CN115545489A - Public resource transaction platform service quality evaluation method based on AHP-fuzzy comprehensive evaluation - Google Patents
Public resource transaction platform service quality evaluation method based on AHP-fuzzy comprehensive evaluation Download PDFInfo
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Abstract
The invention discloses a public resource transaction platform service quality evaluation method based on AHP-fuzzy comprehensive evaluation, which determines index weight by using an AHP method and determines a service quality result by using a fuzzy comprehensive evaluation method, and comprises the following steps: 1, constructing a service quality evaluation index system of a public resource transaction platform; 2, calculating subjective weight of each level of index by an analytic hierarchy process; and 3, determining a total evaluation result by using a fuzzy comprehensive evaluation method according to the evaluation index system. The invention can construct a set of service quality evaluation standards of the public resource transaction platform, thereby providing support for the design of service quality evaluation indexes of the public resource transaction platform.
Description
Technical Field
The invention belongs to the field of service quality evaluation of public resource trading platforms, and particularly relates to a service quality evaluation method of a public resource trading platform based on AHP-fuzzy comprehensive evaluation.
Background
The public resource trading platform is used as a comprehensive mechanism for providing public resource trading service, and has important significance for promoting the standardization construction of the public resource service and promoting the sunlight operation of public resource trading. However, the public resource transaction platform has the problems of non-uniform service standards, non-standard service flow and the like in the operation process, which directly influences the service quality level of the public resource transaction platform. The improvement of the service quality of the public resource trading platform is an important guarantee for the high-quality development of the public resource trading. Therefore, in order to promote the public resource trading platform to continuously optimize the service flow, standardize the service behavior, strengthen the service guarantee and provide higher-quality service for the market subject, a set of service quality evaluation standards of the public resource trading platform is urgently needed to be constructed.
In the related research of the service quality of the existing public resource trading platform, the index weight of the service quality evaluation is usually given by experts according to experience and has certain subjectivity. AHP is a quantitative and qualitative combination, the subjective judgement of people is expressed and processed in the form of quantity, minimize the disadvantage brought by subjective assumption of the person, make the result of evaluation more credible on this, the invention has designed a public resource transaction platform service quality assessment method based on AHP-fuzzy comprehensive evaluation.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a public resource trading platform service quality evaluation method based on AHP-fuzzy comprehensive evaluation so as to provide support for public resource trading platform service quality evaluation index design, thereby improving the service level of the public resource trading platform and promoting the high-quality and high-level development of the public resource trading platform.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention discloses a public resource transaction platform service quality evaluation method based on AHP-fuzzy comprehensive evaluation, which is characterized by comprising the following steps:
step 1, constructing a public resource transaction platform service quality evaluation index system, which comprises a primary index and a secondary index; the primary indexes include: index of formability A 1 Reliability index A 2 Responsiveness index A 3 Index A of emotional activity 4 And electronization index A 5 (ii) a Let you want toThe ith primary index is A i ，i＝1,2,…,5；
Step 2, belonging to the ith primary index A i Is set as
a ij Indicates the ith primary index A i The j-th secondary index of (2); m is i Indicates the ith primary index A i The number of secondary indexes of (1);
step 3, calculating subjective weights of indexes at all levels by an analytic hierarchy process:
step 3-1: qualitatively describing the relative importance of the primary index and the secondary index by a 1-9 scaling method respectively, and quantitatively expressing the relative importance by numbers, thereby constructing a judgment matrix B of the primary index with dimension of 5 multiplied by 5 and a judgment matrix M with dimension of m i ×m i Judgment matrix C of the second-level index i ，i＝1,2,…,5；
Step 3-2: calculating the maximum characteristic root corresponding to the judgment matrix B of the first-level index
Calculating the product of each row element in the judgment matrix B
p, q =1,2 …; recalculate M p Root of 5 times
