CN115018452A - Construction and comprehensive evaluation method for project progress management index system - Google Patents

Construction and comprehensive evaluation method for project progress management index system Download PDF

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CN115018452A
CN115018452A CN202210557115.5A CN202210557115A CN115018452A CN 115018452 A CN115018452 A CN 115018452A CN 202210557115 A CN202210557115 A CN 202210557115A CN 115018452 A CN115018452 A CN 115018452A
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程文胜
周绍武
周星妤
杜建国
宫照光
周天刚
黄伟
卫慧
梁晖
张贺
宋玥
熊臣宇
马花月
刘朝
张诗媛
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Shanghai Investigation Design and Research Institute Co Ltd SIDRI
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Abstract

The invention discloses a construction and comprehensive evaluation method of a project construction project progress management index system, S 1 Constructing an engineering construction project progress management index system, wherein the index system comprises a target index, the target index is split to obtain a plurality of first-level classification indexes, and the first-level classification indexes are split to obtain a plurality of second-level classification indexes; s 2 Defining the combined weight of each secondary classification index in the corresponding primary classification index by combining an APH analytic hierarchy process and an entropy weight method; s 3 Obtaining the scores of all secondary classification indexes by using a Topsis-gray correlation method, and obtaining the comprehensive scores of the corresponding primary classification indexes by combining the combined weights of all the secondary classification indexes; s 4 And then, defining the combination weight of each primary classification index by combining an APH (android Package) analytic hierarchy process and an entropy weight method, and combining the comprehensive score of each primary classification index to obtain the comprehensive score of the target index. The method and the system can solve the problem that the quality degree of progress management cannot be quantified in project management, and can accurately grade the indexes of the progress management.

Description

Construction and comprehensive evaluation method for project progress management index system
Technical Field
The invention belongs to the technical field of project progress management, and particularly relates to a construction and comprehensive evaluation method of a project progress management index system of an engineering construction project.
Background
In the existing engineering project, the project is not finished within a specified construction period due to the fact that common influencing factors of the project, such as the size of the project amount, the support level of capital technology, the utilization condition of resources, potential influencing factors and the like, are not planned and monitored in detail, and unnecessary economic loss and engineering disputes are caused. Therefore, many enterprises fill progress weekly reports in the construction process, but the enterprises have progress management data, and lack scientific and effective methods for classification management and deeper analysis of the data, so that objective evaluation and transverse comparison of progress management of different projects cannot be performed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a construction and comprehensive evaluation method of an engineering construction project progress management index system, which aims to solve the problem that the quality degree of progress management cannot be quantified in engineering construction project management and can accurately evaluate the progress management index.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a construction project progress management index system construction and comprehensive evaluation method comprises the following steps:
S 1 constructing an engineering construction project progress management index system, wherein the index system comprises a target index, the target index is split to obtain a plurality of first-level classification indexes, and the first-level classification indexes are split to obtain a plurality of second-level classification indexes;
S 2 comparing the importance degrees of a plurality of secondary classification indexes in the corresponding primary classification indexes respectively by a method combining an APH (advanced peripheral health) analytic hierarchy process and an entropy weight method to obtain the combined weight of each secondary classification index;
S 3 obtaining the scores of all secondary classification indexes in the corresponding primary classification indexes by using a Topsis-gray correlation method, and obtaining the comprehensive scores of the corresponding primary classification indexes by combining the combined weight of all the secondary classification indexes;
S 4 and then obtaining the combined weight of each primary classification index by using a method of combining an APH (advanced analytical hierarchy) method and an entropy weight method, and obtaining the comprehensive score of the target index by combining the comprehensive score of each primary classification index, thereby obtaining the dynamic progress management evaluation grade of the engineering construction project.
Further, step S 2 The method comprises the following steps:
S 21 respectively constructing a judgment matrix for each secondary classification index in the corresponding primary classification index by adopting an APH (advanced peripheral component analysis) analytic hierarchy process through 1-9 scales, and calculating the subjective weight of each secondary classification index by utilizing a geometric mean method;
S 22 evaluating the orderliness and the effectiveness of each secondary classification index in the corresponding primary classification index by an entropy weight method through an information entropy theory, constructing a decision matrix and calculating the objective weight of each secondary classification index;
S 23 and respectively carrying out linear combination on the subjective weight and the objective weight of each secondary classification index in the corresponding primary classification index to obtain the combined weight of each secondary classification index.
Further, step S 21 The method comprises the following steps:
S 21-1 constructing a judgment matrix A of each secondary classification index in the corresponding primary classification indexes ij ，
Figure BDA0003655362180000021
Wherein i ═ i (1, 2, 3.., m), j ═ j (1, 2, 3.., n), m ═ n, n represents the number of each of the secondary classification indexes in the corresponding primary classification index;
S 21-2 confirming the judgment matrix through 1-9 scales;
S 21-3 and carrying out consistency check on the confirmed judgment matrix, wherein the steps are as follows:
(1) calculating a consistency index C 1 ：
Figure BDA0003655362180000022
Wherein λ is max The maximum eigenvalue of the judgment matrix is obtained;
(2) selecting a corresponding average random consistency index R according to the order number n of the judgment matrix 1 ；
(3) Calculating a consistency ratio C of the judgment matrix R ：
Figure BDA0003655362180000023
When C is present R If the consistency check is passed when the consistency check is less than 0.10, otherwise, the judgment matrix needs to be adjusted until C is met R ＜0.10；
S 21-4 And obtaining the subjective weight W of each secondary classification index through a geometric mean method and normalization processing according to the judgment matrix passing consistency check j ，
Figure BDA0003655362180000024
Wherein i ═ 1, 2, 3.., m), and j ═ 1, 2, 3.., n.
