CN109460926A - Platform area group of assets comprehensive performance evaluation method based on analytic hierarchy process (AHP) and Information Entropy - Google Patents

Platform area group of assets comprehensive performance evaluation method based on analytic hierarchy process (AHP) and Information Entropy Download PDF

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CN109460926A
CN109460926A CN201811350489.XA CN201811350489A CN109460926A CN 109460926 A CN109460926 A CN 109460926A CN 201811350489 A CN201811350489 A CN 201811350489A CN 109460926 A CN109460926 A CN 109460926A
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任腾云
陈刚
王春波
周溢青
屈维意
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State Grid Jiangsu Electric Power Co Ltd
Jiangsu Electric Power Information Technology Co Ltd
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Abstract

The platform area group of assets comprehensive performance evaluation method based on analytic hierarchy process (AHP) and Information Entropy that the invention discloses a kind of, data input module receives the relative importance grade between any two qualitative index and obtains platform area performance information, and wherein platform area performance information includes multiple achievement datas corresponding with quantitative target；Qualitative index weight calculation module calculates weight of each qualitative index with respect to platform area group of assets comprehensive performance using analytic hierarchy process (AHP)；Quantitative target weight calculation module calculates the weight of each quantitative target using Information Entropy；The weight of each quantitative target is obtained weight of each three-level index with respect to platform area performance with respect to the weight of platform area performance multiplied by qualitative index corresponding with the quantitative target by total weight calculation module；Weight and achievement data according to each three-level index with respect to platform area performance calculate the comprehensive performance score of each area's group of assets using weighted sum.The present invention can reasonably and accurately evaluate the comprehensive performance of each area's group of assets.

Description

Platform area group of assets comprehensive performance evaluation method based on analytic hierarchy process (AHP) and Information Entropy
Technical field
The present invention relates to power industries, and in particular to a kind of comprehensive based on the platform area group of assets of analytic hierarchy process (AHP) and Information Entropy Performance Evaluation Methods.
Background technique
With the expansion of platform area scale, the variation of the promotion of management philosophy and power utilization environment, Utilities Electric Co. increasingly payes attention to The performance management of platform area group of assets, to promote the professional ability of group of assets management platform.With reference to Balanced scorecard, from four angles Analyze the group of assets performance management of platform area: dominant achievement angle refers to department higher level and wants the thing that department does；Functional tribute Offer angle cover a department contribute institute for enterprise must a ready-made one's work；Performance management angle covers classics The angle of department's internal process in Balanced scorecard；Requirement of the study with development angle to department is exactly how hoisting power is with full Sufficient requirements of one's work.By giving not area's performance marking on the same stage, to find their shortcomings in performance management.
Summary of the invention
For defect present in existing performance appraisal technology, the purpose of the present invention is to provide one kind to be based on step analysis The platform area group of assets comprehensive performance evaluation method of method and Information Entropy.Analytic hierarchy process (AHP) is a kind of multiobjective decision-making side of subjective weights Method, and Information Entropy belongs to objective weight analytic approach, information content size provided by the entropy using each index determine index Weight, each index weights are more acurrate, have more convincingness in conjunction with making for the two.
The purpose of the present invention is achieved through the following technical solutions:
A kind of platform area group of assets comprehensive performance evaluation method based on analytic hierarchy process (AHP) and Information Entropy, it is characterised in that specific Steps are as follows:
S1. data input module receives the relative importance grade between any two qualitative index and obtains platform area achievement Information is imitated, wherein platform area performance information includes multiple achievement datas corresponding with quantitative target；
S2. qualitative index weight calculation module is comprehensive with respect to platform area group of assets using each qualitative index of analytic hierarchy process (AHP) calculating Close the weight of performance；
S3. quantitative target weight calculation module calculates the weight of each quantitative target using Information Entropy；
S4. total weight calculation module is by the weight of each quantitative target multiplied by qualitative index corresponding with the quantitative target The weight of opposite platform area performance obtains weight of each three-level index with respect to platform area performance；
S5. the weight and achievement data according to each three-level index with respect to platform area performance calculate each using weighted sum The comprehensive performance score of area's group of assets.