For vector W b ＝(w 1 ,w 2 ,…w 5 ) T Normalization processing is carried out to obtain normalized vector
Thereby calculating the maximum characteristic root corresponding to the judgment matrix B
Wherein (BW) b ) p Is the resulting vector BW b P (a) ofA vector element;
step 3-2: respectively calculating judgment matrixes C of secondary indexes i Corresponding maximum eigenvalue
Calculating a judgment matrix C i The product of each row of elements in
s,t＝1,2…m i (ii) a Computing
M of i Root of inferior Fang
For vector
Normalization processing is carried out to obtain normalized vector
Thereby calculating a judgment matrix C i Corresponding maximum feature root
Wherein
Is the obtained vector
The s-th vector element of (1);
step 3-3: respectively calculating the consistency indexes of the first-level index and the second-level index
r＝b,C i ，n r Judging the order corresponding to the matrix;
step 3-4: according to the order number n of the judgment matrix r Inquiring RI order standard value table to obtain order n r Corresponding average random consistency index RI r Thereby calculating a consistency ratio
Step 3-5: when judging the consistency ratio CR of the matrix r When the judgment matrix is less than or equal to 0.1, the judgment matrix passes consistency inspection; otherwise, the one-time check is not passed, and the step 3-1 is returned to execute;
step 4, after the weight values of the first-level evaluation index and the second-level evaluation index are determined, establishing a first-level index weight distribution set W b T Second level index weight distribution set
(T stands for transpose), determining a total evaluation result by using a fuzzy comprehensive evaluation method:
step 4-1: the final evaluation grade of the evaluation of the service quality of the public resource transaction platform is divided into 3 grades by adopting a fuzzy mathematics 3-grade scoring method: unsatisfied, substantially satisfied, i.e. comment set V = { unsatisfied, substantially satisfied, satisfied };
step 4-2: evaluating each index according to the comment set to obtain evaluation set data, and establishing the dimension as m i X 3 fuzzy judgment matrix
The fuzzy judgment matrix
The y-th row element in the x-th row represents the membership degree for carrying out y-grade evaluation on the x-th index, and the membership degree is represented by the value frequency of the x-th index belonging to the y-th evaluation grade; x =1,2 … m i ,y＝1,2,3；
Step 4-2: calculating comprehensive evaluation vector of each secondary index
(degree is fuzzy comprehensive operator, in fuzzy mathematics is called fuzzy operator, the fuzzy operator has several forms, said invention adopts "weighted average type" operator), so that it can form secondary index evaluation matrix
Step 4-3: comprehensive judgment for calculating first-level indexes
Thus, an evaluation result is obtained according to the maximum membership principle.
Compared with the prior art, the invention has the beneficial effects that:
1. the method is scientific and reasonable, basically solves the quantitative problem of index evaluation, avoids the defect of qualitative description, can quantify the qualitative description, and enables an evaluation conclusion to be more practical, thereby being an evaluation model with excellent property and feasibility.
2. The invention constructs a public resource trading platform service quality evaluation index system, is beneficial to improving the service level of the public resource trading platform, can find the problems of the public resource trading platform service and improve the service with emphasis according to the evaluation system analysis evaluation result, is beneficial to improving the service management level, and promotes the high-quality and high-level development of the public resource trading platform.
Drawings
FIG. 1 is a block diagram of the AHP-fuzzy synthesis method of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
Step 1, constructing a public resource transaction platform service quality evaluation index system, which comprises a primary index and a secondary index; the primary indicators include: index of formability A 1 Reliability index A 2 Responsiveness index A 3 Index A of emotional activity 4 And electronization index A 5 (ii) a Let any ith primary finger be labeled A i ，i＝1,2,…,5；
Step 2, belonging to the ith primary index A i Is recorded as a set of secondary indexes
a ij Indicates the ith primary index A i The jth secondary index of (1); m is i Indicates the ith primary index A i The index system for the service quality of the common resource trading platform is shown in table 1.
TABLE 1 index system for quality of service of public resource trading platform
Step 3, calculating subjective weights of indexes at all levels by an analytic hierarchy process:
step 3-1: qualitatively describing the relative importance of the first-level index and the second-level index by a 1-9 scaling method, quantitatively expressing the relative importance of the first-level index and the second-level index by accurate numbers, and constructing a judgment matrix B with 5 multiplied by 5 dimensions and a judgment matrix m with dimensions i ×m i Judgment matrix C of the second-level index i I =1,2, …,5; wherein, the meaning of "1-9 scale" is shown in Table 2.
