CN109299550B - Steel structure bridge manufacturing decision evaluation method - Google Patents
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Abstract
The invention provides a steel structure bridge manufacturing decision evaluation method, which establishes a corresponding manufacturing task model by hierarchically dividing a steel structure bridge and establishes a corresponding manufacturing enterprise model according to the manufacturing task model. And constructing an ontology model based on the manufacturing task and the manufacturing enterprise model, and obtaining alternative enterprise schemes through ontology reasoning. And constructing a multi-attribute evaluation system according to the obtained alternative enterprise scheme, designing a triangular fuzzy number-TOPSIS method to determine the weight, realizing the sequencing of the manufacturing scheme and obtaining the optimal decision. The invention relates to a steel structure bridge manufacturing decision evaluation method, which is characterized in that a manufacturing task information model is matched with a manufacturing enterprise information model, a body model is established, a multi-attribute decision evaluation system is established aiming at the body model, a triangular fuzzy number is adopted to determine the weight of attributes, a scheme is sequenced by combining a construction triangular fuzzy number-TOPSIS method to obtain an optimal decision, the self-adaptive and autonomous production of a steel structure bridge is realized, and the intelligent and autonomous degree of the bridge manufacturing process is improved.
Description
Technical Field
The invention relates to the field of steel structure bridge manufacturing, relates to a steel structure bridge manufacturing decision, and particularly relates to a steel structure bridge manufacturing decision evaluation method.
Background
With the development and application of emerging technologies such as the Internet of things, an information physical fusion system, a digital twin and the like in the manufacturing industry, the real-time acquisition, processing and analysis of the data of the operation state in the manufacturing process are realized in the manufacturing of traditional mechanical products, and the transparency degree in the production process is improved. However, the network collaborative manufacturing technology established for the industrial construction characteristics of the steel structure bridge is still vacant, and it is difficult to obtain the optimal scheme for decision evaluation in the manufacturing process of the steel structure bridge, and further difficult to realize the autonomous and intelligent production and management of the steel structure bridge.
Aiming at the problem of selecting the manufacturing scheme of the steel structure bridge, the bottleneck problem is caused by knowing the structural characteristics and the manufacturing process of the steel structure bridge and how to accurately describe the optimal manufacturing scheme of the steel structure bridge by a mathematical method.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a steel structure bridge manufacturing decision evaluation method, and solve the technical problem that automatic decision of a steel structure bridge manufacturing scheme cannot be established in the prior art so as to realize automatic intelligent production and management of the steel structure bridge.
In order to solve the technical problems, the invention adopts the following technical scheme to realize, and the method comprises the following steps:
the method comprises the following steps: firstly, establishing a corresponding manufacturing task model by hierarchically dividing a steel structure bridge, supplementing basic information, task requirements and special requirements of the manufacturing task, and respectively establishing a basic information model of the manufacturing task, a requirement model of the manufacturing task and a special requirement model of the manufacturing task;
step two: establishing a manufacturing task information model based on the manufacturing task model, the basic information model of the manufacturing task, the requirement model of the manufacturing task and the special requirement model of the manufacturing task;
step three: establishing a manufacturing enterprise information model based on basic information of a manufacturing enterprise, manufacturing capacity of the manufacturing enterprise and business state information of the manufacturing enterprise;
step four: establishing a steel structure bridge manufacturing matching model based on the manufacturing task information model and the manufacturing enterprise information model, and obtaining an alternative enterprise scheme through ontology reasoning;
step five: establishing an alternative manufacturing scheme model, an attribute model of the alternative manufacturing scheme and weight models of all attributes in the alternative manufacturing scheme according to the obtained alternative enterprise scheme, and constructing an initial multi-attribute decision matrix;
step six: and determining triangular fuzzy number complementary judgment matrixes of all attributes of all schemes by using a triangular fuzzy number-TOPSIS method, carrying out normalized processing on the initial multi-attribute decision matrix by using vector transformation to obtain a normalized multi-attribute decision matrix, and solving and sequencing the normalized multi-attribute decision matrix to obtain the optimal scheme.
