CN115358504A - National logistics city pivotal sorting model under complex network view angle - Google Patents
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Abstract
The invention discloses a national logistics city hub ranking model under a complex network view angle, which mainly solves the problems that the logistics information communication between hubs is difficult to analyze by using single statistics to evaluate logistics hub cities, the influence of peripheral hubs on the information transmission is easy to ignore, and important hubs in key positions cannot be accurately excavated. According to the invention, a multi-index evaluation system of a national logistics hub is constructed by improving a traditional TOPSIS comprehensive evaluation method, more objective and comprehensive logistics capacity evaluation is carried out on logistics hub nodes, a complex logistics urban network model is established, and the influence of node clustering coefficients on information propagation is considered at the same time, so that a network node sorting algorithm is optimized. Compared with various classical sorting algorithms, the method disclosed by the invention has the advantages that the result is more consistent with a published national logistics hub list and has certain address selection foresight.
Description
Technical Field
The invention provides a national logistics city pivot ordering model under a complex network view angle, belongs to the field of transportation economy, and particularly relates to a complex network science and application statistics related technology.
Background
The logistics industry is a strategic, fundamental and precedent industry for ensuring the supply of national production and living. The national logistics hub is a core infrastructure of a logistics system, is a comprehensive logistics hub with wider radiation area, stronger aggregation effect, better service function and higher operation efficiency, and plays the roles of key nodes, important platforms and backbone hubs in a national logistics network. With the development of network science, the solution of various practical problems by using a complex network key node analysis technology has gradually become a research hotspot. Currently, some scholars try to perform development and evaluation research on logistics hub from the perspective of a complex network. For example: wu Tongyu et al perform multi-scale analysis on logistics hubs through a multi-layer complex network (Wu Tongyu, king key. Logistics hub urban multi-scale analysis and development evaluation based on the multi-layer complex network [ J ] traffic transportation system engineering and information, 2019, 19 (1): 7), he Xiang et al (He Xiang, yuan Yongbo, zhang Mingyuan. Evaluation of importance of logistics coupling network nodes considering successive faults [ J ] computer application research, 2018,35 (7): 5) perform evaluation analysis of importance of logistics network nodes from the perspective of successive faults. Meanwhile, a national logistics hub evaluation system is established to realize the preliminary evaluation of each logistics hub, and the method is a typical multi-index comprehensive evaluation problem. In view of the current state of domestic and foreign research, the measurement indexes and algorithms provided for the development and evaluation of logistics hubs are mainly concentrated in a single area and are mostly analyzed by a statistical method. For example: luhongqi et al (Lu Gongji. Logistics hub Classification research based on location entropy method-take Henan province as example [ J ]. Logistics science, 2021,44 (12): 5) introduced into location entropy method to research provincial logistics hub Classification, sibel et al (Alumur S, kara B Y. Network hub location schemes: the state of The art [ J ]. European journal of operational research,2008,190 (1): 1-21) analyzed The hub sites of traffic flow networks from a supply and demand perspective. Although giving a rough assessment, there are certain limitations: the logistics information exchange between hubs is difficult to analyze, the influence of peripheral hubs on the information transmission is easy to neglect, so that the important hubs in the key positions cannot be accurately found, and the important hubs can be well explained by means of a complex network view angle. At present, importance sorting is carried out on large-scale urban logistics hubs, and research on quantitative analysis of site selection evaluation of national logistics hubs from a macro level is rarely related.
Disclosure of Invention
The invention aims to overcome the problems and provides a national logistics city pivot sorting algorithm under a complex network view angle, which comprises three modules: the system comprises a hub city logistics capacity evaluation module, a national logistics city network construction module and a national logistics city network hub node identification module. The method comprises the following steps:
and S01, constructing a TOPSIS comprehensive evaluation method for evaluating the improvement of the logistics capacity of each pivot city by using a pivot city logistics capacity evaluation module, taking the overall situation under the evaluation index into consideration by using a mean value, and improving the calculation of the approximation degree between the alternative point evaluation index and the positive and negative ideal target values in the traditional TOPSIS method so as to calculate and obtain a pivot city logistics capacity evaluation matrix I.