TABLE 2 definition of "1-9 Scale method
| Scale | Means of |
| 1 | Compared with the two indexes, the two indexes have the same importance |
| 3 | The former is slightly more important than the latter in comparison with the two indexes |
| 5 | The former is significantly more important than the latter in comparison with the two indexes |
| 7 | The former is more important than the latter in comparison with the two indexes |
| 9 | The former is extremely important than the latter in comparison with the two indexes |
| 2、4、6、8 | Intermediate value representing the above-mentioned adjacent judgment |
The pairwise comparison judgment matrix of the primary evaluation index relative to the total evaluation target is as follows:
the pairwise judgment matrixes obtained by the secondary evaluation indexes corresponding to the primary evaluation indexes are respectively as follows:
step 3-2: calculating the maximum characteristic root corresponding to the judgment matrix B of the first-level index
Calculating to obtain a vector W b ＝(0.0698，0.3284，0.2636，0.1215，0.2167) T Root of maximum feature
Step 3-2: respectively calculating judgment matrixes C of secondary indexes i Corresponding maximum eigenvalue
Calculating the vector
Root of maximum feature
(Vector)
Root of maximum feature
(Vector)
Root of maximum feature
(Vector)
Root of maximum feature
(Vector)
Root of maximum feature
Step 3-3: respectively calculating the consistency indexes of the first-level index and the second-level index
n r To determine the corresponding order of the matrix. Calculating a consistency index CI of the first-level index b =0.01; second level index A 1 Index of consistency of
Second level index A 2 Index of consistency of
Second level index A 3 Index of consistency of
Second level index A 4 Index of consistency of
Second level index A 5 Index of consistency of
Step 3-4: according to the order number n of the judgment matrix r Inquiring RI order standard value table 3 to obtain order n r Corresponding average random consistency index RI r Thereby calculating a consistency ratio
Calculating a consistency ratio CR of the first-order indicators b =0.01; second level index A 1 Is consistent with the ratio of
Second level index A 2 Is consistent with the ratio of
Second level index A 3 Is consistent with the ratio of
Second level index A 4 Is consistent ratio of
Second level index A 5 Is consistent ratio of
TABLE 3 Table of standard values for the number of orders 3 RI
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| RI | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 | 1.41 |
Step 3-5: when judging the consistency ratio CR of the matrix r When the judgment matrix is less than or equal to 0.1, the judgment matrix passes consistency inspection; otherwise, the one-time check is not passed, and the step 3-1 is returned to execute. The results of the steps 3-5 show that the CR of the first-level index and the CR of the second-level index are both less than 0.1, so that the matrix meets the conditions after consistency test. The weights of the indices are obtained as shown in table 4.
Table 4 public resource transaction platform service quality evaluation index system weight table
Step 4, after the weight values of the first-level evaluation index and the second-level evaluation index are determined, evaluating the service quality evaluation result of a public resource trading platform in a certain city by using a fuzzy synthesis method:
step 4-1: the final evaluation grade of the evaluation of the service quality of the public resource transaction platform is divided into 3 grades by adopting a fuzzy mathematics 3-grade scoring method: unsatisfied, substantially satisfied, i.e. comment set V = { unsatisfied, substantially satisfied, satisfied };
step 4-2: please a plurality of experts to evaluate each index according to the comment set to obtain evaluation set data, and the dimension is m i X 3 fuzzy judgment matrix
Matrix of
Line x y column elements (x =1,2 … m) i Y =1,2,3) indicating the membership degree of the x-th index for the y-th evaluation, which is expressed by the frequency of the value of the x-th index belonging to the y-th evaluation level.
Assuming that the evaluation expert group consists of 10 experts, the evaluation result statistics of each evaluation expert are shown in table 5:
TABLE 5 evaluation result statistics table
From Table 5, the secondary index formability A 1 Reliability A 2 Responsiveness A 3 Emotional shift A 4 Electronization of A 5 The evaluation matrix of (1) is as follows:
step 4-2: calculating comprehensive evaluation vector of each secondary index
Calculated to obtain
Thereby obtaining a matrix
Step 4-3: comprehensive judgment for calculating first-level indexes
And according to the maximum membership principle, the evaluation result of the service quality of the public resource trading platform in the city is 'satisfaction'.
