Railway logistics enterprise competitiveness evaluation method and system based on entropy weight and AHP

Abstract

The invention is suitable for the technical field of data processing, and provides a railway logistics enterprise competitiveness evaluation method and system based on entropy weight and AHP, and the method comprises the following steps: determining an evaluation object and an evaluation index, constructing a hierarchical structure based on the AHP method, and constructing a judgment matrix of each hierarchy; calculating a characteristic value and a characteristic vector according to the judgment matrix, and calculating a consistency index CR; using feature vector normalization to obtain an AHP weight of each evaluation index; performing standardization processing on the evaluation indexes, and calculating an entropy value of each evaluation index; calculating a difference coefficient of each evaluation index according to the entropy value, and calculating an entropy weight of each evaluation index according to the difference coefficient; and combining the AHP weight and the entropy weight to obtain a combined weight, and calculating a comprehensive score of each evaluation object. According to the method, subjective judgment and objective data are combined, the judgment matrix is constructed, and the entropy weight is calculated, so that the deviation of subjective judgment is reduced, and the objectivity and the accuracy of an evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to a method and system for evaluating the competitiveness of railway logistics enterprises based on entropy weight and AHP. Background Art

[0002] With the development of globalization and informatization, the position of the railway logistics industry in the national economy has become increasingly prominent. However, while facing market opportunities, railway logistics enterprises are also confronted with a highly competitive environment. To enhance their competitiveness, railway logistics enterprises need to continuously optimize resource allocation, improve service quality, and innovate in technology and management models. Therefore, it is particularly important to objectively and comprehensively evaluate the competitiveness of railway logistics enterprises. Traditional competitiveness evaluation methods often focus only on financial indicators or certain specific business indicators, overlooking the multi-dimensional characteristics of the overall operation of enterprises. This approach is prone to leading to one-sidedness and inaccuracy in evaluation results. Traditional evaluation methods may overly rely on the subjective opinions of experts, resulting in insufficient objectivity of evaluation results. Therefore, there is a need to provide a method and system for evaluating the competitiveness of railway logistics enterprises based on entropy weight and AHP to address the above problems. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a method and system for evaluating the competitiveness of railway logistics enterprises based on entropy weight and AHP to solve the problems described in the above background art.

[0004] The present invention is implemented as follows. A method for evaluating the competitiveness of railway logistics enterprises based on entropy weight and AHP, the method comprising the following steps:

[0005] Determine the evaluation object and evaluation indicators, construct a hierarchical structure based on the AHP method, divide the evaluation indicators into a target layer and a main criterion layer, and construct a judgment matrix for each layer;

[0006] Calculate the eigenvalue and eigenvector according to the judgment matrix, and calculate the consistency index CR; use eigenvector normalization to obtain the AHP weight of each evaluation indicator;

[0007] Standardize the evaluation indicators, and calculate the entropy value of each evaluation indicator according to the standardized indicator data;

[0008] Calculate the difference coefficient of each evaluation indicator according to the entropy value, and calculate the entropy weight of each evaluation indicator according to the difference coefficient;

[0009] Combine the AHP weight and the entropy weight to obtain a combined weight, and calculate the comprehensive score of each evaluation object according to the combined weight and the standardized data.

[0010] As a further solution of the present invention: The steps of calculating the eigenvalue and eigenvector according to the judgment matrix, calculating the consistency index CR, and using eigenvector normalization to obtain the AHP weight of each evaluation index specifically include:

[0011] Calculate the eigenvalue and eigenvector based on the formula AV = λV, where A is the judgment matrix, λ is the eigenvalue, and V is the eigenvector;

[0012] Calculate the consistency index CR based on the formula CR = CI / RI, where CI is the consistency index and RI is the random consistency index obtained by looking up the table;

[0013] Use eigenvector normalization to obtain the AHP weight of each index. The normalization formula is: Wia = Vi / ∑Vi, where Wia is the AHP weight of evaluation index i and Vi is the value of the corresponding element in the eigenvector.