S02, a national logistics urban network construction module is used for constructing a national logistics urban network model for extracting inter-region logistics relations, the network construction is mainly carried out based on a gravitation model, and the connecting edge weight of the national logistics urban network is mainly determined by two parts: a hub city logistics capacity evaluation matrix I and a hub city distance matrix d; the network connection weight of the national logistics cities shows the strength of the relationship among the logistics cities.
And S03, the national logistics urban network hub node identification module performs hub sorting on each logistics hub node in the national logistics urban network model by using the improved node importance sorting algorithm C-LeaderRank to obtain a port type logistics urban node list ranked in the top ten and compares the port type logistics urban node list with a port type national logistics hub urban list published by the country, so that the effectiveness of the model algorithm is explained.
The step S01 specifically includes:
s11, firstly, acquiring the logistics capacity evaluation value of the logistics hub alternative points under each index, wherein the number of the logistics alternative hubs is N, the number of objective evaluation indexes participating in decision making is M, and an evaluation decision matrix is X = { X = ij } N×M Normalizing the forward indicator and the reverse indicator, respectively, and after normalizing the data, the evaluation values are all located in the interval [0,1 ]]And the positive indicators and the negative indicators are all converted into the positive indicators, the optimal value is 1, the worst value is 0, and the normalized dimensionless multi-indicator evaluation decision matrix is X '= { X' ij } N×M ；
S12, determining index weights of the evaluation system by an entropy weight method, wherein X '= { X' ij } N×M Setting p for dimensionless multi-index evaluation decision matrix after data standardization ij The proportion of the alternative point i of the logistics hub under the evaluation index j is large, and the information entropy of the index j is e j Calculating the weight of the index j in the system by an entropy weight method as follows: w is a j ；
S13, measuring the material flow capacity of alternative points of the national material flow hub by using an improved TOPSIS method, and specifically comprising the following steps:
s13-1, calculating a weighted evaluation matrix Y:
wherein X' is a pivot evaluation decision matrix after data normalization, and W is an index weight diagonal matrix obtained by entropy weight method calculation;
s13-2, calculating a positive ideal target value and a negative ideal target value:
the evaluation of each index positive ideal target value is as follows:
the negative ideal target value of each index evaluation is as follows:
s13-3, calculating the closeness degree between the pivot candidate point and the positive and negative ideal target values:
the overall situation under the evaluation index is considered by utilizing the mean value, and the calculation of the closeness degree between the evaluation index of the alternative points and the positive and negative ideal target values in the traditional TOPSIS method is improved as follows:
wherein the content of the first and second substances,
and (4) averaging the alternative points of the logistics hub under the j index evaluation, wherein alpha is an adjustable parameter.
S14, calculating the closeness I of the ideal solution i As the evaluation result of the logistics capacity of the junction city:
will be close to degree I i The method is used as a final result of multi-index comprehensive evaluation of alternative points of each port type logistics hub. When the alternative point of the pivot is closer to the positive ideal value, the closer the alternative point of the pivot is to the positive ideal value
The smaller the value of the number is,
the larger the value is, the closeness I to the ideal solution i The larger the candidate point is, the better the score is under the comprehensive evaluation system.
The step S02 specifically includes:
s21, taking the domestic main logistics city as a complex logistics network node, and connecting edge weights I between the nodes I and j ij The relationship of the mutual influence between the logistics hub cities i and j corresponding to the logistics hub cities is determined;
considering that the inverse ratio of the logistics radiation capacity and the distance of the hub city is proportional to the logistics capacity of the hub city obtained in the claim 2, the logistics relation is described by means of a gravity model:
wherein, I i The method is characterized in that the comprehensive logistics capacity evaluation value of the urban node i is obtained by using an improved TOPSIS comprehensive evaluation method; g is a gravitational constant; r is the gravity attenuation coefficient; d ij The unit is kilometer of the straight-line distance of the geographic position between the urban nodes i and j of the logistics hub. Due to the limited radiation capability of the actual logistics hub, when the mutual influence relationship between two logistics hub cities is weak enough, namely the connection edge weight I is ij If it is less than a certain value, let it be I ij And =0, disconnecting the edges between the i, j nodes in the complex logistics urban network. And finally constructing to obtain a complex logistics urban network model.