Claims (1)
1. A public resource transaction platform service quality assessment method based on AHP-fuzzy comprehensive evaluation is characterized by comprising the following steps:
step 1, constructing a public resource transaction platform service quality evaluation index system, which comprises a primary index and a secondary index; the primary indicators include: index of formability A 1 Reliability index A 2 And a responsiveness index A 3 Move, moveSentiment index A 4 And electronization index A 5 (ii) a Let any ith primary finger be labeled A i ，i＝1,2,…,5；
Step 2, belonging to the ith primary index A i Is set as
a ij Indicates the ith primary index A i The jth secondary index of (1); m is i Indicates the ith primary index A i The number of secondary indexes of (1);
step 3, calculating subjective weights of indexes at all levels by an analytic hierarchy process:
step 3-1: qualitatively describing the relative importance of the primary index and the secondary index by a 1-9 scaling method respectively, and quantitatively expressing the relative importance by numbers, thereby constructing a judgment matrix B of the primary index with dimension of 5 multiplied by 5 and a judgment matrix M with dimension of m i ×m i Judgment matrix C of the second-level index i ，i＝1,2,…,5；
Step 3-2: calculating the maximum characteristic root corresponding to the judgment matrix B of the first-level index
Calculating the product of each row element in the judgment matrix B
p, q =1,2 …; recalculating M p Root of 5 times
For vector W b ＝(w 1 ,w 2 ,…w 5 ) T Normalization processing is carried out to obtain normalized vector
Thereby calculating the maximum characteristic root corresponding to the judgment matrix B
Wherein (BW) b ) p Is the resulting vector BW b The p-th vector element of (1);
step 3-2: respectively calculating judgment matrixes C of secondary indexes i Corresponding maximum eigenvalue
Calculating a judgment matrix C i The product of each row of elements in
Computing
M of i Root of inferior Fang
For vector
Normalization processing is carried out to obtain normalized vector
Thereby calculating a judgment matrix C i Corresponding maximum feature root
Wherein
Is the obtained vector
The s-th vector element of (1);
step 3-3: respectively calculating the consistency indexes of the first-level index and the second-level index
n r Judging the order corresponding to the matrix;
step 3-4: according to the order number n of the judgment matrix r Inquiring RI order standard value table to obtain order n r Corresponding average random consistency index RI r Thereby calculating a consistency ratio
Step 3-5: when judging the consistency ratio CR of the matrix r When the judgment matrix is less than or equal to 0.1, the judgment matrix passes consistency inspection; otherwise, the one-time check is not passed, and the step 3-1 is returned to execute;
step 4, after the weight values of the first-level evaluation index and the second-level evaluation index are determined, establishing a first-level index weight distribution set W b T Second level index weight distribution set
(T stands for transpose), determining a total evaluation result by using a fuzzy comprehensive evaluation method:
step 4-1: the final evaluation grade of the evaluation of the service quality of the public resource transaction platform is divided into 3 grades by adopting a fuzzy mathematics 3-grade scoring method: unsatisfied, substantially satisfied, i.e. comment set V = { unsatisfied, substantially satisfied, satisfied };
step 4-2: evaluating each index according to the comment set to obtain evaluation set data, and establishing the dimension as m i X 3 fuzzy judgment matrix
The fuzzy judgment matrix
The x-th row and y-th column of the middle column represent the x-th rowThe indexes make the membership degree of y-grade evaluation, and the membership degree is represented by the value frequency of the x index belonging to the y evaluation grade; x =1,2 … m i ,y＝1,2,3；
Step 4-2: calculating comprehensive evaluation vector of each secondary index
The fuzzy operator has several forms, and the invention adopts "weighted average type" operator so as to form secondary index evaluation matrix
Step 4-3: comprehensive judgment S = W for calculating primary index b T Gamma to obtain the evaluation result according to the maximum membership principle.
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