Further, step S 21-2 The method comprises the following steps: confirming the judgment matrix through 1-9 scales, wherein the confirmation method of the scale value comprises the following steps: the scale value 1 is defined as that each secondary classification index is consistent with the contrast importance of the scale value, the scale values 3, 5, 7 and 9 are sequentially defined as that one secondary classification index is slightly more important, obviously more important, strongly more important and extremely more important than the other secondary classification index, the importance defined by the scale value 2 is the middle point between the scale values 1 and 3, the importance defined by the scale value 4 is the middle point between the scale values 3 and 5, the importance defined by the scale value 6 is the middle point between the scale values 5 and 7, the importance defined by the scale value 8 is the middle point between the scale values 7 and 9, and the reciprocals of the scale values 3, 5, 7 and 9 are sequentially defined as that one secondary classification index is slightly less important, obviously less important, strongly less important and extremely less important than the other secondary classification index.
Further, step S 22 The method comprises the following steps:
S 22-1 selecting a plurality of project construction projects to be evaluated, and according to each project construction project to be evaluatedConstructing a decision matrix X for each secondary classification index in the corresponding primary classification indexes ij ，
Figure BDA0003655362180000031
Wherein i is (1, 2, 3.., m), j is (1, 2, 3.., n), m represents the number of the selected engineering construction projects to be evaluated, and n represents the number of each secondary classification index in the corresponding primary classification index;
S 22-2 for the decision matrix X ij Carrying out dimensionless processing to obtain a normalized matrix X ij '，
The normalization matrix X ij ' in, for the forward secondary classification index:
Figure BDA0003655362180000032
for reverse secondary classification indexes:
Figure BDA0003655362180000033
wherein x is ij Is the decision matrix X ij The original sample value of the jth secondary classification index of the ith project construction project to be evaluated,
Figure BDA0003655362180000034
is the decision matrix X ij The maximum value in the j-th secondary classification index,
Figure BDA0003655362180000035
is the decision matrix X ij Minimum value, x, in the j-th secondary classification index ij Is the normalized matrix X ij The standard value of the j-th secondary classification index of the ith project construction project to be evaluated after pretreatment;
S 22-3 carrying out proportion transformation on each standardized secondary classification index, and calculating the ratio of the sample value of the ith project to be evaluated under the jth secondary classification index to the ith secondary classification indexThe weight of the steel is heavy,
Figure BDA0003655362180000036
wherein i ═ 1, 2, 3.., m), and j ═ 1, 2, 3.., n;
S 22-4 calculating the entropy value of the jth secondary classification index,
Figure BDA0003655362180000037
wherein j ═ is (1, 2, 3.., n),
Figure BDA0003655362180000038
(0≤e j ＜1)；
S 22-5 calculating the objective weight of the j second-level classification index,
Figure BDA0003655362180000041
wherein j ═ is (1, 2, 3.., n).
Further, step S 23 The method specifically comprises the following steps: the subjective weight and the objective weight of each secondary classification index in the corresponding primary classification index are respectively subjected to linear combination to obtain the combined weight of each secondary classification index
Figure BDA0003655362180000042
Wherein t is more than or equal to 0 and less than 1.
Further, step S 2 And S 3 Also comprises the following steps: multiplying the normalization matrix by the combined weight of each secondary classification index in the corresponding primary classification index to obtain a weighted normalization matrix Y ij ，
Figure BDA0003655362180000043
Wherein i is (1, 2, 3.., m), j is (1, 2, 3.., n), m represents the number of the selected engineering construction projects to be evaluated, and n represents the number of each secondary classification index in the corresponding primary classification index.