The step S03 specifically includes:
s31, adding a background logistics node V in the complex logistics network obtained in the claim 3 N+1 And establishing connection of the node to all nodes in the logistics network, wherein the connection weight is in direct proportion to the degree of the connected node I N+1,i ＝(k i ) α ；
S32, the unit resources CLR of other nodes except the background node in the logistics network are given at the initial moment i (0)＝1,(i＝1,2,…N)； CLR N+1 (0) =0: node V i The score at time t is defined as CLR i (t), through computational iterations until steady state:
wherein f (c) j ) Is about node V j Cluster coefficient c of j A decreasing function of c j The larger, f (c) j ) The smaller.
S33, because the constructed complex logistics hub network model belongs to a weighted undirected network, k in the formula j Is node V j Degree, obtaining a node V i C-LeaderRank score of
And finally, realizing pivotal sequencing of the network logistics nodes according to the values. Push buttonScore CLR of each pivot node reaching steady state i And (t) performing pivotal sorting from high to low after normalization, namely, obtaining a sorting result of the pivotal sorting model of the national logistics city.
Compared with the prior art, the national logistics city pivotal sorting model under the complex network view angle has the following beneficial effects:
the urban logistics capacity evaluation method based on the TOPSIS algorithm is characterized in that urban logistics capacity evaluation strategies are optimized by combining factors such as the surrounding environment of a hub city and the role of status, the overall distribution situation of each index evaluation value of a national logistics hub evaluation system is considered, the urban logistics capacity is measured by the TOPSIS algorithm, the evaluation result is more detailed, and the method is more in line with the actual situation.
The invention is based on an improved national logistics hub evaluation system, utilizes the gravity model to determine the logistics connection strength among hub cities, constructs a more real port type logistics hub city network model and analyzes the structural characteristics of a logistics hub network.
The invention provides a novel node sorting method C-LeaderRank based on a LeaderRank node sorting algorithm by considering the influence of a clustering coefficient on the node logistics information propagation, and the sorting method can better identify the center attribute and the bridging attribute of the logistics network node.
Drawings
FIG. 1 is a diagram of a pivotal ordering model of a national logistics city under a view angle of a complex network;
FIG. 2 is a comparison graph of comprehensive evaluation values of alternative points before and after TOPSIS improvement;
FIG. 3 is a diagram of a port-type complex logistics city network constructed by using a gravity model;
FIG. 4 is a schematic diagram of a network including "bridging" nodes;
FIG. 5 is a comparison graph of sorting algorithms of major port type logistics cities;
fig. 6 is a schematic diagram of a port type complex logistics urban network.
Detailed Description
For a better understanding of the objects, aspects and advantages of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawings, which are included to provide a further understanding of the invention, and are not intended to limit the scope of the invention.
Fig. 1 is a structure diagram of a national logistics city pivot ordering model under a complex network view. The system comprises a hub city logistics capacity evaluation module, a national logistics city network construction module and a national logistics city network hub node identification module. Specifically comprises
And S01, constructing a TOPSIS comprehensive evaluation method for evaluating the improvement of the logistics capacity of each pivot city by using a pivot city logistics capacity evaluation matrix, taking the overall situation under the evaluation index into consideration by using a mean value, and improving the calculation of the approximation degree between the alternative point evaluation index and the positive and negative ideal target values in the traditional TOPSIS method so as to calculate and obtain a pivot city logistics capacity evaluation matrix I.
Specifically, the comparison result of the comprehensive evaluation values of the candidate points before and after the TOPSIS improvement of S01 is shown in fig. 2, and includes the following steps:
s11, firstly, acquiring the logistics capacity evaluation value of the logistics hub alternative points under each index, wherein the number of the logistics alternative hubs is N, the number of objective evaluation indexes participating in decision making is M, and an evaluation decision matrix is X = { X = ij } N×M Respectively normalizing the forward index and the reverse index, and after data normalization, the evaluation values are all located in the interval [0,1 ]]And the positive indicators and the negative indicators are all converted into the positive indicators, the optimal value is 1, the worst value is 0, and the normalized dimensionless multi-indicator evaluation decision matrix is X '= { X' ij } N×M ；
S12, determining index weights of the evaluation system by an entropy weight method, wherein X '= { X' ij } N×M Setting p for dimensionless multi-index evaluation decision matrix after data standardization ij The proportion of the alternative point i of the logistics hub under the evaluation index j is large, and the information entropy of the index j is e j Calculating the weight of the index j in the system by an entropy weight method as follows: w is a j ；
S13, measuring the logistics capacity of alternative points of a national logistics hub by using an improved TOPSIS method, which comprises the following specific steps:
s13-1, calculating a weighted evaluation matrix Y:
wherein X' is a pivot evaluation decision matrix after data normalization, and W is an index weight diagonal matrix obtained by entropy weight method calculation;
s13-2, calculating a positive ideal target value and a negative ideal target value:
the ideal target value of each index evaluation is as follows:
the negative ideal target value of each index evaluation is as follows:
s13-3, calculating the closeness degree between the pivot candidate point and the positive and negative ideal target values:
the overall situation under the evaluation index is considered by utilizing the mean value, and the calculation of the closeness degree between the evaluation index of the alternative points and the positive and negative ideal target values in the traditional TOPSIS method is improved as follows:
wherein the content of the first and second substances,
and f, calculating an average value of alternative points of the logistics hub under the j index evaluation, wherein alpha is an adjustable parameter.
