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Decision framework for location and selection of container multimodal hubs: A case in china under the belt and road initiative

  • * Corresponding author: Jing Lu

    * Corresponding author: Jing Lu 

The first author is supported by National Natural Science Foundation grant No.71473023, National Social Science Fund of China grant No.19VHQ012 and No.18VHQ005, Fundamental Research Funds for the Central Universities grant No.3132019159, Ministry of Education Foundation of Humanities and Social Science grant No.16YJAZH030

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  • The location and selection of logistics nodes that facilitate the China Railway Express and rail-sea intermodal transportation has received increasing attention in China under the Belt and Road Initiative. The objective is to solve problems caused by the increasing number of origin cities opening international trains, such as disorderly competition, insufficient cargoes and low overall coordination. This study screens 22 cities as candidate Chinese international container multimodal hubs (CICMHs) in consideration of the actual situation of China's trade transportation. Thirteen indicators are screened using the information contribution rate-information substitutability method. Then, a comprehensive evaluation model is proposed to evaluate the candidate CICMHs and rank them. The model is based on the extended grey relational analysis-technique for order preference similar to ideal solution in combination with prospect theory. Chongqing, Guangzhou, Shanghai, Wuhan, Chengdu, Xi'an, Nanjing, Tianjin, Zhengzhou and Dalian are selected as the CICMHs. Moreover, a sensitivity analysis of the index weight fluctuations and decision-makers' preference and a comparative analysis of different decision-making methods are performed. The robustness and stability of the proposed model are demonstrated. This study can support the location and selection of CICMHs and expand the methods and applications in the decision-making field.

    Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

    Citation:

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  • Figure 1.  Value function

    Figure 2.  Location decision framework for CICMHs

    Figure 3.  Transport networks in China

    Figure 4.  Preliminary evaluation index system

    Figure 5.  Final evaluation index system

    Figure 6.  Results of the location evaluation of CICMHs

    Figure 7.  Sensitivity analysis of the sub-criteria in logistics trade scale and service quality of hub stations

    Figure 8.  Sensitivity analysis of the sub-criteria in transport location advantages

    Figure 9.  Sensitivity analysis of the sub-criteria in socio-economic support

    Figure 10.  Sensitivity analysis of the sub-criteria in urban infrastructure

    Figure 11.  Sensitivity analysis of different $ \theta $

    Figure 12.  Sensitivity analysis of the preference coefficient

    Figure 13.  Scatter diagrams of evaluation values and serial numbers

    Table 1.  Advantages and limitations of MCDM methods

    Methods Advantages Limitations
    AHP Flexible, concise, easy to calculate Does not consider the interaction between indicators
    TOPSIS Can measure the proximity between alternative and ideal solutions Cannot distinguish vertical line nodes in positive and negative ideal solutions
    GRA Can measure the similarity degree of curve shapes between sequences Does not consider the closeness of the sequence and the directionality of the numerical value
    ELECTRE Applicable when incomparable substitutes exist Needs many parameters defined by decision-makers, and the calculation process is cumbersome
    PROMETHEE Can avoid information loss by focusing on various properties of attributes Fails to consider DMí »s risk aversion psychology
    VIKOR Considers the maximum benefit of the group and minimum regret of the individual The evaluation of uncertainty factors is unsatisfactory
    TODIM Can reflect DMí»s evasion of losses and risk psychology Regression coefficient is not clearly defined, and evaluation results vary with the DMs
    DEMATEL Can avoid consistency errors due to too many comparison times Cannot reflect the fuzziness of decision information and the subjectivity of DMs
    BWM Can reduce the number of comparisons and provide comparisons with consistency, leading to reliable results Needs to be scored by experts with certain subjectivity; DMsí» confidence in their comparisons is not considered
     | Show Table
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    Table 2.  Weight of the evaluation index

    Index A1 A2 A3 A4 A5 B1 B2 B3 C1 C2 C3 D1 D2
    Weight 0.0888 0.1615 0.1163 0.0998 0.0554 0.03 0.1037 0.0237 0.1163 0.0349 0.0348 0.0701 0.0647
     | Show Table
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    Table 3.  Weight of the evaluation index