Wherein: data input module includes qualitative index weight calculation module and quantitative target weight calculation module；For It receives the relative importance grade between any two first class index or two-level index and obtains platform area performance information, described Area's performance information includes multiple achievement datas corresponding with the three-level index, and three-level index is the quantitative finger that can quantify Mark, level-one, two-level index are to belong to qualitative index to the summary of three-level index；Qualitative index weight calculation module, according to institute The relative importance grade between any two first class index or two-level index is stated, and each qualitative based on analytic hierarchy process (AHP) calculating Weight of the index with respect to platform area group of assets comprehensive performance；Quantitative target weight calculation module, it is each fixed to be calculated based on Information Entropy The weight of figureofmerit；
Total weight calculation module is determined corresponding to the weight and the quantitative target according to each quantitative target Property index with respect to the weight of platform area group of assets comprehensive performance, calculate weight of each quantitative target with respect to comprehensive performance, it is described total Weight calculation module calculates platform area group of assets achievement with respect to the weight of comprehensive performance and the achievement data by each three-level index Imitate score.
Calculate weight of each qualitative index with respect to platform area performance the following steps are included:
S21. to being compared two-by-two between qualitative index, the relative importance grade of qualitative index between any two is evaluated, And the judgment matrix A of qualitative index is constructed according to relative importance grade,
Wherein, aijFor the element of judgment matrix A the i-th row jth column, indicate qualitative The relative importance grade of index i and qualitative index j, i=1,2 ..., n, j=1,2 ..., n, and aij=1/aji；
S22. judgment matrix A is obtained into matrix B=(b by row normalizationij)n×n, wherein bijFor matrix B the i-th row jth column Element,
S23. matrix B is obtained into Matrix C=(c by row summationi)n×1, wherein ciFor the element of the i-th row of Matrix C,
S24. Matrix C is normalized to obtain characteristic vector W=(w1,w2,…,wn)T, wherein wiFor the i-th row of feature vector W Element,
S25. the corresponding maximal eigenvector of characteristic vector W is calculated:
S26. the consistency of test and judge matrix A, measures the deviation degree of consistency of judgment matrix A, and measures judgement square Whether battle array A inconsistency exceeds permissible range, if it is not, step S27 is executed, if so, return step S21, is evaluated qualitative again Then the relative importance grade of index between any two executes step S22-S26, until judgment matrix A inconsistency is being allowed In range；
S27. by characteristic vector W=(w1,w2,…,wn)TAs weight vectors.
Calculate the weight of each quantitative target the following steps are included:
S31. the comprehensive performance information in sampling platform area, building initialization matrix X,
Wherein, xijFor the element of initialization matrix X the i-th row jth column, the is indicated The achievement data of j-th of three-level index in the area i Ge Tai, i=1,2 ..., n, j=1,2 ..., m；
S32. the achievement data of each area's three-level index is standardized,
If xjFor positive index, the achievement data of three-level index after standardization:
If xjFor negative sense index, the achievement data of three-level index after standardization:
Treated, and matrix is denoted as X',
x'ijFor the element of matrix X' the i-th row jth column, i-th j-th of area three-level index after standardization is indicated Achievement data, i=1,2 ..., n, j=1,2 ..., m；
S33. the contribution degree in lower i-th area of j-th of three-level index is calculated:Wherein i=1,2 ..., N, j=1,2 ..., m；
S34. the entropy of j-th of three-level index is calculated:Wherein k=1/ln (n), i=1, 2 ..., n, j=1,2 ..., m；
S35. the coefficient of variation of j-th of three-level index: g is calculatedj=1-ej, j=1,2 ..., m；
S36. the weight of each three-level index is calculated:
In the present invention, the qualitative index weight calculation module according to any two first class index or two-level index it Between relative importance grade construct the judgment matrix of qualitative index, and by the judgment matrix by renormalization after row summation Processing obtains feature vector, and acquires the maximum eigenvalue of described eigenvector, the qualitative index weight calculation module according to Whether the maximum eigenvalue of described eigenvector examines the inconsistency of the judgment matrix in permissible range, if so, will Described eigenvector is as weight vectors, if it is not, then evaluating the relative importance grade of qualitative index between any two again.
Four first class index are set in assessment indicator system of the invention, each first class index corresponds to multiple two-level index, often A two-level index corresponds to multiple three-level indexs again.Data input module, for receiving any two first class index or two-level index Between relative importance grade and obtain multiple achievement datas corresponding with three-level index.Three-level index is can to quantify Quantitative target, level-one, two-level index are to belong to qualitative index to the summary of three-level index.It is calculated using analytic hierarchy process (AHP) every The weight of a qualitative index calculates the weight of each quantitative target using Information Entropy.Total weight calculation module, it is each for calculating Three-level index and calculates platform Qu get Fen with respect to the weight of platform area group of assets comprehensive performance.