Further, step S 3 The method comprises the following steps:
S 31 determining the weighted normalization matrix Y ij The maximum value and the minimum value of the jth secondary classification index in the ith project to be evaluated, and the set of the maximum values of all the secondary classification indexes in the ith project to be evaluated forms a positive ideal solution Y + The set of the minimum values of the secondary classification indexes in the ith project to be evaluated forms a negative ideal solution Y - ，
Figure BDA0003655362180000044
Figure BDA0003655362180000045
S 32 Calculating the grey correlation coefficient of each secondary classification index and the positive ideal solution in the corresponding primary classification index, calculating the grey correlation coefficient of each secondary classification index and the negative ideal solution,
Figure BDA0003655362180000046
Figure BDA0003655362180000047
wherein the content of the first and second substances,
Figure BDA0003655362180000048
the j secondary classification index is used for solving Y with positive ideal in the ith project construction project to be evaluated + The gray-scale correlation coefficient of (a),
Figure BDA0003655362180000049
the j-th secondary classification index is subjected to the comparison with the negative ideal solution Y in the ith project construction project to be evaluated - Grey correlation ofCoefficient rho is a resolution coefficient for improving the significance of difference between the correlation coefficients;
S 33 constructing each secondary classification index in the corresponding primary classification index and positive ideal solution Y + Gray correlation coefficient matrix r of + And constructing each secondary classification index and negative ideal solution Y - Gray correlation coefficient matrix r of - ，
Figure BDA0003655362180000051
Wherein i is (1, 2, 3.., m), j is (1, 2, 3.., n), m represents the number of the selected engineering construction projects to be evaluated, n represents the number of each secondary classification index in the corresponding primary classification index,
respectively calculating the j second-level classification index and the positive ideal solution Y + And negative ideal solution Y - Degree of gray correlation of
Figure BDA0003655362180000052
Figure BDA0003655362180000053
S 34 Calculating the gray correlation relative closeness D of each secondary classification index in the corresponding primary classification index j And is used as the score of each secondary classification index,
Figure BDA0003655362180000054
S 35 and obtaining the comprehensive score of the corresponding primary classification index according to the score of each secondary classification index in the corresponding primary classification index and by combining the combined weight of each secondary classification index.
Further, the target indexes comprise a general index A and an industry index R; the general index A can be divided into five first-level classification indexes including a construction period index B 1 Engineering quantity index B 2 The image progress index B 3 Resource utilizationIndex B 4 And indirect influence factor index B 5 (ii) a The construction period index B 1 Can be divided into four secondary classification indexes, including the ratio C of the advance time to the lag time of the total project completion 11 Milestone completion node ratio of lead time to lag time C 12 The ratio of the critical working lead time to the lag time C 13 Ratio of non-critical working lead time to lag time C 14 (ii) a The resource utilization index B 4 Can be divided into four secondary classification indexes including labor consumption balance C 41 Worker output value C 42 Main construction machinery utilization rate C 43 Resource utilization balance C 44 (ii) a The indirect influence factor index B 5 Can be divided into three secondary classification indexes including change factor C 51 Social environmental factor C 52 Other factors C 53 。
Furthermore, the industry index R is a power supply index, can be divided into four first-level classification indexes including a start-up index M 1 Engineering quantity index M 2 Installed production index M 3 And a power generation amount index M 4 (ii) a The installed production index M 3 Can be divided into three secondary classification indexes including production time deviation N 31 Production scale completion rate N 32 Installation scale completion rate N 33 。
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to a construction project progress management index system construction and comprehensive evaluation method, which comprises the following steps: constructing an engineering construction project progress management index system, wherein the index system comprises a target index, the target index is split to obtain a plurality of first-level classification indexes, and the first-level classification indexes are split to obtain a plurality of second-level classification indexes; respectively comparing the importance degrees of a plurality of secondary classification indexes in corresponding primary classification indexes by a method combining an APH (android Package) analytic hierarchy process and an entropy weight method to obtain the combined weight of each secondary classification index; obtaining the gray correlation relative closeness of each secondary classification index in the corresponding primary classification index by using a Topsis-gray correlation method, and obtaining the comprehensive score of the corresponding primary classification index by combining the combined weight of each secondary classification index; and then, obtaining the combination weight of each primary classification index by utilizing a method of combining an APH (advanced persistent Power) analytic hierarchy process and an entropy weight method, and obtaining the comprehensive score of the target index by combining the comprehensive score of each primary classification index, thereby obtaining the dynamic progress management evaluation grade of the engineering construction project. The invention evaluates the progress management degrees of different engineering construction projects so as to transversely compare the progress management levels of the engineering construction projects, when one of the engineering construction projects is found to have a low score, the problems in the engineering construction project schedule management can be immediately organized and analyzed, so as to make corresponding improvement plan and scheme to raise the level of project progress management, the project construction project can be finished at the specified time node, the delivery of the whole project is not delayed, a series of project disputes and economic losses caused by the delay of the progress are avoided, when one project construction project score is relatively high, can be used as an excellent demonstration project to carry out achievement display, summarize the successful experience of the project construction project progress management, so as to facilitate the subsequent development of the project of engineering construction and provide reference and reference for subsequent similar projects.