[0014] As a further solution of the present invention: The steps of standardizing the evaluation index and calculating the entropy value of each evaluation index according to the standardized index data specifically include:

[0015] Standardize the index data based on the standardization formula X'ij = (Xij - Xjmin) / (Xjmax - Xjmin), where X'ij is the standardized data, Xij is the original data, and Xjmin and Xjmax are the minimum and maximum values of evaluation index j respectively;

[0016] Calculate the entropy value of the evaluation index based on the formula Ej = -k∑(Pij × lnPij), where k = 1 / lnm, m is the number of evaluation objects, and Pij = X'ij / ∑X'ij, and Pij is the proportion of standardized index i in the jth evaluation object.

[0017] As a further solution of the present invention: The steps of calculating the difference coefficient of each evaluation index according to the entropy value and calculating the entropy weight of each evaluation index according to the difference coefficient specifically include:

[0018] Calculate the difference coefficient of each evaluation index based on the calculation formula dj = 1 - Ej, where dj is the difference coefficient and Ej is the entropy value;

[0019] Calculate the entropy weight of each evaluation index based on the calculation formula Wie = dj / ∑dj, where Wie is the entropy weight of evaluation index i.

[0020] As a further solution of the present invention: The steps of combining the AHP weight and the entropy weight to obtain the combined weight and calculating the comprehensive score of each evaluation object according to the combined weight and the standardized data specifically include:

[0021] Based on the combined weight calculation formula \(W_i'=\alpha W_{ia}+(1 - \alpha)W_{ie}\), the AHP weight and entropy weight are combined, where \(W_i'\) is the combined weight, \(W_{ia}\) is the AHP weight, \(W_{ie}\) is the entropy weight, and \(\alpha\) is the weight distribution coefficient;

[0022] Based on the formula \(W_i'' = W_i' / \sum W_i'\), the combined weight is normalized so that the sum of all weights is equal to 1;

[0023] Based on the calculation formula \(S_i=\sum(W_i''\times X_{ij}')\), the comprehensive score of each evaluation object is calculated, where \(S_i\) is the comprehensive score of evaluation object \(i\), \(W_i''\) is the combined weight, and the evaluation objects are sorted according to the comprehensive score.

[0024] Another object of the present invention is to provide a competitiveness evaluation system for railway logistics enterprises based on entropy weight and AHP. The system includes:

[0025] A judgment matrix construction module, used to determine evaluation objects and evaluation indicators, construct a hierarchical structure based on the AHP method, divide the evaluation indicators into a target layer and a main criterion layer, and construct a judgment matrix for each layer;

[0026] An AHP weight calculation module, used to calculate eigenvalues and eigenvectors according to the judgment matrix, calculate the consistency index CR; use eigenvector normalization to obtain the AHP weight of each evaluation indicator;

[0027] An index entropy value calculation module, used to standardize the evaluation indicators, and calculate the entropy value of each evaluation indicator according to the standardized indicator data;

[0028] An index entropy weight calculation module, used to calculate the difference coefficient of each evaluation indicator according to the entropy value, and calculate the entropy weight of each evaluation indicator according to the difference coefficient;

[0029] A combined weight calculation module, used to combine the AHP weight and entropy weight to obtain the combined weight, and calculate the comprehensive score of each evaluation object according to the combined weight and the standardized data.

[0030] As a further solution of the present invention: the AHP weight calculation module includes:

[0031] A feature calculation unit, used to calculate eigenvalues and eigenvectors based on the formula \(AV = \lambda V\), where \(A\) is the judgment matrix, \(\lambda\) is the eigenvalue, and \(V\) is the eigenvector;

[0032] A consistency index calculation unit, used to calculate the consistency index CR based on the formula \(CR = CI / RI\), where \(CI\) is the consistency index and \(RI\) is the random consistency index, which is obtained by looking up the table;

[0033] The AHP weight calculation unit is used to obtain the AHP weight of each index by using eigenvector normalization. The normalization formula is: Wia = Vi / ∑Vi, where Wia is the AHP weight of evaluation index i, and Vi is the value of the corresponding element in the eigenvector.

