Multi-project multi-target optimization decision-making method

Abstract

The invention provides a multi-project multi-target optimization decision-making method, which comprises the following steps of: obtaining an evaluation dimension which needs to be concerned for performing optimization decision-making on a project, forming a project index system, and obtaining a weight set of the project index system; for each item, performing fuzzy level evaluation on each item according to a fuzzy evaluation level corresponding to the evaluation dimension, and determining the membership degree of the evaluation index of the last level of each item on different fuzzy evaluation levels so as to form a fuzzy evaluation matrix set of each item; based on the weight set of the project index system and the fuzzy evaluation matrix set of each project, performing fuzzy comprehensive evaluation operation on each project, and determining a comprehensive evaluation vector of each project; and according to the score set corresponding to the fuzzy evaluation grade and the comprehensive evaluation vector of each item, calculating a final score of each item. According to the method, multiple mathematical models are combined to carry out multi-project multi-target optimal decision making, and the model expansibility is high.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of project selection, and in particular to a multi-project multi-target optimization decision method. BACKGROUND

[0002] Project selection generally refers to the process of selecting and decision-making according to certain standards and procedures by relevant organizations or institutions in order to screen out construction project plans that meet certain requirements, have implementation conditions and potential value. Through the selection process, the most potential and beneficial construction projects can be screened out, so as to ensure that limited resources (such as funds, land, manpower, etc.) can be most reasonably allocated and utilized. This helps to avoid waste of resources and improve resource utilization efficiency.

[0003] Traditional single project optimization models, such as analytic hierarchy process and TOPSIS model, have the following problems and shortcomings: it is difficult to perform project optimization according to multi-target requirements under the total set of all projects to be selected; it is difficult to achieve the planning ability requirements of comprehensive consideration of project capacity and cost in current project selection. SUMMARY

[0004] The present application provides a multi-project multi-target optimization decision method to overcome the above problems.

[0005] The present application provides a multi-project multi-target optimization decision method, which comprises:

[0006] Obtaining evaluation dimensions needed for optimization decision of projects, forming a project index system, and obtaining a weight set of the project index system; the project index system is composed of evaluation indexes of different levels, and the evaluation indexes of the next level have a cascading relationship with one evaluation index of the previous level to form a hierarchical structure model of the project index system;

[0007] For each project, fuzzy level evaluation is performed on each project according to fuzzy evaluation grades corresponding to the evaluation dimensions, the membership degrees of the evaluation indexes of the last level of each project at different fuzzy evaluation grades are determined to form a fuzzy evaluation matrix set of each project;

[0008] Based on the weight set of the project index system and the fuzzy evaluation matrix set of each project, fuzzy comprehensive evaluation operation is performed on each project respectively to determine a comprehensive evaluation vector of each project;

[0009] The final score of each project is calculated according to the score set corresponding to the fuzzy evaluation grades and the comprehensive evaluation vector of each project respectively to determine the optimization decision grade of each project.

[0010] Furthermore, the weight set of the project index system and the fuzzy evaluation matrix set of each project are used to perform fuzzy comprehensive evaluation operations on each project to determine the comprehensive evaluation vector of each project, including:

[0011] The total membership of different fuzzy evaluation levels of the target project in each evaluation dimension is calculated based on a preset first evaluation vector calculation model, wherein the first evaluation vector calculation model is expressed as:

[0012]

[0013] Where r xi is the total membership degree of the target project on the i-th fuzzy evaluation level of the x-th evaluation dimension, M is the membership degree of the i-th fuzzy evaluation level of the m-th evaluation indicator in the last level of the x-th evaluation dimension of the target project, x is the total number of evaluation indicators at the last level of the x-th evaluation dimension of the target project, is the weight of the mth evaluation indicator in the last level of the xth evaluation dimension, N is the number of levels of evaluation indicators in the project indicator system, is the weight of the evaluation indicator of the jth level in the xth evaluation dimension that has a cascade relationship with the mth evaluation indicator in the last level;

[0014] According to the total membership of different fuzzy evaluation levels of the target project in each evaluation dimension, the evaluation dimension vector of the target project in each evaluation dimension is determined to form a comprehensive evaluation vector of the target project; wherein the comprehensive evaluation vector of the target project is expressed as:

[0015] R x =[r x1 ,r x2 ,...,r xK ],x=1,2,...,X

[0016] Where R x is the evaluation dimension vector of the target project on the xth evaluation dimension, K is the total number of fuzzy evaluation levels, and X is the total number of evaluation dimensions.

[0017] Furthermore, the final score of each project is calculated based on the score set corresponding to the fuzzy evaluation level and the comprehensive evaluation vector of each project to determine the preferred decision level of each project, including:

[0018] Perform multiplication operations on the evaluation dimension vectors of the target project in each evaluation dimension and the score set respectively to obtain the evaluation dimension scores of the target project in different evaluation dimensions;

[0019] The final score of the target project is obtained by weighting and calculating the evaluation dimension scores in different evaluation dimensions according to the weights of the target project in different target evaluation dimensions;

[0020] The final scores are sorted from high to low to obtain the preferred decision levels of the projects.