S14, calculating the closeness I of the ideal solution i As the evaluation result of the logistics capacity of the junction city:
will be close to i The method is used as a final result of multi-index comprehensive evaluation of alternative points of each port type logistics hub. When the alternative point of the pivot is closer to the positive ideal value, the closer the alternative point of the pivot is to the positive ideal value
The smaller the value of the number is,
the larger the value, the closer I to the ideal solution i The larger the value, the better the score of the candidate point under the comprehensive evaluation system.
S02, extracting logistics urban relationship, constructing a national logistics urban network model for extracting inter-region logistics relationship, and constructing a network mainly based on a gravitation model, wherein the connecting edge weight of the national logistics urban network is mainly determined by two parts: a hub city logistics capacity evaluation matrix I and a hub city distance matrix d; the network connection weight of the national logistics cities indicates the strength of the relationship among the logistics cities.
Specifically, taking each large port-type complex logistics city in China as an example, as shown in fig. 3, a port-type complex logistics city network constructed by using an S02 project gravitation model includes the following steps:
s21, taking the domestic main logistics city as a complex logistics network node, and connecting edge weights I between the nodes I and j ij The relationship of the mutual influence between the logistics hub cities i and j corresponding to the logistics hub cities is determined;
considering that the logistics radiation capacity of the hub city is inversely proportional to the distance and is directly proportional to the logistics capacity of the hub city obtained in the claim 2, the logistics relationship is described by means of a gravity model:
wherein the content of the first and second substances,I i the method is characterized in that the comprehensive logistics capacity evaluation value of the urban node i is obtained by using an improved TOPSIS comprehensive evaluation method; g is a gravitational constant; r is the gravity attenuation coefficient; d ij The unit is kilometer for the straight-line distance between the geographical positions of the urban nodes i and j of the logistics hub. Due to the limited radiation capability of the actual logistics hub, when the mutual influence relationship between two logistics hub cities is weak enough, namely the connection edge weight I is ij If it is less than a certain value, let it be I ij And =0, disconnecting the edges between the i, j nodes in the complex logistics urban network. And finally, constructing to obtain a complex logistics urban network model.
And S03, performing pivot sorting on each logistics hub node in the national logistics urban network model by using an improved node importance sorting algorithm C-leader rank to obtain a port type logistics urban node list ranked in the top ten, and comparing the port type logistics urban node list with a port type national logistics hub city list published by the country, so that the effectiveness of the model is demonstrated.
The step S03 specifically includes:
s31, adding a background logistics node V in the complex logistics network obtained in the claim 3 N+1 And establishing connections of the node to all nodes in the logistics network, the connection weight being proportional to the degree of the connected node I N+1,i ＝(k i ) α ；
S32, the unit resources CLR of other nodes except the background node in the logistics network are given at the initial moment i (0)＝1,(i＝1,2,…N)； CLR N+1 (0) =0: node V i The score at time t is defined as CLR i (t), through computational iterations until steady state:
wherein f (c) j ) Is about node V j Cluster coefficient c of j A decreasing function of c j The larger, f (c) j ) The smaller.
S33, because the constructed complex logistics hub network model belongs to a weighted undirected network, k in the formula j Is node V j Degree, to obtain a node V i C-LeaderRank score of
And finally, realizing pivotal sequencing of the network logistics nodes according to the values. According to the CLR of each pivot node score reaching the steady state i And (t) performing pivotal sorting from high to low after normalization, namely, obtaining a sorting result of the pivotal sorting model of the national logistics city.