Information Entropy of the invention belongs to objective weight analytic approach, information content size provided by the entropy using each index It determines index weights, makes that each index weights are more acurrate, more convincingness, can reasonably and accurately evaluate each area's group of assets Comprehensive performance.
Detailed description of the invention
Platform area performance appraisal structural schematic diagram in Fig. 1-present invention based on analytic hierarchy process (AHP) and Information Entropy；
The flow chart of platform area group of assets comprehensive performance evaluation is carried out in Fig. 2-present invention；
Qualitative index weight calculation module calculates the flow chart of weight in Fig. 3-present invention；
Quantitative target weight calculation module calculates the flow chart of weight in Fig. 4-present invention；
Specific embodiment
A kind of platform area group of assets comprehensive performance evaluation method based on analytic hierarchy process (AHP) and Information Entropy, refers to equipped with multiple level-ones Mark, and each first class index corresponds to multiple two-level index, each two-level index corresponds to multiple three-level indexs again.
Including data input module and total weight calculation module.Data input module includes qualitative index weight calculation module With quantitative target weight calculation module；For receiving the relative importance grade between any two first class index or two-level index And platform area performance information is obtained, described area's performance information includes multiple achievement datas corresponding with the three-level index, Three-level index is the quantitative target that can quantify, and level-one, two-level index are to belong to qualitative index to the summary of three-level index.It is fixed Property index weights computing module, according to the relative importance grade between any two first class index or two-level index, And weight of each qualitative index with respect to platform area group of assets comprehensive performance is calculated based on analytic hierarchy process (AHP).Quantitative target weight calculation Module calculates the weight of each quantitative target based on Information Entropy；
Total weight calculation module is determined corresponding to the weight and the quantitative target according to each quantitative target Property index with respect to the weight of platform area group of assets comprehensive performance, calculate weight of each quantitative target with respect to comprehensive performance, it is described total Weight calculation module calculates platform area group of assets achievement with respect to the weight of comprehensive performance and the achievement data by each three-level index Imitate score.
Qualitative index weight calculation module is according to relatively important between any two first class index or two-level index Property grade construct the judgment matrix of qualitative index, and the judgment matrix is handled to obtain feature by renormalization after row summation Vector, and the maximum eigenvalue of described eigenvector is acquired, the qualitative index weight calculation module is according to described eigenvector Maximum eigenvalue examine the inconsistency of the judgment matrix whether in permissible range, if so, by described eigenvector As weight vectors, if it is not, then evaluating the relative importance grade of qualitative index between any two again.
A kind of platform area group of assets comprehensive performance methods of marking based on analytic hierarchy process (AHP) and Information Entropy the following steps are included:
S1. data input module receives the relative importance grade between any two qualitative index and obtains platform area achievement Information is imitated, wherein platform area performance information includes multiple achievement datas corresponding with quantitative target；
S2. qualitative index weight calculation module is comprehensive with respect to platform area group of assets using each qualitative index of analytic hierarchy process (AHP) calculating Close the weight of performance；
S3. quantitative target weight calculation module calculates the weight of each quantitative target using Information Entropy；
S4. total weight calculation module is by the weight of each quantitative target multiplied by qualitative index corresponding with the quantitative target The weight of opposite platform area performance obtains weight of each three-level index with respect to platform area performance；
S5. the weight and achievement data according to each three-level index with respect to platform area performance calculate each using weighted sum The comprehensive performance score of area's group of assets.