[0034] As a further solution of the present invention: The index entropy value calculation module includes:

[0035] The standardization processing unit is used to perform standardization processing on the index data based on the standardization formula X'ij = (Xij - Xjmin) / (Xjmax - Xjmin), where X'ij is the standardized data, Xij is the original data, and Xjmin and Xjmax are the minimum and maximum values of evaluation index j respectively;

[0036] The index entropy value calculation unit is used to calculate the entropy value of the evaluation index based on the formula Ej = -k∑(Pij × lnPij), where k = 1 / lnm, m is the number of evaluation objects, Pij = X'ij / ∑X'ij, and Pij is the proportion of the standardized index i in the jth evaluation object.

[0037] As a further solution of the present invention: The index entropy weight calculation module includes:

[0038] The difference coefficient calculation unit is used to calculate the difference coefficient of each evaluation index based on the calculation formula dj = 1 - Ej, where dj is the difference coefficient and Ej is the entropy value;

[0039] The index entropy weight calculation unit is used to calculate the entropy weight of each evaluation index based on the calculation formula Wie = dj / ∑dj, where Wie is the entropy weight of evaluation index i.

[0040] As a further solution of the present invention: The combined weight calculation module includes:

[0041] The combined weight calculation unit is used to combine the AHP weight and the entropy weight based on the calculation formula of the combined weight Wi' = αWia + (1 - α)Wie, where Wi' is the combined weight, Wia is the AHP weight, Wie is the entropy weight, and α is the weight distribution coefficient;

[0042] The normalization processing unit is used to perform normalization processing on the combined weight based on the formula Wi” = Wi' / ∑Wi' so that the sum of all weights is equal to 1;

[0043] The comprehensive score calculation unit is used to calculate the comprehensive score of each evaluation object based on the calculation formula Si = ∑(Wi” × X'ij), where Si is the comprehensive score of evaluation object i, Wi” is the combined weight, and the evaluation objects are sorted according to the comprehensive score.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] The evaluation method of the present invention based on entropy weight and AHP can comprehensively consider multiple indicators, including financial indicators, market indicators, technical indicators, etc., so as to more comprehensively reflect the competitiveness of enterprises. And the evaluation method based on entropy weight and AHP combines subjective judgment and objective data. By constructing a judgment matrix and calculating the entropy weight, it reduces the deviation of subjective judgment, improves the objectivity and accuracy of the evaluation results, and can also adjust the evaluation indicators and weights according to the actual situation, so as to better adapt to the dynamic changes of enterprise competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 FIG. is a flow chart of an evaluation method for the competitiveness of railway logistics enterprises based on entropy weight and AHP.

[0047] Figure 2 FIG. is a flow chart of obtaining the AHP weight in the evaluation method for the competitiveness of railway logistics enterprises based on entropy weight and AHP.

[0048] Figure 3 FIG. is a flow chart of calculating the entropy value of evaluation indicators in the evaluation method for the competitiveness of railway logistics enterprises based on entropy weight and AHP.

[0049] Figure 4 FIG. is a flow chart of calculating the entropy weight of evaluation indicators in the evaluation method for the competitiveness of railway logistics enterprises based on entropy weight and AHP.

[0050] Figure 5 FIG. is a flow chart of obtaining the combined weight in the evaluation method for the competitiveness of railway logistics enterprises based on entropy weight and AHP.

[0051] Figure 6 FIG. is a schematic structural diagram of an evaluation system for the competitiveness of railway logistics enterprises based on entropy weight and AHP. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] The following describes the specific implementation of the present invention in detail with reference to specific embodiments.