Further, in step five, the set of candidate enterprise solution models P ═ P 1 ,P 2 ,…,P m }，P i I is more than or equal to 1 and less than or equal to m which is the number of alternative manufacturing schemes;
set of attribute models U ═ U 1 ,u 2 ,…,u n }，u j J is more than or equal to 1 and less than or equal to m, and n is the number of attributes of a certain scheme;
set of weight models W ═ W 1 ,w 2 ,…,w n }，w j J is more than or equal to 1 and less than or equal to m, and the re-definition of ownership is as follows: w is a 1 +w 2 +…+w n ＝1；
The initial multi-attribute decision matrix X constructed according to the model of the alternative manufacturing scheme, the attribute model corresponding to a specific scheme and the weight models of all the attributes in the scheme is as follows:
wherein x is ij Evaluating an index value for an initial decision of a jth attribute of an ith scheme in the initial multi-attribute decision matrix.
Further, the sixth step comprises the following steps:
a) note the book
Defining expected values of triangular blur numbers for triangular blur numbers
Is calculated by the formula：
b) evaluating the attributes pairwise according to the evaluation purpose and the evaluation index, establishing a triangular fuzzy number complementation judgment matrix, and evaluating the triangular fuzzy number complementation judgment matrix A at the kth time k ，
Wherein
In the formula
Respectively representing the most conservative evaluation value, the most possible evaluation value and the most optimistic evaluation value of the ith evaluation index relative to the jth evaluation index, which are obtained by the k-th evaluation of the expert system;
c) using the weight to aggregate the evaluation values to obtain the comprehensive triangular fuzzy number complementary judgment matrix elements related to the evaluation indexes:
in the formula: i, j is 1,2, …, n, k is the number of evaluations; omega k Authority for the expert system's kth assessment;
d) triangular blur number weight for ith index
The calculation was performed using the following formula:
in the formula (I), the compound is shown in the specification,
the most conservative evaluation value of the ith evaluation index relative to the jth evaluation index is shown;
a most probable evaluation value indicating an ith evaluation index relative to a jth evaluation index;
a second evaluation value indicating the most optimistic evaluation value of the ith evaluation index relative to the jth evaluation index;
e) here, the decision maker assumes a neutral attitude, so taking λ equal to 0.5, the expectation calculation formula becomes:
f) calculating the weight omega of the ith attribute of each scheme in the alternative enterprise scheme set P i ，
g) Carrying out weight statistics on the multi-attribute of each scheme in P, establishing triangular fuzzy number complementary judgment matrixes of all the attributes of all the schemes, carrying out normalized processing on the initial multi-attribute decision matrix by using the triangular fuzzy number complementary judgment matrixes by adopting a vector transformation method to obtain a normalized multi-attribute decision matrix Y,
wherein, y ij Representing the weight of the jth attribute of the ith scheme;
h) determining a set C of ideal solution solutions based on normalized multi-attribute decision matrix values j * And set C of negative ideal solution schemes j 0 Calculating weighted Euclidean distances d of each scheme from the ideal solution and the negative ideal solution j * And d j 0 And a composite proximity index f j ；
i) And sequencing all the schemes from small to large according to the comprehensive nearness index, wherein the scheme with the maximum comprehensive nearness index is the optimal scheme.
Further, the choice of λ in step a) depends on the risk attitude of the decision maker:
when the decision maker is biased to optimism, lambda is more than 0.5 and less than 1;
when the decision maker is biased towards pessimism, 0 < lambda < 0.5.
