Comprehensive evaluation method for logistics service supply chain toughness level approaching ideal solution based on ISM-ANP-CRITIC

Abstract

The invention discloses a port logistics service supply chain toughness level approximate ideal solution comprehensive evaluation method based on an ISM-ANP-CRITIC, and the method specifically comprises the following steps: firstly, constructing a port logistics service supply chain toughness evaluation index system under an emergency, analyzing the incidence relation between indexes through employing an ISM method, and dividing the indexes into four levels; secondly, determining the weight of each index by using an ANP-CRITIC combined weighting method, and analyzing to obtain that the weights occupied by the three indexes including the risk management capability, the cooperation capability and the staff quality in the six first-level indexes are relatively large; thirdly, evaluating the logistics service supply chain toughness of the five port groups of the Z enterprise by using an approximate ideal solution comprehensive evaluation method, and analyzing and finding that the toughness capacities of the five port groups are all in a general level; and finally, suggestions are provided for improving the toughness of the port logistics service supply chain from five aspects of the port emergency capability, the staff quality, the big data analysis capability, the infrastructure guarantee capability and the cooperation capability. According to the invention, a comprehensive evaluation method standard for the toughness level of the port logistics service supply chain approaching an ideal solution is constructed, so that the toughness level evaluation of the port logistics service supply chain is realized, and the toughness of the port logistics service supply chain can be improved based on an evaluation result; and a scientific basis is provided for related decisions of port enterprises for improving logistics service supply chain toughness.

Description

Technical Field

[0001] The present invention relates to the field of port logistics service supply chain resilience level assessment, and specifically to a port logistics service supply chain resilience level approximate ideal solution comprehensive assessment method based on ISM-ANP-CRITIC. Background Art

[0002] In recent years, economic globalization has accelerated, and my country's port logistics industry has developed rapidly. In 2022, my country's port container throughput was 295.87 million TEUs, an increase of 4.7% compared with last year, and the foreign trade cargo transportation volume of port logistics accounted for more than 85%. As an important hub for transportation, the development of ports has received great attention, and the intelligentization and specialization of ports have achieved remarkable results. my country's port groups have integrated logistics service functions and are developing in the direction of supply chain services. Competition between ports has gradually transformed into competition for the port logistics service supply chain in which the ports are located.

[0003] While the port logistics service supply chain is developing rapidly, emergencies such as fire and explosion, flood, typhoon, public health incidents, snowstorm and so on occur frequently, which affect the normal operation of the port logistics service supply chain. Port enterprises are the core of the port logistics service supply chain and an important transportation hub in coastal areas. They are more vulnerable to the threat of emergencies due to their unique geographical location and important economic functions. The port logistics service supply chain has many and complex nodes. Once impacted by emergencies, it will not only affect itself, but also affect other industries through capital flow, information flow and product flow, forming a chain reaction. Under the action of bullwhip effect and anti-bullwhip effect, the impact will be amplified. If it is not controlled, it will have a negative impact on the entire national economy.

[0004] The impact of emergencies on the logistics service supply chain is lasting and far-reaching. If the nodes in the supply chain deviate as a result, the entire supply chain network and related industries will be unbalanced. Therefore, the task of improving the resilience of the port logistics service supply chain is urgent. A highly resilient supply chain has the ability to withstand the impact of emergencies and recover quickly from them. However, the frequent occurrence of emergencies continues to expose the problem of insufficient risk management in the port logistics service supply chain, such as imperfect emergency infrastructure, an imperfect emergency system, and chaotic safety supervision. Based on this, the present invention designs a comprehensive evaluation method for the resilience level of the port logistics service supply chain based on ISM-ANP-CRITIC, which is close to the ideal solution. Summary of the invention

[0005] 1. A comprehensive evaluation method for the resilience level of logistics service supply chain approaching the ideal solution based on ISM-ANP-CRITIC, characterized by comprising the following steps:

[0006] S1: Determine the factors affecting the resilience of the port logistics service supply chain and construct an evaluation index system for the resilience level of the port logistics service supply chain, including primary indicators and secondary indicators. The primary indicators include: emergency response capability, staff quality, big data analysis capability, risk management capability, infrastructure support capability, collaboration capability, etc. A primary indicator system for the resilience of the port logistics service supply chain has been established, with a total of 6 primary indicators.