The invention provides a method for evaluating indexes by combining an APH (android Package) analytic hierarchy process and an entropy weight method and calculating the combined weight of the indexes and then evaluating the indexes by using a Topsis-gray correlation method; firstly, the APH analytic hierarchy process is very subjective and excessively depends on expert experience or human factors to judge the weight, so that certain limitation exists, the entropy weight method is an objective evaluation method, weighting is carried out on the basis of objective data, no subjective factor is involved, the defect is that the quality of original data is excessively depended on, the APH analytic hierarchy process and the entropy weight method are linearly combined, so that the weight distribution can be maximized and unified objectively and subjectively, and the accuracy of the weight distribution is greatly improved; secondly, the Topsis method is a common comprehensive evaluation method, which essentially represents the relative closeness degree between the alternative scheme and the ideal scheme through a function curve mode, to realize the evaluation of the scheme, however, the Topsis method does not reflect the difference between the variation trend of each factor in the scheme and the ideal scheme well, and the deviation of the conclusion is easy to occur under the condition of limited information, the defect of the Topsis method can be well neutralized by introducing the gray correlation method, which is an evaluation method in the gray system theory, different schemes are evaluated through the relevance among indexes, the grey relevance method can reflect the difference between the change trend of each factor in the scheme and the ideal scheme, has better applicability in the case of limited information, therefore, the Topsis method and the gray correlation method are combined, so that the scientificity and the accuracy of comprehensive evaluation can be effectively improved.
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FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a diagram of a generic class schedule management index architecture according to the present invention;
fig. 3 is a power class progress management index system diagram in the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings. These embodiments are merely illustrative of the present invention and are not intended to limit the present invention.
In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
A construction project progress management index system construction and comprehensive evaluation method comprises the following steps:
S 1 constructing an engineering construction project progress management index system, wherein the index system comprises target indexes, and the target indexes comprise a general index A and an industrial index R;
as shown in fig. 2, the general index a is applicable to each service plate, and can be divided into five first-level classification indexes, including a construction period index B 1 Engineering quantity index B 2 The image progress index B 3 Resource utilization index B 4 And indirect influence factor index B 5 (ii) a Indicator of construction period B 1 Four secondary classification indexes are obtained through lower-level splitting, and the four secondary classification indexes comprise the ratio C of the predicted total construction period completion lead time to the predicted total construction period completion lag time 11 Milestone completion node ratio of lead time to lag time C 12 Critical work lead time and lag timeC is a ratio of 13 Ratio of non-critical working lead time to lag time C 14 (ii) a Engineering quantity index B 2 The project completion rate is equal to the ratio of the actual project to the planned project; image progress indicator B 3 The image progress percentage of the accumulated finished project is equal to the ratio of the construction cost of the accumulated finished project amount to the total construction cost of the project; resource utilization index B 4 Obtaining four secondary classification indexes including labor consumption balance C through lower-level splitting 41 (ratio of the number of workers at the peak construction period to the average number of workers per day at the construction period), and worker output C 42 (ratio of total planned construction number to average number of people), utilization rate C of main construction machinery 43 (ratio of number of work shifts of construction machine to number of planned work shifts of construction machine), resource utilization balance C 44 (ratio of maximum resource demand to average resource demand during construction); index of indirect influence factor B 5 Obtaining three second-level classification indexes including a change factor C through lower-level splitting 51 Social environmental factor C 52 Other factors C 53 Wherein the factor C is changed 51 Design change factors for engineering construction projects;
as shown in FIG. 3, the industry index R is researched according to the progress index of the power supply project and can be divided into four first-level classification indexes including a start-up index M 1 Engineering quantity index M 2 Installed production index M 3 And the power generation amount index M 4 (ii) a Start-up index M 1 The deviation of the start-up scale is equal to the difference between the actual start-up scale and the planned start-up scale; engineering quantity index M 2 The project completion rate is equal to the ratio of the actual project to the planned project; installed production index M 3 Obtaining three second-level classification indexes including production time deviation N through lower-level splitting 31 (difference between actual production time and planned production time), production scale completion rate N 32 (ratio of cumulative completion of production to planned completion of production), and installation scale completion ratio N 33 (ratio of actual construction scale to planned construction scale); electric energy generation index M 4 The power generation completion rate is equal to the ratio of the accumulated power generation amount to the planned power generation amount;
S 2 as shown in fig. 1, an APH analytic hierarchy process is used to construct a judgment matrix for each secondary classification index in the corresponding primary classification index through 1-9 scales, and a geometric mean method is used to calculate the subjective weight of each secondary classification index, which specifically includes the following steps:
S 2-1 constructing a judgment matrix A of each secondary classification index in the corresponding primary classification indexes ij ，
Figure BDA0003655362180000081
Wherein i is (1, 2, 3.., m), j is (1, 2, 3.., n), m is n, and n represents the number of each secondary classification index in the corresponding primary classification index;