[0054] As Figure 1 shown, an embodiment of the present invention provides an evaluation method for the competitiveness of railway logistics enterprises based on entropy weight and AHP. The method includes the following steps:

[0055] S100. Determine the evaluation object and evaluation indicators, construct a hierarchical structure based on the AHP method, divide the evaluation indicators into an objective layer and a main criterion layer, and construct a judgment matrix for each layer;

[0056] S200. Calculate the eigenvalue and eigenvector according to the judgment matrix, and calculate the consistency index CR; use eigenvector normalization to obtain the AHP weight of each evaluation indicator;

[0057] S300. Standardize the evaluation indicators, and calculate the entropy value of each evaluation indicator according to the standardized indicator data;

[0058] S400. Calculate the difference coefficient of each evaluation indicator according to the entropy value, and calculate the entropy weight of each evaluation indicator according to the difference coefficient;

[0059] S500. Combine the AHP weight and the entropy weight to obtain a combined weight, and calculate the comprehensive score of each evaluation object according to the combined weight and the standardized data.

[0060] It should be noted that traditional competitiveness evaluation methods often only focus on financial indicators or certain specific business indicators, ignoring the multi-dimensional characteristics of the overall operation of the enterprise. This method is likely to lead to one-sidedness and inaccuracy of the evaluation results. Traditional evaluation methods may rely too much on the subjective opinions of experts, resulting in insufficient objectivity of the evaluation results. The embodiments of the present invention aim to solve the above problems.

[0061] In the embodiments of the present invention, first, the evaluation object and evaluation indicators are determined. For example, the evaluation object is three railway logistics enterprises A, B, and C, and the evaluation indicators include market share (X1), customer satisfaction (X2), logistics efficiency (X3), technological innovation ability (X4), and service quality (X5). Then, a hierarchical structure is constructed based on the AHP method, and the evaluation indicators are divided into a target layer and a main criterion layer. A judgment matrix for each layer is constructed. For example, the target layer is the evaluation of the competitiveness of railway logistics enterprises, and the main criterion layer is market share, customer satisfaction, logistics efficiency, technological innovation ability, and service quality. The elements in the judgment matrix represent the relative importance of one indicator to another, and a scale of 1-9 is used for evaluation. 1 means no difference, and 9 means extremely important. Assuming there are n indicators, the judgment matrix A is an n×n matrix, where Aij represents the importance of indicator i relative to indicator j. Next, the eigenvalue and eigenvector are calculated according to the judgment matrix, and the consistency index CR is calculated; the eigenvector is normalized to obtain the AHP weight of each evaluation indicator; then the evaluation indicators are standardized, and according to the standardized indicator data, the entropy value of each evaluation indicator is calculated, and the difference coefficient of each evaluation indicator is calculated according to the entropy value, and the entropy weight of each evaluation indicator is calculated according to the difference coefficient. Finally, the AHP weight and the entropy weight are combined to obtain the combined weight, and according to the combined weight and the standardized data, the comprehensive score of each evaluation object is calculated. The higher the score, the stronger the competitiveness of the enterprise.

[0062] As Figure 2 shown, as a preferred embodiment of the present invention, the steps of calculating the eigenvalue and eigenvector according to the judgment matrix, calculating the consistency index CR; using the eigenvector normalization to obtain the AHP weight of each evaluation indicator specifically include:

[0063] S201, calculating the eigenvalue and eigenvector based on the formula AV = λV, where A is the judgment matrix, λ is the eigenvalue, and V is the eigenvector;

[0064] S202, calculating the consistency index CR based on the formula CR = CI / RI, where CI is the consistency index and RI is the random consistency index, which is obtained by looking up the table;

[0065] S203, using the eigenvector normalization to obtain the AHP weight of each indicator. The normalization formula is: Wia = Vi / ∑Vi, where Wia is the AHP weight of evaluation indicator i, and Vi is the value of the corresponding element in the eigenvector.

[0066] In the embodiments of the present invention, the eigenvalue and eigenvector of the judgment matrix are calculated according to the formula AV = λV and using tools such as MATLAB or Excel, where A is the judgment matrix, λ is the eigenvalue, and V is the eigenvector; then the consistency index CR is calculated according to the formula CR = CI / RI, where CI is the consistency index and RI is the random consistency index, which is obtained by looking up the table. Assuming the calculated eigenvector is V = [0.45, 0.28, 0.15, 0.12] (normalized), calculate the consistency index CI and RI (obtain the RI value by looking up the table, for example, RI(4) = 0.9), calculate CR = CI / RI, if CR < 0.1, it is determined that the consistency of the judgment matrix is good. Finally, the eigenvector is normalized to obtain the AHP weight of each index, and the normalization formula is: Wia = Vi / ∑Vi, where Wia is the AHP weight of evaluation index i, and Vi is the value of the corresponding element in the eigenvector.