Based on the above technical solution, calculating each qualitative index with respect to the weight of platform area performance includes following step It is rapid:
S21. to being compared two-by-two between qualitative index, the relative importance grade of qualitative index between any two is evaluated, And the judgment matrix A of qualitative index is constructed according to relative importance grade,
Wherein, aijFor the element of judgment matrix A the i-th row jth column, indicate qualitative The relative importance grade of index i and qualitative index j, i=1,2 ..., n, j=1,2 ..., n, and aij=1/aji；
S22. judgment matrix A is obtained into matrix B=(b by row normalizationij)n×n, wherein bijFor matrix B the i-th row jth column Element,
S23. matrix B is obtained into Matrix C=(c by row summationi)n×1, wherein ciFor the element of the i-th row of Matrix C,
S24. Matrix C is normalized to obtain characteristic vector W=(w1,w2,…,wn)T, wherein wiFor the i-th row of feature vector W Element,
S25. the corresponding maximal eigenvector of characteristic vector W is calculated:
S26. the consistency of test and judge matrix A, measures the deviation degree of consistency of judgment matrix A, and measures judgement square Whether battle array A inconsistency exceeds permissible range, if it is not, step S27 is executed, if so, return step S21, is evaluated qualitative again Then the relative importance grade of index between any two executes step S22-S26, until judgment matrix A inconsistency is being allowed In range；
S27. by characteristic vector W=(w1,w2,…,wn)TAs weight vectors.
Based on the above technical solution, it is weighed in the consistency of the test and judge matrix A using coincident indicator CI Measure the deviation degree of consistency of judgment matrix A:And it is inconsistent using consistency ratio CR measurement judgment matrix A Property permissible range: CR=CI/RI, wherein RI be average homogeneity index, select with judgment matrix A with valence data.RI It is shown in Table 1 with the relationship ginseng of order.
The relationship of table 1 average homogeneity index RI value and order
n 1 2 3 4 5 6 7 8 9 10 11
RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51
Based on the above technical solution, calculate the weight of each quantitative target the following steps are included:
S31. the comprehensive performance information in sampling platform area, building initialization matrix X,
Wherein, xijFor the element of initialization matrix X the i-th row jth column, the is indicated The achievement data of j-th of three-level index in the area i Ge Tai, i=1,2 ..., n, j=1,2 ..., m；
S32. the achievement data of each area's three-level index is standardized,
If xjFor positive index, the achievement data of three-level index after standardization:
If xjFor negative sense index, the achievement data of three-level index after standardization:
Treated, and matrix is denoted as X',
x'ijFor the element of matrix X' the i-th row jth column, i-th j-th of area three-level index after standardization is indicated Achievement data, i=1,2 ..., n, j=1,2 ..., m；
S33. the contribution degree in lower i-th area of j-th of three-level index is calculated:Wherein i=1,2 ..., N, j=1,2 ..., m；
S34. the entropy of j-th of three-level index is calculated:Wherein k=1/ln (n), i=1, 2 ..., n, j=1,2 ..., m；
S35. the coefficient of variation of j-th of three-level index: g is calculatedj=1-ej, j=1,2 ..., m.J-th of three-level is referred to Mark, when its entropy is smaller, then coefficient of variation gjIt is bigger, and gjIt is more big, indicate that the three-level index is more important.
S36. the weight of each three-level index is calculated:
Weight and achievement data corresponding with three-level index according to three-level index with respect to platform area performance, are asked using weighting With each area's performance score of calculating.Its specific formula for calculation sees below formula:
Si=q1*x'i1+q2*x'i2+…qj*x'ij, j=1,2 ..., m；
Finally according to the platform area group of assets comprehensive performance score of calculating, suitable score section is chosen, is divided different excellent Good middle poor grade.
Based on the above technical solution, first class index has 4, and two-level index has 13, and three-level index has 53.With The prior art is compared, the present invention has the advantages that
Title of the invention includes qualitative index weight calculation module, quantitative target weight calculation module and total weight calculation Module, the present invention is based on the performance in platform area, according to qualitative index weight calculation module, quantitative target weight calculation module and total power Re-computation module can quantitatively calculate the performance score of platform area group of assets.According to the comprehensive performance score in platform area, divide different excellent Good middle poor grade.So as to preferably find the platform area of high performance.According to different platform area performance ratings, different achievements is designed Management strategy is imitated, convenient for promoting the professional ability of Utilities Electric Co..