[0007] S2: The set of secondary indicators belonging to the i-th first-level indicator Ai is denoted as aij, which represents the j-th secondary indicator of the i-th first-level indicator Ai; mi represents the number of secondary indicators of the i-th first-level indicator Ai; including emergency organization system (A1), emergency operation mechanism (A2), guarantee system (A3), staff risk awareness (B1), operation standardization degree (B2), response capability (B3), digital integration of industrial chain nodes (C1), forecasting capability (C2), digital platform construction (C3), risk identification capability (D1), risk assessment capability (D2), risk control capability (D3), risk management innovation capability (D4), target berth number (E1), target handling capacity (E2), year-on-year growth of container throughput (E3), information sharing degree (F1), supply chain network reorganization capability (F2), and loyalty of cooperative enterprises (F3), a total of 19 secondary indicators.

[0008] S3: Determine the logical relationship between indicators through the data obtained from the expert scores in this field to establish a binary relationship matrix, and calculate the reachability matrix through software. The specific steps are as follows:

[0009] S3-1: Determine the binary relationship matrix. The binary relationship matrix is ​​used to determine the relationship between indicators and is established using the expert group discussion method. In the binary relationship matrix, "V", "A", "X", and "O" are used to represent the correlation between evaluation indicators. Among them, "V" is used to represent when the column indicator has a direct impact on the row indicator, "A" is used to represent when the row indicator has a direct impact on the column indicator, "X" is used to represent when the row and column indicators affect each other, and "O" is used to represent when there is no correlation between the row and column.

[0010] S3-2: Establish an adjacency matrix. In the adjacency matrix A, the evaluation indicators of the resilience of the port logistics service supply chain under emergencies are replaced by A1, A2, etc., which correspond to the emergency logistics network service capacity, facility equipment load capacity, etc. The adjacency matrix representing the association of elements is obtained based on the experience and logical judgment of experts. The element "0" in the adjacency matrix indicates that there is no influence between the indicators, and "1" indicates that there is an influence. Among them, if the indicator S i For index S j If there is an impact, then S ij The value of S is 1; if the index S i For index S jNo effect, then element S ij The value is 0

[64] .

[0011] S3-3: Calculate the reachability matrix. The value of the direct reachability relationship is obtained by the sum of the adjacency matrix and the unit matrix, and then calculated according to the Boolean operation rules. The reachability matrix of the evaluation index of the port logistics service supply chain resilience under emergencies can be calculated by formula (1), where I is the unit matrix and n is a natural number.

[0012] M=(A+I) k =(A+I) k-1 ≠(A+I) k-2 ≠…≠(A+I),(k≤n-1) (1)

[0013] S3-4: Divide the indicators into regions through the reachable matrix. Calculate the reachable set R, the predecessor set Q and the common set T, where T = R ∩ Q. The reachable set R represents all the indicators S i The set of all factors that affect S. The antecedent set represents all factors that directly or indirectly affect S. i The set of all factors.

[0014] S3-5: Based on the hierarchical division results of S3-4, an explanatory structural model of the resilience of the port logistics service supply chain under emergencies is constructed.

[0015] S4: Use the ANP-CRITIC method to perform combined weighting analysis on the network model established by ISM and calculate the weights of indicators at all levels; the specific steps are as follows:

[0016] S4-1: Based on the direct and indirect influence relationships obtained by the ISM model, the influence relationships of various indicators are input into the Super Decision software to construct the ANP network structure model.

[0017] S4-2: Construct a judgment matrix. According to the evaluation index relationship network structure diagram in the first step, the control layer element P i As a guideline, the network layer element C ij As a secondary criterion, group the elements C i The elements in C j The influence of the elements is compared indirectly. The nine-point method is used for scoring. The established matrix needs to pass the consistency test, and the consistency index CR value should be less than 0.1. The nine-point method is shown in Table 1.

