调运站—到达站路径最优解
Table 10. Optimal solution for the path from the transfers to arrival stations
方案编号 调运站 到达站 运输方式 货运量/TEU 铁路班列编组/辆 运输成本/元 碳排放成本/元 时间$/ \mathrm{h}$ 1 合肥 武汉 公路 20 14004.00 326.76 7.03 2 上海 北京 铁路 73 37 5204.32 1357.22 15.39 3 金华 北京 铁路 50 25 4348.50 1211.00 17.59 4 合肥 广州 铁路 59 30 4351.19 1148.97 15.21
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表 11 全程公路运输最优方案
Table 11. Optimal solution for road-only transportation
方案编号 始发站 到达站 货运量/TEU 运输成本/元 碳排放成本/元 运输时间/h 1 南京 武汉 7 6501.60 151.70 8.60 2 合肥 武汉 13 9102.60 212.39 6.48 3 上海 武汉 50 72990.00 1703.10 13.52 4 南京 北京 14 29282.40 29282.40 19.37 5 合肥 北京 26 51901.20 1211.03 18.48 6 上海 北京 49 117129.60 2733.02 22.13 7 金华 北京 194 604116.00 14096.04 28.83 8 南京 广州 9 29208.60 681.53 30.05 9 合肥 广州 17 42564.60 993.17 23.18 10 上海 广州 33 91000.80 2123.35 25.53 11 金华 广州 148 391075.20 9125.09 24.47
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