S 2-2 confirming the judgment matrix through 1-9 scales, wherein the confirmation method of the scale value comprises the following steps: the scale value 1 is defined as that each secondary classification index is consistent with the contrast importance of the scale value, the scale values 3, 5, 7 and 9 are sequentially defined as that one secondary classification index is slightly more important, obviously more important, strongly more important and extremely more important than the other secondary classification index, the importance defined by the scale value 2 is the middle point of the scale values 1 and 3, the importance defined by the scale value 4 is the middle point of the scale values 3 and 5, the importance defined by the scale value 6 is the middle point of the scale values 5 and 7, the importance defined by the scale value 8 is the middle point of the scale values 7 and 9, and the reciprocals of the scale values 3, 5, 7 and 9 are sequentially defined as that one secondary classification index is slightly less important, obviously less important, strongly less important and extremely less important than the other secondary classification index;
S 2-3 and carrying out consistency check on the confirmed judgment matrix, wherein the steps are as follows:
(1) calculating a consistency index C 1 ：
Figure BDA0003655362180000082
Wherein λ max Judging the maximum eigenvalue of the matrix;
(2) referring to the following table, selecting corresponding average random consistency index R according to the order number n of the judgment matrix 1 ；
n 1 2 3 4 5 6 7 8 9
R 1 0 0 0.52 0.89 1.12 1.24 1.32 1.41 1.5
(3) Calculating a consistency ratio C of the decision matrix R ：
Figure BDA0003655362180000091
When C is present R If the consistency check is passed when the consistency check is less than 0.10, otherwise, the judgment matrix needs to be adjusted until C is met R ＜0.10；
S 2-4 And obtaining the subjective weight W of each secondary classification index by a geometric mean method and normalization processing according to the judgment matrix passing consistency check j ，
Figure BDA0003655362180000092
Wherein i ═ 1, 2, 3.., m), and j ═ 1, 2, 3.., n;
S 3 evaluating the orderliness and the effectiveness of each secondary classification index in the corresponding primary classification index by an entropy weight method through an information entropy theory, constructing a decision matrix and calculating the objective weight of each secondary classification index, wherein the method specifically comprises the following steps:
S 3-1 selecting a plurality of project construction projects to be evaluated, and constructing a decision matrix X according to each secondary classification index in the corresponding primary classification index of each project construction project to be evaluated ij ，
Figure BDA0003655362180000093
The evaluation method comprises the steps of obtaining a first-level classification index, a second-level classification index and a third-level classification index, wherein i is (1, 2, 3.., m), j is (1, 2, 3.., n), m represents the number of selected engineering construction projects to be evaluated, and n represents the number of each second-level classification index in the corresponding first-level classification index;
S 3-2 the decision matrix X ij Carrying out dimensionless processing to obtain a normalized matrix X ij '，
Normalized matrix X ij ' in, for the forward secondary classification index:
Figure BDA0003655362180000094
for reverse secondary classification indexes:
Figure BDA0003655362180000095
wherein x is ij Is a decision matrix X ij The original sample value of the jth secondary classification index of the ith project construction project to be evaluated,
Figure BDA0003655362180000096
is a decision matrix X ij The maximum value in the j-th secondary classification index,
Figure BDA0003655362180000097
is a decision matrix X ij Minimum value, x, in the j-th secondary classification index ij ' is a normalized matrix X ij The standard value of the j-th secondary classification index of the ith project construction project to be evaluated after pretreatment;
S 3-3 carrying out proportion transformation on each standardized secondary classification index, calculating the proportion of the sample value of the ith project construction project to be evaluated under the jth secondary classification index in the secondary classification index,
Figure BDA0003655362180000101
wherein i ═ 1, 2, 3.., m), and j ═ 1, 2, 3.., n;
S 3-4 calculating the entropy value of the j second-level classification index,
Figure BDA0003655362180000102
wherein j ═ is (1, 2, 3.., n),
Figure BDA0003655362180000103
(0≤e j ＜1)；
S 3-5 calculating the objective weight of the j second-level classification index,
Figure BDA0003655362180000104
wherein j ═ is (1, 2, 3.., n);
S 4 two of the corresponding first class classification indexesThe subjective weight and the objective weight of the level classification indexes are respectively linearly combined to obtain the combined weight of each level classification index
Figure BDA0003655362180000105
In particular to
Figure BDA0003655362180000106
Wherein t is more than or equal to 0 and less than 1;
S 5 multiplying the normalized matrix by the combined weight of each secondary classification index in the corresponding primary classification index to obtain a weighted normalized matrix Y ij ，
Figure BDA0003655362180000107
The evaluation method comprises the steps of obtaining a first-level classification index, a second-level classification index and a third-level classification index, wherein i is (1, 2, 3.., m), j is (1, 2, 3.., n), m represents the number of selected engineering construction projects to be evaluated, and n represents the number of each second-level classification index in the corresponding first-level classification index;
S 6 the method comprises the following steps of obtaining scores of all secondary classification indexes in corresponding primary classification indexes by using a Topsis-gray correlation method, and obtaining comprehensive scores of the corresponding primary classification indexes by combining the combined weights of all the secondary classification indexes, wherein the method specifically comprises the following steps:
S 6-1 determining a weighted normalization matrix Y ij The maximum value and the minimum value of the jth secondary classification index in the ith project construction project to be evaluated, and the maximum value, namely the set of ideal optimal values of each secondary classification index in the ith project construction project to be evaluated form a positive ideal solution Y + The minimum value of each secondary classification index in the ith project to be evaluated, namely the set of the worst value, forms a negative ideal solution Y - ，
Figure BDA0003655362180000108
Figure BDA0003655362180000111
S 6-2 Calculating the grey correlation coefficient between each secondary classification index and the positive ideal solution in the corresponding primary classification index, calculating the grey correlation coefficient between each secondary classification index and the negative ideal solution,
Figure BDA0003655362180000112
Figure BDA0003655362180000113
wherein the content of the first and second substances,
Figure BDA0003655362180000114
the j secondary classification index is used for solving Y with positive ideal in the ith project construction project to be evaluated + The gray-scale correlation coefficient of (a),
Figure BDA0003655362180000115