[0018] Table 1 Description of the nine-degree method

[0019]

[0020]

[0021] S4-3: Calculate the supermatrix, weighted supermatrix and limit matrix. The weighted matrix is ​​obtained by judging the eigenvector of the normalized matrix. Taking the control layer as the criterion, the importance of each group of elements under the control layer to the criterion is compared to obtain the weighted matrix. The weighted supermatrix is ​​obtained by multiplying the weighted matrix and the supermatrix. After the comparison is completed in the Super Decision software, the supermatrix, the normalized weighted supermatrix and the limit matrix will be automatically generated.

[0022] S4-4: Apply ANP to solve the weight. The weight of each element is obtained by summing up the extreme local relative weight according to the weight of each criterion. All the above steps are implemented by Super Decision.

[0023] S4-5: Apply the CRITIC method to calculate weights. First, the data is dimensionless. The properties and units of each indicator are different. In order to calculate and compare the data, the original data must be dimensionless. The processing methods for positive indicators and negative indicators are shown in Formula 2 and Formula 3 respectively.

[0024]

[0025] S4-6: Calculate the standard deviation of each evaluation indicator. The value of the standard deviation is the comparison strength of each indicator. The specific calculation formula is shown in Formula 4.

[0026]

[0027] in, is x j The average value of .

[0028] S4-7: Calculate the correlation coefficient r of the indicator ij and conflict C j , the larger the conflict value, the greater the influence of the evaluation index in the entire evaluation index system, and thus it will have a greater weight. The calculation process is shown in Formula 5 and Formula 6 respectively.

[0029]

[0030] S4-7: Calculate the final weight using the CRITIC method, see formula 7.

[0031]

[0032] S4-8: Calculate the combination weight

[0033] The weights calculated in step S4-4 and step S4-7 are combined, and the combined weight W is obtained by linear combination W j The calculation formula is shown in Formula 8.

[0034]

[0035] Among them, α is the subjective weight obtained by the ANP method, is the weight value of the jth indicator in ANP, is the weight value of the jth indicator in the CRITIC method. Minimize the sum of squares according to formula 8:

[0036]

[0037] Substitute formula 8 into formula 9 for derivation, set the derivative equal to 0, and the solution of α is 0.5. Therefore, the linear combination expression of the subjective and objective combination weights is:

[0038]

[0039] S5: Use the approximate ideal solution ranking method to determine the overall evaluation results of the port logistics service supply chain resilience. The specific steps are as follows: S5-1: Establish the initial evaluation matrix R x Assume there are n indicators, m evaluation objects, x ij is the jth indicator of the i-th evaluation object, where i=1,2,…,m; j=1,2,n,.

[0040] The initial evaluation matrix is ​​shown in formula 11:

[0041]

[0042] Further standardization is performed. Since the units and properties of the indicator data are different, dimensionless processing is required. The TOPSIS method involves directional issues, so the square sum normalization method is used for standardization. The standardized matrix R y The calculation formula is shown in Formula 12, where i = 1, 2,…, m; j = 1, 2, n,.

[0043]

[0044] Calculate the weighted normalization matrix V,v ij The calculation method is shown in Formula 14, where i = 1, 2, ..., m; j = 1, 2, ..., n.

[0045]

[0046] v ij =w j *y ij (14)

[0047] Calculate the positive ideal solution V + and negative ideal solution V - :

[0048]

[0049]

[0050] Calculate the distance to the ideal solution and

[0051]

[0052] Calculate the relative progress E i . Calculation formula for relative posting progress 19:

[0053]

[0054] S5-3: Sort the evaluation objects. i The values ​​of are used as the toughness coefficients for sorting. The greater the relative closeness, the higher the ranking, and the higher the toughness level.

[0055] 2. According to the ISM-ANP-CRITIC-based comprehensive evaluation method for the resilience level of the port logistics service supply chain approaching the ideal solution of claim 1, it is characterized in that in step S3, the ISM method is used to model and analyze the constructed port logistics service supply chain resilience level evaluation index system.