Further, the step f) further comprises consistency verification of the weight value, and the judgment index CI calculation formula of the consistency verification is as follows:
in the formula:
maximum characteristic root value of;
is composed of
In the expectation that the position of the target is not changed,
the consistency judgment coefficient CR is calculated by the following formula:
in the formula: RI is an average random consistency index; when CR is given<And when the sum of the values of the triangular fuzzy number judgment matrix is 0.1, the triangular fuzzy number judgment matrix is considered to pass the consistency test, and if the sum of the values of the triangular fuzzy number judgment matrix is not passed, the triangular fuzzy number judgment matrix is reestablished.
The average random consistency index RI is related to the dimension of the judgment matrix, and the specific corresponding relation is as follows:
| dimension of matrix | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
Further, in the step one, the steel structure bridge divides into according to from low to high in proper order: the characteristic layer, the segment layer, the bridge span layer and the bridge layer.
Further, in step three, the mathematical description of the manufacturing enterprise information model is as follows: ME { ME _ Basic, ME _ Capacity, ME _ Status, ME _ Auxiliary }, where ME _ Basic represents Basic information of the manufacturing enterprise, ME _ Capacity represents manufacturing capability of the manufacturing enterprise, ME _ Status represents Status information of the enterprise, and ME _ Auxiliary represents Auxiliary information of the enterprise.
Compared with the prior art, the invention has the following technical effects:
the invention provides a steel structure bridge manufacturing decision evaluation method, which comprises the steps of carrying out hierarchical division on a steel structure bridge, establishing a corresponding manufacturing task model, establishing a corresponding manufacturing enterprise model according to the manufacturing task model, matching the manufacturing task information model with the manufacturing enterprise information model, establishing a body model, obtaining an alternative enterprise scheme through body reasoning, constructing a multi-attribute evaluation system according to the obtained alternative enterprise scheme, determining the weight of attributes by adopting triangular fuzzy numbers, constructing a triangular fuzzy number-TOPSIS method by combining with the TOPSIS method to sort the schemes according to the magnitude of comprehensive proximity indexes and obtain an optimal decision, laying a foundation for the self-adaptation and autonomous production of the steel structure bridge, and improving the intelligent and autonomous degree of the bridge manufacturing process.
Drawings
FIG. 1 is a diagram of implementation steps of a steel structure bridge manufacturing decision evaluation method;
FIG. 2 is a manufacturing task information model diagram;
FIG. 3 is a diagram of a manufacturing enterprise information model.
Detailed Description
The present invention is not limited to the following embodiments, and all equivalent changes based on the technical solutions of the present invention fall within the protection scope of the present invention.
Example (b):
the S bridge is located in a county of Shaanxi province and constructed in 2001, and the bearing capacity of the S bridge is reduced due to perennial overload use. In order to ensure the operation safety of the bridge, the bridge is entrusted by a local highway administration, the bridge diseases are subjected to site reconnaissance, and the construction drawing design work is carried out aiming at the bridge diseases. Finally, the scheme of reconstructing the upper structure and the bridge deck system is determined, and the novel structure of the steel plate composite beam is adopted.
In order to ensure the construction quality, the scheme mainly has the following regulations in the aspect of manufacturing the steel structure bridge:
(1) steel structure bridge manufacturing enterprises must have two or more levels of qualification for processing and manufacturing corresponding steel structures.
(2) The manufacture and acceptance of steel beams must use certified measuring instruments and should be operated according to the relevant regulations.
(3) The steel used by the steel beam is in accordance with the regulations of GB/T1591-2008.
(4) The scheme of plate division in factory manufacture must be applied after the confirmation of original design unit.
(5) Cutting and edge processing: the bridge steel structure plate should be cut by flame in principle, and the cutting of main parts should preferably adopt precise cutting such as numerical control, automatic and semi-automatic cutting.