[0067] As Figure 3 shown, as a preferred embodiment of the present invention, the step of standardizing the evaluation index and calculating the entropy value of each evaluation index according to the standardized index data specifically includes:

[0068] S301, standardize the index data based on the standardization formula X'ij = (Xij - Xjmin) / (Xjmax - Xjmin), where X'ij is the standardized data, Xij is the original data, and Xjmin and Xjmax are the minimum and maximum values of evaluation index j respectively;

[0069] S302, calculate the entropy value of the evaluation index based on the formula Ej = -k∑(Pij×lnPij), where k = 1 / lnm, m is the number of evaluation objects, and Pij = X'ij / ∑X'ij, and Pij is the proportion of standardized index i in the jth evaluation object.

[0070] In the embodiments of the present invention, before formally calculating the entropy value, it is necessary to standardize the index data according to the standardization formula X'ij = (Xij - Xjmin) / (Xjmax - Xjmin), where X'ij is the standardized data, Xij is the original data, and Xjmin and Xjmax are the minimum and maximum values of evaluation index j respectively, and then calculate the entropy value of the evaluation index according to the formula Ej = -k∑(Pij×lnPij). For example, for the market share X1, its entropy value Ej can be calculated through the formula.

[0071] As Figure 4 shown, as a preferred embodiment of the present invention, the step of calculating the difference coefficient of each evaluation index according to the entropy value and calculating the entropy weight of each evaluation index according to the difference coefficient specifically includes:

[0072] S401. Calculate the coefficient of variation dj for each evaluation index based on the calculation formula dj = 1 - Ej, where dj is the coefficient of variation and Ej is the entropy value.

[0073] S402. Calculate the entropy weight Wie of each evaluation index based on the calculation formula Wie = dj / ∑dj, where Wie is the entropy weight of evaluation index i.

[0074] As Figure 5 shown, as a preferred embodiment of the present invention, the step of combining the AHP weight and the entropy weight to obtain the combined weight and calculating the comprehensive score of each evaluation object according to the combined weight and the standardized data specifically includes:

[0075] S501. Combine the AHP weight and the entropy weight based on the calculation formula of the combined weight Wi' = αWia + (1 - α)Wie, where Wi' is the combined weight, Wia is the AHP weight, Wie is the entropy weight, and α is the weight distribution coefficient.

[0076] S502. Normalize the combined weight based on the formula Wi” = Wi' / ∑Wi' so that the sum of all weights is equal to 1.

[0077] S503. Calculate the comprehensive score of each evaluation object based on the calculation formula Si = ∑(Wi” × X'ij), where Si is the comprehensive score of evaluation object i, Wi” is the combined weight, and rank the evaluation objects according to the comprehensive score.

[0078] In the embodiment of the present invention, the AHP weight and the entropy weight are combined according to the calculation formula of the combined weight Wi' = αWia + (1 - α)Wie to obtain the combined weight. Wi' is the combined weight, Wia is the AHP weight, Wie is the entropy weight, and α is the weight distribution coefficient, which is determined according to the actual situation. Assume α = 0.5. Then, the combined weight is normalized based on the formula Wi” = Wi' / ∑Wi' so that the sum of all weights is equal to 1. Finally, the comprehensive score of each evaluation object is calculated according to the calculation formula Si = ∑(Wi” × X'ij), where Si is the comprehensive score of evaluation object i and Wi” is the combined weight. Rank the evaluation objects according to the comprehensive score, and the higher the score, the stronger the competitiveness of the enterprise. Through the above steps, the competitiveness of railway logistics enterprises can be comprehensively evaluated and compared based on the entropy weight and AHP methods, providing a scientific basis for the strategic planning and decision-making of enterprises.