[0056] 3. According to the method for comprehensive evaluation of the resilience level of the port logistics service supply chain based on ISM-ANP-CRITIC and approaching the ideal solution of claim 1, it is characterized in that in step S4, the ANP-CRITIC method is used to combine and weight the constructed port logistics service supply chain resilience level evaluation index system, and the weight is obtained after consistency test.

[0057] 4. According to the method for comprehensive evaluation of the resilience level of the port logistics service supply chain based on ISM-ANP-CRITIC and close to the ideal solution of claim 1, it is characterized in that in step S5, the overall evaluation result is determined according to the evaluation index system by using the close to the ideal solution method, so that the evaluation result is more intuitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below in conjunction with the accompanying drawings, wherein

[0059] Figure 1 It is a method flow framework diagram in a specific embodiment of the present invention;

[0060] Figure 2 Explanation of the structural model diagram

[0061] Figure 3 ANP indicator weight diagram DETAILED DESCRIPTION

[0062] The present invention illustrates the implementation of the present invention through specific examples, and those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other example implementations, and the details in this specification can also be modified in various ways based on different viewpoints and applications. The technical solution of the present invention is further introduced below in conjunction with specific implementations and drawings.

[0063] This paper proposes a comprehensive evaluation method for the resilience level of port logistics service supply chain based on ISM-ANP-CRITIC, which is close to the ideal solution. Figure 1 The specific algorithm is described as follows:

[0064] S1: Determine the factors affecting the resilience of the port logistics service supply chain and construct an evaluation index system for the resilience level of the port logistics service supply chain, including primary indicators and secondary indicators. The primary indicators include: emergency response capability, staff quality, big data analysis capability, risk management capability, infrastructure support capability, collaboration capability, etc. A primary indicator system for the resilience of the port logistics service supply chain has been established, with a total of 6 primary indicators.

[0065] S2: The secondary indicator set belonging to the i-th primary indicator Ai is recorded as aij, which represents the j-th secondary indicator of the i-th primary indicator Ai; mi represents the number of secondary indicators of the i-th primary indicator Ai; including emergency organization system (A1), emergency operation mechanism (A2), guarantee system (A3), risk awareness of staff (B1), degree of standardization of operations (B2), response ability (B3), digital integration of industrial chain nodes (C1), prediction ability (C2), digital platform construction (C3), risk identification ability (D1), risk assessment ability (D2), risk control ability (D3), risk management innovation ability (D4), target berth number (E1), target processing capacity (E2), year-on-year growth of container throughput (E3), information sharing degree (F1), supply chain network reorganization ability (F2), and loyalty of cooperative enterprises (F3). A total of 19 secondary indicators. As shown in Table 2:

[0066] Table 2 Port logistics service supply chain resilience index system

[0067]

[0068]

[0069] S3: Determine the logical relationship between indicators through the data obtained from the expert scores in this field to establish a binary relationship matrix, calculate the reachability matrix through software, and establish a network relationship diagram. The specific steps are as follows;

[0070] The specific steps are as follows;

[0071] S3-1: Determine the binary relationship table. The expert group discussion method was used to establish it. The expert group members consisted of five supply chain experts and five port logistics enterprise managers. After three rounds of discussion and sorting, a consensus was finally reached. In the binary relationship matrix, when the column index has a direct impact on the row index, it is represented by "V", when the row index has a direct impact on the column index, it is represented by "A", when the row and column indicators affect each other, it is represented by "X", and when there is no relationship between the row and column, it is represented by "O". The final binary relationship table is shown in Table 3.

[0072] Table 3 Binary relationship table

[0073]

[0074]

[0075] S3-2: Determine the adjacency matrix. According to the binary relationship table, a 19×19 adjacency matrix A is obtained. Among them, when there is an influence relationship between the indicators, it is represented by "1", and when there is no influence relationship between the two indicators, it is represented by "0". The adjacency matrix obtained from the binary relationship table is shown in Table 4.