Referring to fig. 1, the present embodiment provides a steel structure bridge manufacturing decision evaluation method, including the following steps:
firstly, a steel structure bridge is divided in layers, a task information model of each layer is established according to tasks of each layer, a corresponding manufacturing task model is further established, basic manufacturing task information, task requirements and special requirements are supplemented, and a basic manufacturing task information model, a manufacturing task requirement model and a manufacturing task special requirement model are respectively established;
step two, establishing a manufacturing task information model based on the manufacturing task model, the manufacturing task basic information model, the manufacturing task requirement model and the manufacturing task special requirement model;
establishing a manufacturing enterprise information model based on the basic information, the manufacturing capacity and the business state information of the manufacturing enterprise;
fourthly, establishing a body model based on the manufacturing task information model and the manufacturing enterprise information model, wherein the body model is a steel structure bridge manufacturing matching model, and obtaining alternative enterprise schemes through body reasoning;
step five, establishing a corresponding alternative enterprise scheme model, an attribute model corresponding to a specific scheme and weight models of all attributes in the scheme according to the alternative enterprise scheme obtained in the step four, and constructing an initial multi-attribute decision matrix;
and step six, determining triangular fuzzy number judgment matrixes of all attributes of all schemes by utilizing a triangular fuzzy number-TOPSIS method, carrying out normalized processing on the initial multi-attribute decision matrix by utilizing vector transformation to obtain a normalized multi-attribute decision matrix, and solving and sequencing the normalized multi-attribute decision matrix to obtain an optimal scheme.
The method of the invention comprises two parts:
the first part, establish the body model, deduce and obtain the alternative enterprise scheme through the body, including the above-mentioned step one to step four;
and a second part, establishing an initial multi-attribute evaluation decision matrix, and obtaining a normalized multi-attribute decision matrix by a triangular fuzzy number-TOPSIS method so as to obtain an optimal scheme, wherein the optimal scheme comprises the fifth step to the sixth step.
A first part: and establishing a body model, and describing the manufacturing task of the steel structure bridge, wherein the hierarchical structure of the steel structure bridge is firstly determined. The elements of the manufacturing task are determined by analyzing the hierarchy.
Referring to fig. 2, in step one, the steel structure bridge is divided into the following parts from low to high in sequence according to the levels: characteristic layer, segment layer, bridge span layer and bridge layer.
(1) Feature layer
The characteristic layer is the lowest level of the steel structure bridge and cannot be continuously subdivided. The characteristics of steel structure bridge include:
firstly, material characteristics M: the first step of manufacturing the steel structure bridge is to purchase materials which mainly comprise section steel, steel plates and connecting pieces. Such as hot rolling or welding H-section steel using different materials (e.g., Q345B); some materials (e.g., steel plates) need to be delivered along with associated certificates, and manufacturing companies need to record information such as the certificates and lot numbers to ensure that quality information can be tracked.
Secondly, shape characteristics: the shape characteristics of the steel structure bridge mainly include a body (H-shaped, I-shaped and the like), a surface (a friction surface, a coating surface and the like), a hole (a bolt hole and the like), a groove (an X-shaped groove, a V-shaped groove, a U-shaped groove and the like), and assembly (bolt connection, welding and the like).
(iii) precision feature A: the precision characteristics are mainly as follows: sand blasting grade, surface roughness, coating thickness and processing precision (dimensional precision, shape precision and position precision).
Fourthly, geometric feature G: the geometric features are used to describe the geometry of the manufacturing task.
Process characteristic T: the process is a method and a process for converting steel structure bridges from raw materials to finished products, and the specific processing process is shown in section 2.3.
Processing resource characteristic R: the collection of resources, including personnel, equipment, etc., that implement the manufacturing process.
(2) Segment layer
A feature is an element of a segment, and a segment is a collection of features. And a segment is the smallest unit of manufacture that an enterprise performs. The task of fabricating the segment layer is denoted by T _ Se here. According to the definition of the above characteristics, the assembly of the manufacturing tasks of the segment layer is described as follows:
T_Se i ＝{M,S,A,G,T,R}
in the formula: t _ Se i The manufacturing task for segment number i is indicated.