[0079] As Figure 6 shown, the embodiment of the present invention also provides a competitiveness evaluation system for railway logistics enterprises based on entropy weight and AHP. The system includes:

[0080] The judgment matrix construction module 100 is used to determine the evaluation object and evaluation indicators, construct a hierarchical structure based on the AHP method, divide the evaluation indicators into the target layer and the main criterion layer, and construct the judgment matrix for each layer;

[0081] The AHP weight calculation module 200 is used to calculate the eigenvalue and eigenvector according to the judgment matrix, and calculate the consistency index CR; use eigenvector normalization to obtain the AHP weight of each evaluation indicator;

[0082] The index entropy value calculation module 300 is used to standardize the evaluation indicators, and calculate the entropy value of each evaluation indicator according to the standardized index data;

[0083] The index entropy weight calculation module 400 is used to calculate the difference coefficient of each evaluation indicator according to the entropy value, and calculate the entropy weight of each evaluation indicator according to the difference coefficient;

[0084] The combined weight calculation module 500 is used to combine the AHP weight and the entropy weight to obtain the combined weight, and calculate the comprehensive score of each evaluation object according to the combined weight and the standardized data.

[0085] In the embodiment of the present invention, the AHP weight calculation module 200 includes:

[0086] The feature calculation unit is used to calculate the eigenvalue and eigenvector based on the formula AV = λV, where A is the judgment matrix, λ is the eigenvalue, and V is the eigenvector;

[0087] The consistency index calculation unit is used to calculate the consistency index CR based on the formula CR = CI / RI, where CI is the consistency index and RI is the random consistency index, which is obtained by looking up the table;

[0088] The AHP weight calculation unit is used to use eigenvector normalization to obtain the AHP weight of each index. The normalization formula is: Wia = Vi / ∑Vi, where Wia is the AHP weight of evaluation index i, and Vi is the value of the corresponding element in the eigenvector.

[0089] In the embodiment of the present invention, the index entropy value calculation module 300 includes:

[0090] The standardization processing unit is used to standardize the index data based on the standardization formula X'ij = (Xij - Xjmin) / (Xjmax - Xjmin), where X'ij is the standardized data, Xij is the original data, and Xjmin and Xjmax are the minimum and maximum values of evaluation index j respectively;

[0091] An index entropy value calculation unit, which is used to calculate the entropy value of an evaluation index based on the formula Ej=-k∑(Pij×lnPij), where k = 1 / lnm, m is the number of evaluation objects, and Pij = X'ij / ∑X'ij, and Pij is the proportion of index i in the jth evaluation object after standardization.

[0092] In an embodiment of the present invention, the index entropy weight calculation module 400 includes:

[0093] A difference coefficient calculation unit, which is used to calculate the difference coefficient of each evaluation index based on the calculation formula dj = 1 - Ej, where dj is the difference coefficient and Ej is the entropy value;

[0094] An index entropy weight calculation unit, which is used to calculate the entropy weight of each evaluation index based on the calculation formula Wie = dj / ∑dj, and Wie is the entropy weight of evaluation index i.

[0095] In an embodiment of the present invention, the combined weight calculation module 500 includes:

[0096] A combined weight calculation unit, which is used to combine the AHP weight and the entropy weight based on the combined weight calculation formula Wi' = αWia+(1-α)Wie, where Wi' is the combined weight, Wia is the AHP weight, Wie is the entropy weight, and α is the weight distribution coefficient;

[0097] A normalization processing unit, which is used to normalize the combined weight based on the formula Wi” = Wi' / ∑Wi' so that the sum of all weights is equal to 1;

[0098] A comprehensive score calculation unit, which is used to calculate the comprehensive score of each evaluation object based on the calculation formula Si = ∑(Wi”×X'ij), where Si is the comprehensive score of evaluation object i, Wi” is the combined weight, and the evaluation objects are sorted according to the comprehensive score.