[0076] Table 4 Adjacency matrix

[0077]

[0078]

[0079] S3-3: The reachable matrix is ​​calculated by formula 1. The reachable matrix can represent the direct and indirect relationships between indicators. The reachable matrix is ​​obtained by the sum of the adjacency matrix and the unit matrix, and then calculated according to the Boolean operation rules. The reachable matrix of the evaluation index of the resilience of the port logistics service supply chain under emergencies can be calculated by formula 2-1, where "1" indicates that there is an influence relationship between the indicators, and "0" indicates that there is no influence relationship between the two indicators. The calculation results are shown in Table 5.

[0080] Table 5 Reachability matrix

[0081]

[0082] S3-4: Perform hierarchical decomposition based on the reachable matrix. The reachable set R, the antecedent set Q and the common set T are analyzed, where T = R ∩ Q. The reachable set R represents the set of all factors that can be affected by other indicators, and the antecedent set Q represents the set of all factors that directly or indirectly affect other indicators. Among them, "1", "2", "3", "4"... represent the first indicator, the second indicator, the third indicator... respectively. According to the hierarchical decomposition steps of the ISM method, the indicators of the first level can be obtained: guarantee system A3, response capability B3, risk control capability D3, supply chain network reorganization capability F2. The reachable set, antecedent set and common set of the indicators are shown in Table 6.

[0083] Table 6 Antecedent set, reachable set, and common set of indicators

[0084]

[0085] S3-5: According to the method description in the previous step, A3, B3, D3, and F2 are the first-level indicators, and the indicators at this level have no effect on other indicators. Then remove the first-level indicators from the table, repeat this step, and get the second-level indicators, the third-level indicators, and the bottom-level indicators in turn. The second-level indicators are: operation standardization degree B2, data prediction ability C2, risk identification ability D1, risk assessment ability D2, risk management innovation ability D4, and information sharing degree F1. The third-level indicators are staff risk awareness B1, digital integration of industrial chain nodes C1, digital platform construction C3, year-on-year growth of terminal throughput E3, and loyalty of cooperative enterprises F3. The fourth-level indicators are emergency organization system A1, emergency operation mechanism A2, target berth number E1, and target processing capacity E2. The final hierarchical division results are shown in Table 7.

[0086] Table 7 Hierarchical division results

[0087]

[0088] S3-6: Draw an explanatory structural model of the indicator association relationship based on the hierarchical division results, such as Figure 2 .

[0089] S4: Use the ANP-CRITIC method to perform combined weighting analysis on the network model established by ISM and calculate the weights of indicators at all levels; the specific steps are as follows:

[0090] S4-1: Based on the direct and indirect influence relationships obtained by the ISM model, the influence relationships of various indicators are input into the Super Decision software to construct the ANP network structure model.

[0091] S4-2: Construct a judgment matrix. According to the evaluation index relationship network structure diagram in the first step, the control layer element Pi As a guideline, the network layer element C ij As a secondary criterion, group the elements C i The elements in C j The influence of the elements is compared indirectly. The nine-point method is used for scoring. The established matrix needs to pass the consistency test, and the consistency index CR value should be less than 0.1.

[0092] S4-3: Calculate the supermatrix, weighted supermatrix and limit matrix. The weighted matrix is ​​obtained by judging the eigenvector of the normalized matrix. Taking the control layer as the criterion, the importance of each group of elements under the control layer to the criterion is compared to obtain the weighted matrix. The weighted supermatrix is ​​obtained by multiplying the weighted matrix and the supermatrix. After the comparison is completed in the Super Decision software, the supermatrix, the normalized weighted supermatrix and the limit matrix will be automatically generated.

[0093] S4-4: Apply ANP to solve the weight. The weight of each element is obtained by summing up the extreme local relative weight according to the weight of each criterion. All the above steps are implemented by Super Decision software. Based on the direct and indirect connections between indicators in the explanatory structural model diagram obtained by the ISM method, the network relationship between indicators is gradually established. On this basis, combined with expert opinions, the connection between indicators is enriched. Draw a network relationship diagram based on the determined relationship between indicators.