(3) Bridge spanning layer: the bridge span layer is a set of all sections between two piers, and the sections are connected to form a bridge span. The bridge cross-layer manufacturing task is a collection of segment-layer manufacturing tasks. The aggregations are represented as:
T_Bs j ＝{T_Se i }i＝1,2,3,…,n
in the formula: t _ Bs j The manufacturing task of segment layer number j is indicated.
(4) Bridge layer: represents the whole steel structure bridge and is the set of all bridge spans. The manufacturing task of the bridge layer is a collection of bridge span manufacturing tasks, which is the overall manufacturing task. The aggregations are represented as:
T_B＝{T_Bs j }j＝1,2,3,…,m
in the formula: t _ B represents a bridge floor manufacturing task.
The processing and manufacturing of the steel structure bridge cannot be completed only through the above description, and the information model needs to be further completed through supplementing some other necessary information through the step 2.
Step two, Basic information T _ Basic of the manufacturing task: the basic information includes the name and number of the manufacturing task and the issuer of the task, as shown in the following formula.
T_Basic＝{T_name,T_id,T_customer}
In the formula: t _ name is the name of the manufacturing task; t _ id is the number of the manufacturing task; t _ customer is the publisher of the manufacturing task.
For a certain manufacturing task, it is also limited and the corresponding requirements are set. The manufacturing task of the steel structure bridge generally comprises the requirements of delivery date, cost, qualification and execution standard. The Requirement T _ Requirements for the manufacturing task is described as follows:
T_Requirement＝{duedate,cost,qualification,standard}
in the formula: duedete is the delivery date; cost is a cost requirement; qualifications are qualification requirements; standard is the execution standard.
The manufacturing task of the steel structure bridge also comprises the requirements of special equipment and tools. The special requirement is represented by T _ special and is described by the following formula.
T_special＝{s_equipment,s_tools}
In the formula: s _ equipment represents a special device; s _ tools represents a special tooling.
Comprehensively carrying out aggregation on all the elements, and describing a manufacturing task MT of the steel structure bridge as follows:
MT＝{T_Basic,T_B,T_Requirement,T_Special}
referring to fig. 3, step three: establishing a manufacturing enterprise information model, wherein the mathematical description of the manufacturing enterprise information model is as follows:
ME＝{ME_Basic,ME_Capacity,ME_Status,ME_Auxiliary}，
which comprises the following steps:
basic information ME _ Basic of the manufacturing company { E _ name, E _ location, E _ contact },
the manufacturing capability ME _ Capacity of the manufacturing enterprise { C-M, C-S, C-A, C-G, C-T, C-R },
the Status information ME _ Status of the enterprise { E-Status, E-qualification },
the Auxiliary information ME _ axiliary of the enterprise { OTD, Qualified-rate, M-experience }.
E _ name is the name of a manufacturing enterprise, E _ location is the residence of the enterprise, and E _ contact is the contact way of the enterprise;
C-M, C-S, C-A, C-G, C-T and C-R are enterprise manufacturing capability evaluations and respectively provide materials, machinable shapes, machinable precision and machinable sizes, a machining process and machining resources;
e-status is the operation state of the manufacturing enterprise, including persistent, working, canceling, immigration, stopping and clearing; e-qualification is the qualification of manufacturing enterprises;
and the OTD is the standard time delivery rate, the Qualified-rate is the qualification rate of the manufacturing enterprises, and the M-experience is the manufacturing experience of the enterprises.
Step four: establishing a steel structure bridge manufacturing matching model according to the manufacturing task information model and the manufacturing enterprise information model, editing the body by adopting a body editing tool, carrying out body reasoning by a Jena reasoning machine, constructing an accurate and complete body model, converting the conceptual model into a model understandable by a computer, and obtaining an alternative enterprise scheme B 1 ～B 4 。
The second part, step five: and establishing a corresponding alternative enterprise scheme model, an attribute model corresponding to a specific scheme and weight models of all attributes in the scheme according to the alternative enterprise scheme, and constructing an initial multi-attribute decision matrix.