Specifically, by researching and analyzing a port type complex logistics city network degree accumulated distribution probability curve diagram, the port type complex logistics city network presents a typical scale-free long tail characteristic, that is, most logistics cities have small node degrees, and a few logistics cities have strong capacity, such as: shanghai city, nanjing city and Zhoushan city are connected with most logistics cities. And by adopting function fitting comparison, the degree cumulative distribution probability and the degree value are in accordance with power law distribution with the index of-1.247.
In order to further verify the performance of the complex logistics urban network model established by the invention, the diameter, the average clustering coefficient and the average path length network structure parameter between the harbor type complex logistics urban network and the same-specification random network are compared, as shown in table 1, the average path length of the complex harbor type logistics urban network is 2.241, the network diameter is 4, and the network diameter is similar to the same-specification random network; and the network clustering coefficient reaches 0.76, which is far larger than the clustering coefficient of a scale random network of 0.209. The network has the characteristics of small world structure, namely higher connectivity and stronger aggregation.
TABLE 1 Complex network architecture parameters
In order to verify the effective identification capability of the improved method of the invention on the 'bridging' node in the complex network, taking fig. 4 as an example, a node V containing 'bridging' is constructed F Undirected and unweighted small-scale complex networks. If the self-capability of each node is the same, only the network is consideredThe influence of the network topology and the location of the nodes in the network on the importance of the nodes.
The C-leader rank algorithm, the degree centrality, the betweenness centrality, the Pagerank algorithm and the leader rank algorithm are respectively utilized to carry out importance ranking on each node in the upper graph, the ranking result Top-5 is shown in the following table 2, and the value in parentheses after the node numbering is the computing result of each ranking algorithm:
TABLE 2 Top-5 ranking results in FIG. 4
Table 3 shows the list of the city nodes of the first ten ranked port type logistics after the five algorithms are used for sorting. The bold font is that the city in the port type national logistics hub city list published by the country by 2020, and the PageRank algorithm and the degree center method (k) with the lowest matching degree with the list are in the port type national logistics hub city list published by the state institute except three cities of Shanghai, sunshine and continuous cloud harbor, which illustrates the effectiveness of the construction of the complex logistics hub network.
TABLE 3 ranking results of the algorithms Top-10
Fig. 5 is a comparison graph of major harbor type logistics cities after being sorted by algorithms and normalized scores. Except the ClusterRank algorithm, the sequencing results of the other four sequencing algorithms are approximately the same and have higher discrimination. The scores of all the main port type logistics hub cities in the ClusterRank algorithm sorting are generally higher, which shows that the node clustering coefficients of the main port type logistics hub cities in the complex logistics city network are generally higher and the logistics information transmission capacity is stronger. But different from other algorithms, the ranking results of Yueyang and Yichang under the ranking algorithm are in front, compared with the ranking results of main logistics cities of Shanghai, nanjing, ningbo and the like, the method shows that the city nodes of Yueyang, yichang and the like are in more key positions of the harbor type complex logistics city network, and is beneficial to the propagation of the information capacity of the city nodes. In the other four sequencing algorithms, sequencing results of Shanghai, nanjing and Ningbo cities are all in the front, which shows that the logistics hub of the ports of the sequencing algorithms is strong.
The port logistics hub network is visually processed by means of the Gaode open platform and the distribution of the spatial positions of the main ports in China, the main logistics hub cities are highlighted, and the stronger the logistics contact among the cities, the darker the connecting side color. As shown in fig. 6, it can be summarized that:
1. the main port logistics hub is concentrated in coastal areas in the southeast of China, and the connection is very close. The coastal city selected as the port hub is beneficial to exerting the advantages of large shipping capacity, convenient and fast external trade and the like. The inland cities such as Chongqing, wuhan and the like are selected as port hubs, so that the advantages of inland rivers and ports are favorably exerted, and the logistics communication between the inland cities and coastal cities in China is communicated by relying on favorable terrain along rivers.