the j-th secondary classification index is subjected to the comparison with the negative ideal solution Y in the ith project construction project to be evaluated - Rho is a resolution coefficient for improving the significance of the difference between the correlation coefficients, and rho belongs to [0,1 ]]Generally, 0.5 is taken;
S 6-3 constructing each second-level classification index in corresponding first-level classification indexes and positive ideal solution Y + Gray correlation coefficient matrix r of + And constructing each secondary classification index and negative ideal solution Y - Gray correlation coefficient matrix r of - ，
Figure BDA0003655362180000116
Wherein i is (1, 2, 3.., m), j is (1, 2, 3.., n), m represents the number of the selected engineering construction projects to be evaluated, n represents the number of each secondary classification index in the corresponding primary classification index,
respectively calculating the j second-level classification index and the positive ideal solution Y + And negative ideal solution Y - Degree of gray correlation of
Figure BDA0003655362180000117
Figure BDA0003655362180000118
S 6-4 Calculating the gray correlation relative closeness D of each secondary classification index in the corresponding primary classification index j ，
Figure BDA0003655362180000119
The gray correlation relative closeness D of each secondary classification index j As the score of each secondary classification index, the gray correlation relative closeness D of the secondary classification index j The larger the grade, the closer the grade two classification index is to the positive ideal sample, the better the grade is, otherwise, the gray correlation relative closeness degree D of the grade two classification index is j The smaller the grade is, the closer the secondary classification index is to the negative ideal sample, and the worse the grade is;
S 6-5 obtaining a comprehensive score of the corresponding primary classification index according to the score of each secondary classification index in the corresponding primary classification index and by combining the combined weight of each secondary classification index;
S 7 and repeating the step S 2 To S 4 And obtaining the comprehensive score of the target index by combining the comprehensive scores of all the primary classification indexes, which specifically comprises the following steps: repeating step S 2 To S 4 And comparing the importance degrees of the plurality of primary classification indexes by combining an APH (advanced analytical hierarchy) analysis method and an entropy weight method to obtain the combined weight of each primary classification index, and obtaining the comprehensive score of the target index by combining the comprehensive score of each primary classification index, wherein the higher the comprehensive score of the target index is, the higher the progress management level of the engineering construction project is, and conversely, the lower the comprehensive score of the target index is, the lower the progress management level of the engineering construction project is.
According to the method, the construction and the rating of the index system are carried out on the progress management of the engineering construction project, so that the objective evaluation and the transverse comparison of the progress management of different engineering construction projects are realized.
According to the invention, the APH analytic hierarchy process and the entropy weight process are linearly combined, so that the weight distribution of the index can be unified to the maximum of subjectivity and objectivity, and the accuracy of the weight distribution of the index is greatly improved; and the situations of uncertain decision information, complex decision problem and the like exist in the field of engineering management, the gray correlation method is suitable for the multi-attribute decision problem of 'poor information', the calculation amount is small, and the method is easy to fuse with other methods, so that the science and the accuracy of comprehensive evaluation can be effectively improved by combining the Topsis method and the gray correlation method.
The above is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and substitutions can be made without departing from the technical principle of the present invention, and these modifications and substitutions should also be regarded as the protection scope of the present invention.

Claims (10)

1. A construction project progress management index system construction and comprehensive evaluation method is characterized by comprising the following steps:
S 1 constructing an engineering construction project progress management index system, wherein the index system comprises a target index, the target index is split to obtain a plurality of first-level classification indexes, and the first-level classification indexes are split to obtain a plurality of second-level classification indexes;
S 2 comparing the importance degrees of a plurality of secondary classification indexes in the corresponding primary classification indexes respectively by a method combining an APH (advanced peripheral health) analytic hierarchy process and an entropy weight method to obtain the combined weight of each secondary classification index;
S 3 obtaining the scores of all secondary classification indexes in the corresponding primary classification indexes by using a Topsis-gray correlation method, and obtaining the comprehensive scores of the corresponding primary classification indexes by combining the combined weight of all the secondary classification indexes;
S 4 obtaining the combined weight of each class classification index by combining an APH (advanced peripheral Package) analytic hierarchy process and an entropy weight method, and combining each class classification indexAnd classifying the comprehensive scores of the indexes to obtain the comprehensive scores of the target indexes, and further obtaining the dynamic progress management evaluation level of the engineering construction project.
2. The method for constructing and comprehensively evaluating the project progress management index system according to claim 1, wherein the step S is 2 The method comprises the following steps:
S 21 respectively constructing a judgment matrix for each secondary classification index in the corresponding primary classification index by adopting an APH (advanced peripheral component analysis) analytic hierarchy process through 1-9 scales, and calculating the subjective weight of each secondary classification index by utilizing a geometric mean method;
S 22 evaluating the orderliness and the effectiveness of each secondary classification index in the corresponding primary classification index by an entropy weight method through an information entropy theory, constructing a decision matrix and calculating the objective weight of each secondary classification index;
S 23 and respectively carrying out linear combination on the subjective weight and the objective weight of each secondary classification index in the corresponding primary classification index to obtain the combined weight of each secondary classification index.