The above alternative enterprise solution model is described as P ═ { P in an aggregated manner 1 ,P 2 ,…,P m M is the number of fabrication protocols; the aggregated description of the attribute model is U ═ U 1 ,u 2 ,…,u n N is the number of attributes of a certain scheme; the clustering of the weight model is described as W ═{w 1 ,w 2 ,…,w n Redefined as: w is a 1 +w 2 +…+w n ＝1；
The initial multi-attribute decision matrix constructed according to the alternative enterprise scheme model, the attribute model corresponding to a specific scheme and the weight models of all the attributes in the scheme is as follows:
wherein x is ij An initial decision evaluation index value, e.g., an initial decision evaluation index value of a cost attribute, for a jth attribute of an ith solution in the initial multi-attribute decision matrix is a cost spent on executing the solution.
Step six: the method specifically comprises the following steps:
a) note the book
For the triangular fuzzy number, the expected value calculation formula for defining the triangular fuzzy number is as follows:
b) establishing an automatic inference machine according to the evaluation purpose and the related data of the evaluation index, evaluating the importance of the attribute pairwise by adopting a scale of 0.1-0.9 (the scale of 0.1-0.9 is set to be shown in a table 1.1), expanding the evaluation into a triangular fuzzy number, then establishing a triangular fuzzy number complementary judgment matrix, carrying out five-time evaluation in an expert system, and carrying out evaluation on B 1 ～B 4 The specific data evaluated are shown in table 1.2.
Table 1.10.1-0.9 scale setting table
TABLE 1.2 triangular fuzzy number complementation judgement matrix
The weights of the five evaluations by the expert system are: 0.3, 0.1, 0.2, 0.3, 0.1. And (3) obtaining a comprehensive triangular fuzzy number complementation judgment matrix by the comprehensive weight, wherein the specific numerical value is shown in table 2.
TABLE 2 comprehensive triangular fuzzy number complementation judgement matrix
From the above table, the triangular blur number weight of each index is calculated according to equation 2, as shown in table 3.
TABLE 3 triangular fuzzy number weights
According to the formula 3 and the formula 4, the weights of the indexes can be obtained by combining the above table as follows:
and according to the steps, sequentially evaluating and calculating the weight of the secondary indexes below the indexes. C 1 And C 2 The weight calculation process is as follows:
TABLE 4 triangular fuzzy number complementation judgement matrix
TABLE 5 triangular fuzzy number weights
C 1 And C 2 The weight of (2) is as follows:
C 3 to C 7 The calculation process of the weight value is as follows:
TABLE 6 triangular fuzzy number complementation judgement matrix
TABLE 7 comprehensive triangular fuzzy number complementation judgement matrix
According to the above table, C is calculated 3 To C 7 The triangular blur number weights of (1) are shown in table 8 below.
TABLE 8 triangular fuzzy number weights
C is calculated according to the table 3 To C 7 The weight of (2) is as follows:
C 8 and C 9 The calculation process of the weight value is as follows:
TABLE 9 triangular fuzzy number complementation judgement matrix
TABLE 10 comprehensive triangular fuzzy number complementation judgment matrix
TABLE 11 triangular fuzzy number weights
To obtain
And
the values of (A) are as follows:
C 10 and C 11 The calculation process of the weight value is as follows:
TABLE 12 triangular fuzzy number complementation judgement matrix
TABLE 13 comprehensive triangular fuzzy number complementation judgement matrix
TABLE 14 triangular fuzzy number weights
To obtain C 10 And C 11 The weights of (A) are as follows:
the statistics of the calculation results of the above weights are shown in table 15.
Table 15 weight statistics
And the established steel structure bridge manufacturing task information model is matched with the steel structure manufacturing enterprise information model to obtain four sets of alternative enterprise schemes. And carrying out normalized processing on the initial multi-attribute decision matrix by adopting vector transformation to obtain a normalized multi-attribute decision matrix, and solving and sequencing the normalized multi-attribute decision matrix to obtain an optimal scheme.