[0094] Based on the constructed ANP network relationship network structure, the weight of each indicator is determined. This paper uses an expert group discussion to determine the importance of indicators. The expert group members are composed of five supply chain experts and five port enterprise managers. After three rounds of discussion and sorting, a consensus was finally reached. The discussion results are input into the software and the pairwise comparison matrix is ​​checked for consistency. The pairwise comparison matrix between each dimension and each indicator is input into the software for calculation, and the unweighted matrix W and the weighted super matrix W can be obtained. And the extreme weighted super matrix. Finally, the weights of each indicator are generated, such as Figure 3 .

[0095] S4-5: Apply the CRITIC method to calculate weights. First, the data is dimensionless. The properties and units of each indicator are different. In order to calculate and compare the data, the original data must be dimensionless. The processing methods for positive indicators and negative indicators are shown in Formula 2 and Formula 3 respectively.

[0096]

[0097] S4-6: Calculate the standard deviation of each evaluation indicator. The value of the standard deviation is the comparison strength of each indicator. The specific calculation formula is shown in Formula 4.

[0098]

[0099] in, is x j The average value of .

[0100] S4-7: Calculate the correlation coefficient r of the indicator ij and conflict C j , the larger the conflict value, the greater the influence of the evaluation index in the entire evaluation index system, and thus it will have a greater weight. The calculation process is shown in Formula 5 and Formula 6 respectively.

[0101]

[0102] S4-8: Calculate the final weight using the CRITIC method, see formula 7.

[0103]

[0104] According to the above steps, the expert panel members of the ANP method are invited to score the importance of the indicators using a questionnaire survey method, and the scores are scored using a percentage system. The matrix obtained by scoring is dimensionless using steps 4-5, and the correlation coefficient and conflict value are calculated according to formulas 5 and 6. Finally, the objective weight is calculated using formula 7. The obtained weights are shown in Table 8.

[0105] Table 8 Objective weights based on CRITIC

[0106]

[0107]

[0108] S4-8: Calculate the combination weight

[0109] The subjective and objective weights are combined and calculated according to formula 10 to obtain the combined weights of the indicators. The combined weights of the indicators are shown in Table 9.

[0110] Table 9 Summary of indicator weight results

[0111]

[0112] S5: Use the approximate ideal solution ranking method to determine the overall evaluation results of the Z company's port logistics service supply chain resilience. The specific steps are as follows:

[0113] The resilience of the logistics service supply chain of Company Z in the five major port clusters along the coast of China was evaluated. The data of quantitative indicators were mainly collected through the company's official website and annual report, and the qualitative indicators were quantified through the fuzzy comprehensive evaluation method.

[0114] Table 10 Quantitative indicator data

[0115]

[0116] S5-1: Determine the factor set for evaluating the resilience of the port logistics service supply chain under emergencies: U = {u1,u2,…,u n}, the evaluation factors are the evaluation indicators, where n is the number of evaluators. Then determine the comment set: V = {V1, V2, ..., V m} = {very low, low, average, high, very high}, where m is the number of comments.

[0117] S5-2: Determine the fuzzy comprehensive judgment matrix. Invite 40 experts to score the resilience index, and the score is the original value of the qualitative index. The membership degree is calculated by formula 11 to obtain the membership matrix R, where N is the total number of scorers, N j is the number of experts who rated indicator i as comment j. The weight of each indicator is set to 1, and the weight matrix and the membership matrix are multiplied to obtain the comprehensive membership matrix, see Table 11.

[0118] Table 11 Comprehensive membership of indicators

[0119]

[0120]

[0121]

[0122] S5-3: Calculate the comprehensive score. According to the resilience level of the evaluation set, the scores are set from low to high as 1, 2, 3, 4 and 5 respectively. The comprehensive membership matrix and the comment set matrix are multiplied to obtain the comprehensive score, as shown in Table 12.

[0123] Table 12 Comprehensive score of indicators

[0124]

[0125] According to the data, the initial evaluation matrix can be obtained. Among them, the three quantitative indicators are all positive indicators. The data in Table 11 are standardized. The target berth number E1 is standardized as [0.025, 0.011, 0.006, 0.013, 0.009], the target handling capacity E2 is standardized as [0.033, 0.018, 0.010, 0.029, 0.016], and the terminal throughput year-on-year growth E3 is standardized as [0.005, 0.009, 0.025, 0.007, 0.]0. The qualitative indicators constructed in this paper are also positive indicators. Formula 2-12 is used to standardize the data in Table 4.10. Finally, the weighted standard matrix is ​​obtained, see Table 13.