[0099] The above only describes the preferred embodiments of the present invention in detail, and does not limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0100] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0101] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0102] After considering the specification and the disclosure of the embodiments, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

Claims

1. The railway logistics enterprise competitiveness evaluation method based on entropy weight and AHP is characterized by: The method comprises the following steps: Determine the evaluation object and evaluation indicators, build a hierarchical structure based on the AHP method, divide the evaluation indicators into the target layer and the main criterion layer, and build a judgment matrix for each layer; Calculate the eigenvalue and eigenvector according to the judgment matrix, and calculate the consistency index CR; use the eigenvector normalization to obtain the AHP weight of each evaluation index; The evaluation indicators are standardized, and the entropy value of each evaluation indicator is calculated based on the standardized indicator data; Calculate the difference coefficient of each evaluation indicator according to the entropy value, and calculate the entropy weight of each evaluation indicator according to the difference coefficient; The AHP weight and entropy weight are combined to obtain the combined weight. The comprehensive score of each evaluation object is calculated based on the combined weight and the standardized data.

2. The railway logistics enterprise competitiveness evaluation method based on entropy weight and AHP according to claim 1 is characterized in that: The eigenvalues ​​and eigenvectors are calculated according to the judgment matrix, and the consistency index CR is calculated; The steps of using eigenvector normalization to obtain the AHP weight of each evaluation index include: Calculate the eigenvalue and eigenvector based on the formula AV = λV, where A is the judgment matrix, λ is the eigenvalue, and V is the eigenvector; The consistency index CR is calculated based on the formula CR = CI / RI, where CI is the consistency index and RI is the random consistency index, which is obtained by looking up the table; The eigenvector is normalized to obtain the AHP weight of each indicator. The normalization formula is: Wia = Vi / ∑Vi, where Wia is the AHP weight of evaluation indicator i, and Vi is the value of the corresponding element in the eigenvector.

3. The railway logistics enterprise competitiveness evaluation method based on entropy weight and AHP according to claim 1 is characterized in that: The step of performing standardization processing on the evaluation index and calculating the entropy value of each evaluation index according to the index data after the standardization processing specifically includes: The index data is standardized based on the standardized formula X'ij = (Xij-Xjmin) / (Xjmax-Xjmin), where X'ij is the standardized data, Xij is the original data, and Xjmin and Xjmax are the minimum and maximum values ​​of the evaluation index j, respectively; The entropy value of the evaluation index is calculated based on the formula Ej=-k∑(Pij×lnPij), where k=1 / lnm, m is the number of evaluation objects, Pij=X'ij / ∑X'ij, and Pij is the proportion of the standardized index i in the jth evaluation object.

4. The railway logistics enterprise competitiveness evaluation method based on entropy weight and AHP according to claim 3 is characterized in that: The step of calculating the difference coefficient of each evaluation indicator according to the entropy value, and calculating the entropy weight of each evaluation indicator according to the difference coefficient specifically includes: The difference coefficient of each evaluation index is calculated based on the calculation formula dj=1-Ej, where dj is the difference coefficient and Ej is the entropy value; The entropy weight of each evaluation index is calculated based on the calculation formula Wie=dj / ∑dj, where Wie is the entropy weight of evaluation index i.

5. The railway logistics enterprise competitiveness evaluation method based on entropy weight and AHP according to claim 4 is characterized in that: The step of combining the AHP weight and the entropy weight to obtain a combined weight, and calculating the comprehensive score of each evaluation object according to the combined weight and the standardized data specifically includes: The AHP weight and entropy weight are combined based on the calculation formula of the combined weight Wi'=αWia+(1-α)Wie, where Wi' is the combined weight, Wia is the AHP weight, Wie is the entropy weight, and α is the weight distribution coefficient; Normalize the combined weights based on the formula Wi”=Wi' / ∑Wi' so that the sum of all weights is equal to 1; The comprehensive score of each evaluation object is calculated based on the calculation formula Si=∑(Wi”×X'ij), where Si is the comprehensive score of evaluation object i, Wi” is the combined weight, and the evaluation objects are ranked according to the comprehensive score.