The raw data and the data after normalization processing are shown in table 16. Wherein the normalized data is multiplied by 10 -6 。
TABLE 16 raw data and normalization processing
After multiplying by the weights in table 15, a weighting specification matrix is obtained. Specific numerical values thereof are shown in table 16.
TABLE 17 weighted norm matrix values
Note that C1, C2, C8 belong to negative attributes, so the minimum is taken in the ideal solution set and the maximum is taken in the negative ideal solution set. From the above table, a set of ideal solution schemes
Comprises the following steps:
set of negative ideal solution scenarios
Comprises the following steps:
weighted Euclidean distance of four schemes from ideal solution and negative ideal solution
Respectively as follows:
calculating a composite proximity index f i Comprises the following steps: f. of i ＝[0.9178,0.745,0.797,0.203]
The scheme is obtained by sequencing the comprehensive proximity indexes from small to large: x 4 <X 2 <X 3 <X 1 . Therefore, the scheme IThe best solution is preferred, and solution three can be an alternative.
Claims (8)
1. A steel structure bridge manufacturing decision evaluation method comprises the following steps:
the method comprises the following steps: firstly, establishing a corresponding manufacturing task model by hierarchically dividing a steel structure bridge, supplementing basic information, task requirements and special requirements of the manufacturing task, and respectively establishing a basic information model of the manufacturing task, a requirement model of the manufacturing task and a special requirement model of the manufacturing task;
step two: establishing a manufacturing task information model based on the manufacturing task model, the basic information model of the manufacturing task, the requirement model of the manufacturing task and the special requirement model of the manufacturing task;
step three: establishing an alternative manufacturing scheme model, an attribute model of the alternative manufacturing scheme and a weight model of all attributes in the alternative manufacturing scheme based on the manufacturing task information model, and constructing an initial multi-attribute decision matrix;
step four: and determining triangular fuzzy number complementary judgment matrixes of all attributes of all alternative manufacturing schemes by using a triangular fuzzy number-TOPSIS method, carrying out standardized processing on the initial multi-attribute decision matrix by using vector transformation to obtain a standardized multi-attribute decision matrix, and solving and sequencing the standardized multi-attribute decision matrix to obtain an optimal scheme.
2. The steel structure bridge manufacturing decision evaluation method of claim 1, wherein in step three, the set of candidate enterprise solution models P ═ { P ═ P 1 ,P 2 ,…,P m }，P i I is more than or equal to 1 and less than or equal to m which is the number of alternative manufacturing schemes;
set of attribute models U ═ U 1 ,u 2 ,…,u n }，u j J is more than or equal to 1 and less than or equal to m, and n is the number of attributes of a certain scheme;
set of weight models W ═ W 1 ,w 2 ,…,w n }，w j Is different in a certain schemeThe weight of the attribute, j is more than or equal to 1 and less than or equal to m, and the re-definition of the ownership is as follows: w is a 1 +w 2 +…+w n ＝1；
An initial multi-attribute decision matrix X constructed according to the alternative manufacturing scheme model, the attribute model corresponding to a specific scheme and the weight models of all attributes in the scheme is as follows:
wherein x is ij Evaluating an index value for an initial decision of a jth attribute of an ith scheme in the initial multi-attribute decision matrix.