    Node $ D_i^+ $ $ D_i^- $ $ Q_i^+ $ $ Q_i^- $ $ C_i $ Ranking
    Chongqing 0.0681 1.0000 1.0000 0.8495 0.6855 1
    Chengdu 0.3259 0.7244 0.9498 0.8952 0.5783 5
    Zhengzhou 0.3362 0.5597 0.9166 0.9241 0.5395 9
    Xi'an 0.3269 0.6633 0.9347 0.9069 0.5643 6
    Suzhou 0.55 0.6076 0.9219 0.9297 0.5083 13
    Wuhan 0.1838 0.6908 0.9257 0.9024 0.5981 4
    Changsha 0.3888 0.4129 0.888 0.9572 0.4915 14
    Hefei 0.5098 0.4152 0.8817 0.9553 0.4696 18
    Lanzhou 0.3519 0.3349 0.8713 0.9851 0.4743 17
    Shenyang 1.0000 0.2845 0.858 1.0000 0.3636 22
    Harbin 0.6944 0.3196 0.8673 0.9899 0.4134 21
    Nanjing 0.2759 0.5634 0.9055 0.9244 0.5503 7
    Hangzhou 0.43 0.5315 0.9036 0.9367 0.5122 11
    Nanning 0.6054 0.3407 0.8685 0.9785 0.4329 20
    Urumqi 0.5853 0.3767 0.8791 0.993 0.4431 19
    Shanghai 0.2839 0.8857 0.9707 0.8955 0.6115 3
    Ningbo 0.3697 0.4831 0.8877 0.9444 0.5106 12
    Guangzhou 0.2633 0.883 0.9686 0.8883 0.6166 2
    Tianjin 0.3155 0.5932 0.9154 0.9222 0.5493 8
    Qingdao 0.4115 0.4219 0.8826 0.9526 0.4888 15
    Dalian 0.4104 0.545 0.896 0.9515 0.5141 10
    Xiamen 0.4029 0.3858 0.8699 0.9767 0.4765 16
     | Show Table
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    Table 4.  Ranking orders with different $ \theta $

    $ \theta $ L0 L1 L2 L3 L4 L5 L6 L7 L8 L9 Ranking
    1 0.6865 0.5813 0.5441 0.5677 0.6011 0.5544 0.6096 0.6152 0.5529 0.5143 L0>L7>L6>L4>L1>L3>L5>L8>L2>L9
    1.5 0.6861 0.5801 0.5423 0.5664 0.5999 0.5529 0.6103 0.6157 0.5515 0.5141 L0>L7>L6>L4>L1>L3>L5>L8>L2>L9
    2 0.6857 0.5789 0.5405 0.565 0.5987 0.5512 0.6111 0.6162 0.5501 0.514 L0>L7>L6>L4>L1>L3>L5>L8>L2>L9
    2.25 0.6855 0.5783 0.5395 0.5643 0.5981 0.5503 0.6115 0.6166 0.5493 0.5141 L0>L7>L6>L4>L1>L3>L5>L8>L2>L9
    2.5 0.6853 0.5776 0.5384 0.5636 0.5975 0.5494 0.612 0.6169 0.5485 0.5142 L0>L7>L6>L4>L1>L3>L5>L8>L2>L9
    3 0.6847 0.5761 0.5363 0.562 0.5961 0.5475 0.6132 0.6178 0.5469 0.5146 L0>L7>L6>L4>L1>L3>L5>L8>L2>L9
    3.5 0.6842 0.5746 0.534 0.5603 0.5946 0.5455 0.6146 0.6189 0.5451 0.5154 L0>L7>L6>L4>L1>L3>L5>L8>L2>L9
    4 0.6835 0.5729 0.5316 0.5584 0.593 0.5432 0.6163 0.6204 0.5432 0.5168 L0>L7>L6>L4>L1>L3>L5>L8>L2>L9
     | Show Table
    DownLoad: CSV

    Table 5.  Ranking results for different methods

    Cities TOPSIS TODIM GRA-TOPSIS eGRA-TOPSIS
    Value Ranking Value Ranking Value Ranking Value Ranking
    Chongqing 0.6877 1 0.5801 1 0.6278 1 0.6855 1
    Chengdu 0.5324 4 0.5102 6 0.5111 5 0.5783 5
    Zhengzhou 0.3927 10 0.4978 7 0.4512 10 0.5395 9
    Xi'an 0.4621 6 0.5154 5 0.4913 6 0.5643 6
    Wuhan 0.4906 5 0.5195 4 0.5147 4 0.5981 4
    Nanjing 0.415 8 0.4858 9 0.457 9 0.5503 7
    Shanghai 0.6169 3 0.5382 3 0.5875 3 0.6115 3
    Guangzhou 0.6415 2 0.5411 2 0.5967 2 0.6166 2
    Tianjin 0.4366 7 0.4959 8 0.4696 7 0.5493 8
    Dalian 0.4058 9 0.4676 10 0.466 8 0.5141 10
     | Show Table
    DownLoad: CSV

    Table 6.  Weight of the evaluation index

    Method TOPSIS TODIM GRA-TOPSIS eGRA-TOPSIS
    Discrimination 1.0422 1.0775 1.0922 1.0817
     | Show Table
    DownLoad: CSV
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