6. The railway logistics enterprise competitiveness evaluation system based on entropy weight and AHP is characterized by: The system comprises: The judgment matrix construction module is used to determine the evaluation object and evaluation indicators, build a hierarchical structure based on the AHP method, divide the evaluation indicators into the target layer and the main criterion layer, and construct the judgment matrix of each layer; The AHP weight calculation module is used to calculate the eigenvalue and eigenvector according to the judgment matrix and calculate the consistency index CR; the eigenvector is normalized to obtain the AHP weight of each evaluation index; The indicator entropy value calculation module is used to standardize the evaluation indicators and calculate the entropy value of each evaluation indicator based on the indicator data after standardization; An indicator entropy weight calculation module is used to calculate the difference coefficient of each evaluation indicator according to the entropy value, and calculate the entropy weight of each evaluation indicator according to the difference coefficient; The combined weight calculation module is used to combine the AHP weight and the entropy weight to obtain the combined weight, and calculate the comprehensive score of each evaluation object based on the combined weight and the standardized data.

7. The railway logistics enterprise competitiveness evaluation system based on entropy weight and AHP according to claim 6 is characterized in that: The AHP weight calculation module includes: A feature calculation unit, used to calculate the eigenvalue and eigenvector based on the formula AV=λV, where A is the judgment matrix, λ is the eigenvalue, and V is the eigenvector; A consistency index calculation unit, used to calculate the consistency index CR based on the formula CR=CI / RI, where CI is the consistency index and RI is the random consistency index, which is obtained by looking up a table; The AHP weight calculation unit is used to use the eigenvector normalization to obtain the AHP weight of each indicator. The normalization formula is: Wia = Vi / ∑Vi, where Wia is the AHP weight of the evaluation indicator i, and Vi is the value of the corresponding element in the eigenvector.

8. The railway logistics enterprise competitiveness evaluation system based on entropy weight and AHP according to claim 6 is characterized in that: The index entropy value calculation module includes: A standardization processing unit, used to perform standardization processing on the index data based on the standardization formula X'ij=(Xij-Xjmin) / (Xjmax-Xjmin), where X'ij is the standardized data, Xij is the original data, and Xjmin and Xjmax are the minimum and maximum values ​​of the evaluation index j respectively; The index entropy value calculation unit is used to calculate the entropy value of the evaluation index based on the formula Ej=-k∑(Pij×lnPij), where k=1 / lnm, m is the number of evaluation objects, Pij=X'ij / ∑X'ij, and Pij is the proportion of standardized index i in the jth evaluation object.

9. The railway logistics enterprise competitiveness evaluation system based on entropy weight and AHP according to claim 8 is characterized in that: The indicator entropy weight calculation module includes: A difference coefficient calculation unit, used to calculate the difference coefficient of each evaluation index based on the calculation formula dj=1-Ej, where dj is the difference coefficient and Ej is the entropy value; The indicator entropy weight calculation unit is used to calculate the entropy weight of each evaluation indicator based on the calculation formula Wie=dj / ∑dj, where Wie is the entropy weight of the evaluation indicator i.

10. The railway logistics enterprise competitiveness evaluation system based on entropy weight and AHP according to claim 9 is characterized in that: The combined weight calculation module includes: A combined weight calculation unit, used for combining the AHP weight and the entropy weight based on a combined weight calculation formula Wi'=αWia+(1-α)Wie, where Wi' is the combined weight, Wia is the AHP weight, Wie is the entropy weight, and α is a weight distribution coefficient; A normalization processing unit, used for normalizing the combined weights based on the formula Wi"=Wi' / ∑Wi' so that the sum of all weights is equal to 1; The comprehensive score calculation unit is used to calculate the comprehensive score of each evaluation object based on the calculation formula Si=∑(Wi”×X'ij), where Si is the comprehensive score of evaluation object i, Wi” is the combined weight, and the evaluation objects are sorted according to the comprehensive score.

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