3. The method for constructing and comprehensively evaluating the project progress management index system according to claim 2, wherein the step S is 21 The method comprises the following steps:
S 21-1 constructing a judgment matrix A of each secondary classification index in the corresponding primary classification indexes ij ，
Figure FDA0003655362170000011
Wherein i ═ i (1, 2, 3.., m), j ═ j (1, 2, 3.., n), m ═ n, n represents the number of each of the secondary classification indexes in the corresponding primary classification index;
S 21-2 confirming the judgment matrix through 1-9 scales;
S 21-3 and carrying out consistency check on the confirmed judgment matrix, and the steps are as follows：
(1) Calculating a consistency index C 1 ：
Figure FDA0003655362170000021
Wherein λ max The maximum eigenvalue of the judgment matrix;
(2) selecting a corresponding average random consistency index R according to the order number n of the judgment matrix 1 ；
(3) Calculating a consistency ratio C of the judgment matrix R ：
Figure FDA0003655362170000022
When C is present R If the consistency check is passed, if the consistency check is less than 0.10, otherwise, the judgment matrix needs to be adjusted until C is met R ＜0.10；
S 21-4 And obtaining the subjective weight W of each secondary classification index by a geometric mean method and normalization processing according to the judgment matrix passing consistency check j ，
Figure FDA0003655362170000023
Wherein i ═ 1, 2, 3.., m), and j ═ 1, 2, 3.., n.
4. The method for constructing and comprehensively evaluating the project construction project progress management index system according to claim 3, wherein the step S 21-2 The method comprises the following steps: confirming the judgment matrix through 1-9 scales, wherein the confirmation method of the scale value comprises the following steps: the scale value 1 is defined as that each secondary classification index is consistent with the contrast importance of the scale value 3, 5, 7 and 9, the scale value 3, 5, 7 and 9 are sequentially defined as that one secondary classification index is slightly more important, obviously more important, strongly more important and extremely more important than the other secondary classification index, the importance defined by the scale value 2 is the middle point of the scale values 1 and 3, the importance defined by the scale value 4 is the middle point of the scale values 3 and 5, the importance defined by the scale value 6 is the middle point of the scale values 5 and 7, the importance defined by the scale value 8 is the middle point of the scale values 7 and 9, and the reciprocals of the scale values 3, 5, 7 and 9 are in accordance with the contrast importance of the scale valuesSecondary is defined as one secondary classifier being slightly less important, significantly less important, strongly less important, or extremely less important than the other secondary classifier.
5. The method for constructing and comprehensively evaluating the project construction project progress management index system according to claim 3, wherein the step S 22 The method comprises the following steps:
S 22-1 selecting a plurality of project construction projects to be evaluated, and constructing a decision matrix X according to each secondary classification index in the corresponding primary classification index of each project construction project to be evaluated ij ，
Figure FDA0003655362170000024
The evaluation method comprises the steps of obtaining a first-level classification index, wherein the first-level classification index comprises a first-level classification index and a second-level classification index, i is (1, 2, 3.., m), and j is (1, 2, 3.., n), m represents the number of the selected engineering construction projects to be evaluated, and n represents the number of each second-level classification index in the corresponding first-level classification index;
S 22-2 for the decision matrix X ij Carrying out dimensionless processing to obtain a normalized matrix X ij '，
The normalization matrix X ij ' in, for the forward secondary classification index:
Figure FDA0003655362170000031
for reverse secondary classification indexes:
Figure FDA0003655362170000032
wherein x is ij Is the decision matrix X ij The original sample value of the jth secondary classification index of the ith project construction project to be evaluated,
Figure FDA0003655362170000039
is the decision matrix X ij The maximum value of the j-th secondary classification index,
Figure FDA00036553621700000310
is the decision matrix X ij Minimum value, x, in the j-th secondary classification index ij Is the normalized matrix X ij The standard value of the j-th secondary classification index of the ith project construction project to be evaluated after pretreatment;
S 22-3 performing proportion transformation on each standardized secondary classification index, calculating the proportion of the sample value of the ith engineering construction project to be evaluated under the jth secondary classification index in the secondary classification index,
Figure FDA0003655362170000033
wherein i ═ 1, 2, 3.., m), and j ═ 1, 2, 3.., n;
S 22-4 calculating the entropy value of the jth secondary classification index,
Figure FDA0003655362170000034
wherein j ═ is (1, 2, 3.., n),
Figure FDA0003655362170000035
S 22-5 calculating the objective weight of the j second-level classification index,
Figure FDA0003655362170000036
where j ═ is (1, 2, 3.., n).
6. The method for constructing and comprehensively evaluating the project progress management index system according to claim 5, wherein the step S is 23 The method specifically comprises the following steps: the subjective weight and the objective weight of each secondary classification index in the corresponding primary classification index are respectively subjected to linear combination to obtain the combined weight of each secondary classification index
Figure FDA0003655362170000037
Figure FDA0003655362170000038
Wherein t is more than or equal to 0 and less than 1.