2. In cities such as Yingkou, qinzhou, yueyang and the like, because the node clustering coefficient in the port logistics hub network is smaller, the logistics information transmission is facilitated, and in addition, the self port logistics capacity is not weak, the port logistics in the peripheral area can be driven, and the city can successfully enter the national port hub city. Therefore, each city should select a logistics hub type matched with the city for development according to specific conditions, and the logistics hub construction drives the development of industrial areas by improving relevant indexes of the city and utilizing advantages of the city.
3. The port logistics are not developed in the northwest due to the influence of geographical factors. The upstream basin city in the yellow river does not have the national port type logistics hub condition due to sludge accumulation. If ecological environment is further improved, a port type logistics hub city of an upstream drainage basin in the yellow river is developed and constructed, economic communication between the middle and western region and the southeast coastal city can be effectively promoted, and then the purpose of driving logistics economy development of the whole middle and western region is achieved.
The experimental results show that the method can solve the problem that the conventional statistical method is used for analyzing so as to cause the problem that the important pivot of the key position cannot be accurately found. The invention improves a comprehensive evaluation algorithm to calculate network side rights, constructs a complex logistics urban network, optimizes a node importance sorting algorithm by considering the structural characteristics of the complex network and logistics node information transfer, provides a logistics urban hub performance analysis method, and performs model verification by taking a port type logistics hub as an example, so that the national logistics hub site selection characteristics can be analyzed and researched more comprehensively, and a new thought is provided for regional logistics hub development.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (7)
1. A national logistics city pivotal ordering model under a complex network view comprises three modules: the system comprises a hub urban logistics capacity evaluation module, a national logistics urban network construction module and a national logistics urban network hub node identification module; the logistics capacity evaluation module of the hub city is improved aiming at a TOPSIS comprehensive evaluation method, a weighted evaluation matrix Y is calculated based on a normalized dimensionless multi-index evaluation decision matrix X' and an index weight diagonal matrix W, and the proximity degree between a hub alternative point and a positive and negative ideal target value is calculated so as to calculate a logistics capacity evaluation matrix I of the hub city; the national logistics urban network construction module constructs a complex logistics urban network based on the gravity model and the hub city logistics capacity evaluation matrix I; the identification module of the junction node of the national logistics urban network identifies the 'bridge node' in the complex network based on the LeaderRank sorting algorithm, and meets the requirement of site selection and sorting of the national logistics junction; the method is characterized in that the module structure is realized by the following steps:
s01, evaluating logistics capacity of hub cities: the method comprises the steps that a TOPSIS comprehensive evaluation method for evaluating improvement of logistics capacity of each pivot city is constructed by integrating a multi-index evaluation decision matrix and each index weight of an evaluation system, the method considers the overall situation under the evaluation index by using a mean value, and the calculation of the closeness between an alternative point evaluation index and a positive and negative ideal target value in the traditional TOPSIS method is improved, so that a pivot city logistics capacity evaluation matrix I is calculated;
s02, extracting the relationship between logistics cities: the method comprises the following steps of constructing a national logistics urban network model for extracting inter-region logistics relations, and constructing a network based on a gravitation model, wherein the connecting edge weight of the national logistics urban network is determined by two parts: a hub city logistics capacity evaluation matrix I and a hub city distance matrix d; the network connection weight of the national logistics cities indicates the strength of the relationship among the logistics cities;
and S03, performing pivot sorting on each logistics pivot node in the national logistics urban network model by using an improved node importance sorting algorithm C-LeaderRank.