3. The steel structure bridge manufacturing decision evaluation method of claim 2, wherein the fourth step comprises the following steps:
a) note the book
Defining expected values of triangular blur numbers for triangular blur numbers
The calculation formula is as follows:
b) evaluating the attributes pairwise according to the evaluation purpose and the evaluation index, establishing a triangular fuzzy number complementation judgment matrix, and evaluating the triangular fuzzy number complementation judgment matrix at the kth time
Wherein
In the formula
Respectively representing the most conservative evaluation value, the most possible evaluation value and the most optimistic evaluation value of the ith evaluation index relative to the jth evaluation index, which are obtained by the k-th evaluation of the expert system;
c) using the weight to aggregate the evaluation values to obtain the comprehensive triangular fuzzy number complementary judgment matrix elements related to the evaluation indexes:
in the formula: i, j is 1,2, …, n, k is the number of evaluations; omega k Authority of the expert system for the kth evaluation;
d) triangular blur number weight for ith index
The calculation was performed using the following formula:
in the formula (I), the compound is shown in the specification,
a most conservative evaluation value representing the ith evaluation index relative to the jth evaluation index;
a most probable evaluation value indicating an ith evaluation index relative to a jth evaluation index;
a second evaluation value indicating the most optimistic evaluation value of the ith evaluation index relative to the jth evaluation index;
e) here, the decision maker assumes a neutral attitude, so taking λ equal to 0.5, the expectation calculation formula becomes:
f) calculating the weight omega of the ith attribute of each scheme in the alternative enterprise scheme set P i ，
g) Carrying out weight statistics on the multi-attribute of each scheme in P, establishing triangular fuzzy number complementary judgment matrixes of all the attributes of all the schemes, carrying out normalized processing on the initial multi-attribute decision matrix by using the triangular fuzzy number complementary judgment matrixes by adopting a vector transformation method to obtain a normalized multi-attribute decision matrix Y,
wherein, y ij Representing the weight of the jth attribute of the ith scheme;
h) determining a set C of ideal solution solutions based on normalized multi-attribute decision matrix values j * And set C of negative ideal solution schemes j 0 Calculating weighted Euclidean distances d of each scheme from the ideal solution and the negative ideal solution j * And d j 0 And a combined proximity index f j ；
i) And sequencing all the schemes from small to large according to the comprehensive nearness index, wherein the scheme with the maximum comprehensive nearness index is the optimal scheme.
4. The steel structure bridge manufacturing decision evaluation method of claim 3, wherein the selection of λ in step a) depends on the risk attitude of the decision maker:
when the decision maker is biased to optimistic attitude, lambda is more than 0.5 and less than 1;
when the decision maker is biased to be pessimistic, 0 < lambda < 0.5.
5. The steel structure bridge manufacturing decision evaluation method of claim 3, wherein the step f) further comprises consistency verification of the weight values, and the judgment index CI calculation formula of the consistency verification is as follows:
in the formula: lambda max Is composed of
Maximum characteristic root value of;
is composed of
In the expectation that the position of the target is not changed,
the consistency judgment coefficient CR is calculated by the following formula:
in the formula: RI is an average random consistency index; when CR is reached<And when the sum of the values of the triangular fuzzy number judgment matrixes is 0.1, the triangular fuzzy number judgment matrix is considered to pass the consistency test, and if the sum of the values of the triangular fuzzy number judgment matrix is not passed, the triangular fuzzy number judgment matrix is reestablished.
6. The steel structure bridge manufacturing decision evaluation method according to claim 1, wherein in the first step, the steel structure bridge is divided into the following components in sequence from low to high: the characteristic layer, the segment layer, the bridge span layer and the bridge layer.
7. The steel structure bridge manufacturing decision evaluation method of claim 1, wherein in step three, a steel structure bridge manufacturing matching model is established based on a manufacturing task information model and a manufacturing enterprise information model, and an alternative enterprise solution is obtained through ontology reasoning; and then establishing an alternative manufacturing scheme model, an attribute model of the alternative manufacturing scheme and a weight model of all attributes in the alternative manufacturing scheme according to the alternative enterprise scheme.
8. The steel structure bridge manufacturing decision evaluation method of claim 7, wherein in the third step, the mathematical description of the manufacturing enterprise information model is as follows: ME { [ ME _ Basic, ME _ Capacity, ME _ Status, ME _ Auxiliary }, where ME _ Basic represents Basic information of a manufacturing enterprise, ME _ Capacity represents manufacturing capability of the manufacturing enterprise, ME _ Status represents Status information of the enterprise, and ME _ Auxiliary represents Auxiliary information of the enterprise.
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