7. The method for constructing and comprehensively evaluating the project construction project progress management index system according to claim 6, wherein the step S 2 And S 3 Also comprises the following steps: multiplying the normalization matrix by the combined weight of each secondary classification index in the corresponding primary classification index to obtain a weighted normalization matrix Y ij ，
Figure FDA0003655362170000041
Wherein i is (1, 2, 3.., m), j is (1, 2, 3.., n), m represents the number of the selected engineering construction projects to be evaluated, and n represents the number of each secondary classification index in the corresponding primary classification index.
8. The method for constructing and comprehensively evaluating the project progress management index system according to claim 7, wherein the step S is 3 The method comprises the following steps:
S 31 determining the weighted normalization matrix Y ij The maximum value and the minimum value of the jth secondary classification index in the ith project to be evaluated, and the set of the maximum values of all the secondary classification indexes in the ith project to be evaluated forms a positive ideal solution Y + The set of the minimum values of the secondary classification indexes in the ith project to be evaluated forms a negative ideal solution Y - ，
Figure FDA0003655362170000042
Figure FDA0003655362170000043
S 32 Calculating the grey correlation coefficient of each secondary classification index and the positive ideal solution in the corresponding primary classification index, calculating the grey correlation coefficient of each secondary classification index and the negative ideal solution,
Figure FDA0003655362170000044
Figure FDA0003655362170000045
wherein the content of the first and second substances,
Figure FDA0003655362170000046
the j-th secondary classification index is compared with the positive ideal Y in the ith project to be evaluated + The gray-associated coefficient of (a) is,
Figure FDA0003655362170000047
the j-th secondary classification index is compared with the negative ideal solution Y in the ith engineering construction project to be evaluated - Rho is a resolution coefficient for improving the significance of the difference between the correlation coefficients;
S 33 constructing each secondary classification index in the corresponding primary classification index and positive ideal solution Y + Gray correlation coefficient matrix r of + And constructing each secondary classification index and negative ideal solution Y - Gray correlation coefficient matrix r of - ，
Figure FDA0003655362170000051
Wherein i ═ 1, 2, 3., m) and j ═ 1, 2, 3., n), m represents the number of the selected engineering construction projects to be evaluated, n represents each secondary classification in the corresponding primary classification indexThe number of the indexes is as follows,
respectively calculating the j second-level classification index and the positive ideal solution Y + And negative ideal solution Y - Degree of gray correlation of
Figure FDA0003655362170000052
Figure FDA0003655362170000053
S 34 Calculating the gray correlation relative closeness D of each secondary classification index in the corresponding primary classification index j And is used as the grade of each secondary classification index,
Figure FDA0003655362170000054
S 35 and obtaining the comprehensive score of the corresponding primary classification index according to the score of each secondary classification index in the corresponding primary classification index and by combining the combined weight of each secondary classification index.
9. The construction project progress management index system construction and comprehensive evaluation method according to claim 1, characterized in that: the target indexes comprise a general index A and an industry index R; the general index A can be divided into five first-level classification indexes including a construction period index B 1 Engineering quantity index B 2 The image progress index B 3 Resource utilization index B 4 And indirect influence factor index B 5 (ii) a The construction period index B 1 Can be divided into four secondary classification indexes, including the ratio C of the advance time to the lag time of the total project completion 11 Milestone completion node ratio of lead time to lag time C 12 The ratio of the key work lead time to the lag time C 13 Ratio of non-critical working lead time to lag time C 14 (ii) a The resource utilization index B 4 Can be divided into four secondary classification indexes including the balance of labor consumptionC 41 Worker output value C 42 Main construction machinery utilization rate C 43 Resource utilization balance C 44 (ii) a The indirect influence factor index B 5 Can be divided into three secondary classification indexes including change factor C 51 Social environmental factor C 52 Other factors C 53 。
10. The engineering construction project progress management index system construction and comprehensive evaluation method according to claim 9, characterized in that: the industry index R is a power supply index and can be divided into four first-level classification indexes including a start-up index M 1 Engineering quantity index M 2 Installation operation index M 3 And the power generation amount index M 4 (ii) a The installed production index M 3 Can be divided into three secondary classification indexes including production time deviation N 31 Production scale completion rate N 32 Installation scale completion rate N 33 。
CN202210557115.5A 2022-05-20 2022-05-20 Construction and comprehensive evaluation method for project progress management index system Pending CN115018452A (en)

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Publication number Priority date Publication date Assignee Title
CN116485282A (en) * 2023-06-19 2023-07-25 浪潮通用软件有限公司 Data grouping method, equipment and medium based on multidimensional index dynamic competition

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116485282A (en) * 2023-06-19 2023-07-25 浪潮通用软件有限公司 Data grouping method, equipment and medium based on multidimensional index dynamic competition
CN116485282B (en) * 2023-06-19 2023-09-29 浪潮通用软件有限公司 Data grouping method, equipment and medium based on multidimensional index dynamic competition

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