2. The dimensionless multi-index evaluation decision matrix according to claim 1, wherein the step S01 specifically comprises:
s11, acquiring logistics capacity evaluation values of logistics hub alternative points under each index, wherein the number of the logistics alternative hubs is N, the number of objective evaluation indexes participating in decision making is M, and an evaluation decision matrix is X = { X = ij } N×M Normalizing the forward indicator and the reverse indicator, respectively, and after normalizing the data, the evaluation values are all located in the interval [0,1 ]]And the positive indicators and the negative indicators are all converted into the positive indicators, the optimal value is 1, the worst value is 0, and the normalized dimensionless multi-indicator evaluation decision matrix is X '= { X' ij } N×M 。
3. The evaluation system index weights according to claim 2, wherein the step S01 specifically includes:
s12, determining each index weight of the evaluation system by using an entropy weight method, wherein X' = { X = X i ′ j } N×M Setting p for dimensionless multi-index evaluation decision matrix after data standardization ij The proportion of the alternative point i of the logistics hub under the evaluation index j is large, and the information entropy of the index j is e j And calculating the weight of the index j in the system by an entropy weight method as follows: w is a j 。
4. The TOPSIS comprehensive evaluation method for evaluating the logistics capacity improvement of each Pivot city according to claim 3, wherein the step S01 specifically comprises the following steps:
s13, measuring the material flow capacity of alternative points of the national material flow hub by using an improved TOPSIS method, and specifically comprising the following steps:
s13-1, calculating a weighted evaluation matrix Y:
wherein X' is a pivot evaluation decision matrix after data normalization, and W is an index weight diagonal matrix obtained by entropy weight method calculation;
s13-2, calculating positive and negative ideal target values:
the evaluation of each index positive ideal target value is as follows:
the negative ideal target value of each index evaluation is as follows:
s13-3, calculating the closeness degree between the pivot candidate point and the positive and negative ideal target values:
the overall situation under the evaluation index is considered by utilizing the mean value, and the calculation of the closeness degree between the evaluation index of the alternative points and the positive and negative ideal target values in the traditional TOPSIS method is improved as follows:
wherein the content of the first and second substances,
and (4) averaging the alternative points of the logistics hub under the j index evaluation, wherein alpha is an adjustable parameter.
5. The hub city logistics capacity evaluation matrix of claim 4, wherein the step S01 specifically comprises:
s14, calculating the closeness I of the ideal solution i As the evaluation result of logistics capacity of hub cities:
will be close to degree I i The final result is comprehensively evaluated by multiple indexes of alternative points of each port type logistics hub; when the alternative point of the pivot is closer to the positive ideal value, the closer the alternative point of the pivot is to the positive ideal value
The smaller the value of the number is,
the larger the value is, the closeness I to the ideal solution i The larger the candidate point is, the better the score of the candidate point is under the comprehensive evaluation system is.
6. The model for pivotal ordering of national logistics cities under the view of a complex network as claimed in claim 1, wherein said step S02 specifically comprises:
s21, taking the domestic main logistics city as a complex logistics network node, and connecting edge weights I between the nodes I and j ij The relationship of the mutual influence between the logistics hub cities i and j corresponding to the logistics hub cities is determined;
considering that the inverse ratio of the logistics radiation capacity and the distance of the hub city is proportional to the logistics capacity of the hub city obtained in the claim 2, the logistics relation is described by means of a gravity model:
wherein, I i The method is characterized in that the comprehensive logistics capacity evaluation value of the urban node i is obtained by using an improved TOPSIS comprehensive evaluation method; g is a gravitational constant; r is the gravity attenuation coefficient; d ij The unit is kilometer of the linear distance of the geographical positions between the logistics hub city nodes i and j; due to the limited radiation capability of the actual logistics hub, when the mutual influence relationship between two logistics hub cities is weak enough, namely the connection side weight I of the two logistics hub cities is the connection side weight I ij If it is less than a certain value, let it be I ij =0, disconnecting the edges between the i, j nodes in the complex logistics urban network; and finally, constructing to obtain a complex logistics urban network model.
7. The model of a national logistics city pivot ordering under the view of a complex network according to claim 1, wherein the improved node importance ordering algorithm model C-leader rank of step S03 specifically comprises:
s31, adding a background logistics node V in the complex logistics network obtained in the claim 3 N+1 And establishing connection of the node to all nodes in the logistics network, wherein the connection weight is in direct proportion to the degree of the connected node I N+1,i ＝(k i ) α ；
S32, at the initial moment, the CLR of the unit resources of other nodes except the background node in the logistics network is given i (0)＝1,(i＝1,2,…N)；CLR N+1 (0) =0: node V i The score at time t is defined as CLR i (t), iterating the calculation until the stability is achievedState:
wherein f (c) j ) Is about node V j Cluster coefficient c of j A decreasing function of c j The larger, f (c) j ) The smaller.
S33, because the constructed complex logistics hub network model belongs to a weighted undirected network, k in the formula j Is node V j Degree, obtaining a node V i C-LeaderRank score of
Finally, implementing pivotal sorting of the network logistics nodes according to the values; according to the CLR of each pivot node score reaching the steady state i And (t) performing pivotal sorting from high to low after normalization, namely, obtaining a sorting result of the pivotal sorting model of the national logistics city.
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