Claims (3)

1. a kind of platform area group of assets comprehensive performance evaluation method based on analytic hierarchy process (AHP) and Information Entropy, it is characterised in that specific step It is rapid as follows:
S1. data input module receives the relative importance grade between any two qualitative index and obtains platform area performance letter Breath, wherein platform area performance information includes multiple achievement datas corresponding with quantitative target；
S2. qualitative index weight calculation module calculates each qualitative index with respect to the comprehensive achievement of platform area group of assets using analytic hierarchy process (AHP) The weight of effect；
S3. quantitative target weight calculation module calculates the weight of each quantitative target using Information Entropy；
S4. total weight calculation module is opposite multiplied by qualitative index corresponding with the quantitative target by the weight of each quantitative target The weight of platform area performance obtains weight of each three-level index with respect to platform area performance；
S5. the weight and achievement data according to each three-level index with respect to platform area performance calculate each area's money using weighted sum The comprehensive performance score of production group；
Wherein: data input module includes qualitative index weight calculation module and quantitative target weight calculation module；For receiving Relative importance grade and acquisition platform area performance information between any two first class index or two-level index, described area's achievement Effect information includes multiple achievement datas corresponding with the three-level index, and three-level index is the quantitative target that can quantify, and one Grade, two-level index are to belong to qualitative index to the summary of three-level index；Qualitative index weight calculation module is appointed according to described It anticipates two relative importance grades between first class index or two-level index, and each qualitative index is calculated based on analytic hierarchy process (AHP) The weight of opposite platform area group of assets comprehensive performance；Quantitative target weight calculation module calculates each quantitatively finger based on Information Entropy Target weight；
Total weight calculation module, qualitative finger corresponding to the weight and the quantitative target according to each quantitative target The weight for marking opposite platform area group of assets comprehensive performance, calculates weight of each quantitative target with respect to comprehensive performance, total weight Computing module calculates platform area group of assets performance with respect to the weight of comprehensive performance and the achievement data by each three-level index and obtains Point.
2. the platform area group of assets comprehensive performance evaluation method according to claim 1 based on analytic hierarchy process (AHP) and Information Entropy, It is characterized by: calculate weight of each qualitative index with respect to platform area performance the following steps are included:
S21. to being compared two-by-two between qualitative index, the relative importance grade of qualitative index between any two, and root are evaluated The judgment matrix A of qualitative index is constructed according to relative importance grade,
Wherein, aijFor the element of judgment matrix A the i-th row jth column, qualitative index i is indicated With the relative importance grade of qualitative index j, i=1,2 ..., n, j=1,2 ..., n, and aij=1/aji；
S22. judgment matrix A is obtained into matrix B=(b by row normalizationij)n×n, wherein bijFor the member of matrix B the i-th row jth column Element,I=1,2 ..., n, j=1,2 ..., n；
S23. matrix B is obtained into Matrix C=(c by row summationi)n×1, wherein ciFor the element of the i-th row of Matrix C,I= 1,2,…,n；
S24. Matrix C is normalized to obtain characteristic vector W=(w1,w2,…,wn)T, wherein wiFor the member of the i-th row of feature vector W Element,I=1,2 ..., n；
S25. the corresponding maximal eigenvector of characteristic vector W is calculated:
S26. the consistency of test and judge matrix A, measures the deviation degree of consistency of judgment matrix A, and measures judgment matrix A not Whether consistency exceeds permissible range, if it is not, step S27 is executed, if so, return step S21, evaluates qualitative index two again Then relative importance grade between two executes step S22-S26, until judgment matrix A inconsistency is in permissible range；
S27. by characteristic vector W=(w1,w2,…,wn)TAs weight vectors.
3. the platform area group of assets comprehensive performance evaluation method according to claim 1 based on analytic hierarchy process (AHP) and Information Entropy, It is characterized by: calculate the weight of each quantitative target the following steps are included:
S31. the comprehensive performance information in sampling platform area, building initialization matrix X,
Wherein, xijFor the element of initialization matrix X the i-th row jth column, i-th is indicated The achievement data of j-th of three-level index in area, i=1,2 ..., n, j=1,2 ..., m；
S32. the achievement data of each area's three-level index is standardized,
If xjFor positive index, the achievement data of three-level index after standardization:
If xjFor negative sense index, the achievement data of three-level index after standardization:
Treated, and matrix is denoted as X',
x′ijFor the element of matrix X' the i-th row jth column, the finger of i-th area, j-th of three-level index after standardization is indicated Mark data, i=1,2 ..., n, j=1,2 ..., m；
S33. the contribution degree in lower i-th area of j-th of three-level index is calculated:Wherein i=1,2 ..., n, j= 1,2,…,m；
S34. the entropy of j-th of three-level index is calculated:Wherein k=1/ln (n), i=1,2 ..., N, j=1,2 ..., m；
S35. the coefficient of variation of j-th of three-level index: g is calculatedj=1-ej, j=1,2 ..., m；
S36. the weight of each three-level index is calculated:J=1,2 ..., m.
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