[0126] According to the combined weights determined by ANP and CRITIC, the distance between the negative ideal value and the positive ideal value is calculated according to formulas 3-17 and 3-18 in 3.4.1, and the relative closeness of each port is calculated according to formula 3-19. The closer the relative closeness is to 1, the higher the resilience level of the port logistics service supply chain. The specific evaluation results calculated by TOPSIS are shown in Table 14.

[0127] Table 13 Weighted Standard Matrix

[0128]

[0129]

[0130] Table 14 Evaluation results

[0131]

[0132] The closer the relative closeness is to 1, the better the supply chain resilience is, which means the better the adaptability of the port logistics service supply chain in dealing with emergencies. The ranking results obtained by using the TOPSIS method are: Bohai Rim > Southeast Coast and Others > Southwest Coast > Pearl River Delta > Yangtze River Delta. From the evaluation results, it can be seen that the resilience coefficients of the five port groups to which Company Z belongs are all between 0.4 and 0.6. According to the level of 2.4.2, Company Z's resilience level is average.

[0133] The contents not described in detail in the specification of the present invention belong to the prior art known to those skilled in the art.

Claims

1. A comprehensive evaluation method for the resilience level of port logistics service supply chain approaching the ideal solution based on ISM-ANP-CRITIC, characterized by Follow these steps: S1: Determine the factors affecting the resilience of the port logistics service supply chain and construct an evaluation index system for the resilience level of the port logistics service supply chain, including primary indicators and secondary indicators. The primary indicators include: emergency response capability, staff quality, big data analysis capability, risk management capability, infrastructure support capability, collaboration capability, etc. A primary indicator system for the resilience of the port logistics service supply chain has been established, with a total of 6 primary indicators. S2: The secondary indicator set belonging to the i-th primary indicator Ai is recorded as aij, which represents the j-th secondary indicator of the i-th primary indicator Ai; mi represents the number of secondary indicators of the i-th primary indicator Ai; including emergency organization system (A1), emergency operation mechanism (A2), guarantee system (A3), risk awareness of staff (B1), degree of operation standardization (B2), response ability (B3), digital integration of industrial chain nodes (C1), prediction ability (C2), digital platform construction (C3), risk identification ability (D1), risk assessment ability (D2), risk control ability (D3), risk management innovation ability (D4), target berth number (E1), target handling capacity (E2), year-on-year growth of container throughput (E3), information sharing degree (F1), supply chain network reorganization ability (F2), and loyalty of cooperative enterprises (F3). A total of 19 secondary indicators. S3: Determine the logical relationship between indicators through the data obtained from the scores of experts in the field to establish a binary relationship matrix, and calculate the reachable matrix through software. S4: Use the ANP-CRITIC method to conduct combined weighted analysis on the network model established by ISM and calculate the weights of indicators at all levels; S5: Use the approximate ideal solution ranking method to comprehensively evaluate the resilience of the port logistics service supply chain.

2. According to claim 1, a method for comprehensively evaluating the resilience level of a port logistics service supply chain based on ISM-ANP-CRITIC and approaching an ideal solution, characterized in that: In step S3, the ISM method is used to model and analyze the constructed port logistics service supply chain resilience level evaluation index system.

3. According to claim 1, a comprehensive evaluation method for the resilience level of port logistics service supply chain approaching an ideal solution based on ISM-ANP-CRITIC, characterized in that: In step S4, the ANP-CRITIC method is used to combine and weight the constructed port logistics service supply chain resilience level evaluation index system, and the weight is obtained after consistency test.

4. According to claim 1, a comprehensive evaluation method for the resilience level of port logistics service supply chain based on ISM-ANP-CRITIC close to the ideal solution is characterized by: In step S5, the overall evaluation result is determined by using the approximate ideal solution according to the evaluation index system, so that the evaluation result is more intuitive.

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