[0021] Further, the fuzzy comprehensive evaluation operation is performed on each project based on the weight set of the project index system and the fuzzy evaluation matrix set of each project, and a comprehensive evaluation vector of each project is determined, including:

[0022] The comprehensive membership degrees of the target project in different fuzzy evaluation levels are calculated based on a preset second evaluation vector calculation model, and the second evaluation vector calculation model is represented as:

[0023]

[0024] In the formula, r i is the comprehensive membership degree of the target project in the i-th fuzzy evaluation level, r mi is the membership degree of the m-th evaluation index in the last level in the i-th fuzzy evaluation level, M is the total number of evaluation indexes in the last level, ω Nm is the weight of the m-th evaluation index in the last level, N is the number of levels of evaluation indexes in the project index system, is the weight of the j-th level evaluation index having a cascading relationship with the m-th evaluation index in the last level;

[0025] The comprehensive evaluation vector of the target project is determined according to the comprehensive membership degrees of the target project in different fuzzy evaluation levels, and the comprehensive evaluation vector of the target project is represented as:

[0026] R = [r1, r2,..., r K ]

[0027] In the formula, K is the total number of fuzzy evaluation levels.

[0028] Further, the final scores of each project are calculated according to the score set corresponding to the fuzzy evaluation level and the comprehensive evaluation vector of each project, respectively, to determine the preferred decision level of each project, including:

[0029] The matrix multiplication operation is performed on the comprehensive evaluation vector of each project and the score set, respectively, to obtain the final score of each project;

[0030] The final scores are sorted from high to low to obtain the preferred decision levels of the projects.

[0031] Furthermore, before obtaining the weight set of the project indicator system, the method further includes:

[0032] Obtain the evaluation factors included in each project, determine the category of the evaluation indicators of the target level to which each evaluation factor belongs, and obtain the number of evaluation factors occupied by each evaluation indicator of the target level;

[0033] According to the number of evaluation factors occupied by each evaluation indicator and the preset weight of each evaluation factor, the relative importance of each evaluation factor in the target level is determined, and the importance judgment matrix of the evaluation indicators of the target level is determined by the 1-9 scale method;

[0034] Calculate the maximum eigenvalue and eigenvector of the importance judgment matrix of the evaluation indicators of the target level;

[0035] The eigenvectors of the importance judgment matrix of the evaluation indicators of the target level are normalized to obtain the weights of each evaluation indicator of the target level.

[0036] Furthermore, before performing fuzzy grade evaluation on each project according to the fuzzy evaluation grade corresponding to the evaluation dimension, the method further includes:

[0037] For each evaluation dimension, the same number of evaluation grades of different levels are selected as the fuzzy evaluation grades of each evaluation dimension;

[0038] The fuzzy evaluation grades at the same level in each evaluation dimension are assigned the same evaluation score, and a score set corresponding to the fuzzy evaluation grade of each evaluation dimension is obtained.

[0039] Furthermore, the determination of the membership of the last level evaluation index of each project on different fuzzy evaluation levels includes:

[0040] The last level evaluation indicator of the target project is selected as the target evaluation indicator, and the number of votes for each target evaluation indicator of the target project at different evaluation levels is obtained; wherein the number of votes for each target evaluation indicator of the target project at different evaluation levels is voted by multiple experts based on the degree of match between the specific content of the evaluation elements included in the target evaluation indicator of the target project and the evaluation criteria of different evaluation levels;

[0041] Normalize the number of votes for the target evaluation index at different evaluation levels to obtain the membership degree of the target evaluation index at different evaluation levels;

[0042] Repeat the above operation until the membership of the evaluation indicators of the last level of the target project at different evaluation levels is obtained, and the fuzzy evaluation matrix set of the target project is obtained.

[0043] The present invention provides a multi-project multi-objective optimal decision-making method, which obtains evaluation dimensions required for optimal decision-making on projects, forms a project indicator system, and obtains a weight set of the project indicator system; the project indicator system is composed of evaluation indicators of different levels, and the evaluation indicators of the latter level have a cascade relationship with an evaluation indicator of the previous level, so as to form a hierarchical structure model of the project indicator system; then, for each project, fuzzy grade evaluation is performed on each project according to the fuzzy evaluation level corresponding to the evaluation dimension, and the membership of the evaluation indicators of the last level of each project at different fuzzy evaluation levels is determined to form a fuzzy evaluation matrix set for each project; and based on the weight set of the project indicator system and the fuzzy evaluation matrix set of each project, fuzzy comprehensive evaluation operations are performed on each project respectively to determine the comprehensive evaluation vector of each project; finally, the final score of each project is calculated respectively according to the score set corresponding to the fuzzy evaluation level and the comprehensive evaluation vector of each project to determine the optimal decision-making level of each project. The present invention comprehensively utilizes model algorithms such as decision constraint evaluation method, cost-effectiveness method, hierarchical analysis method, etc. to realize the optimization method flow of construction projects, and provides a comprehensive optimization decision-making method for multiple projects and multiple objectives. The mathematical models used in each step are not strongly correlated, so the model is scalable.

[0044] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those skilled in the art by reading the detailed description of the preferred embodiment below. The accompanying drawings are only for the purpose of illustrating the preferred embodiment and are not to be considered as limiting the present invention. In the accompanying drawings:

[0046] Figure 1 This is a flow chart of a multi-project multi-objective optimization decision-making method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0048] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art in the art to which the present invention pertains. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with those in the context of the prior art and, unless specifically defined, will not be interpreted in an idealized or overly formal sense.

[0049] The embodiment of the present invention provides a multi-project multi-objective optimization decision-making method, such as Figure 1 As shown, the multi-project multi-objective optimization decision-making method includes the following steps:

[0050] S1. Obtaining evaluation dimensions required for making optimal decisions on projects, forming a project indicator system, and obtaining a weight set for the project indicator system; the project indicator system is composed of evaluation indicators at different levels, and the evaluation indicators at the latter level have a cascade relationship with an evaluation indicator at the previous level, thereby forming a hierarchical structure model of the project indicator system;

[0051] In an embodiment of the present invention, the evaluation dimensions required for selecting the optimal project can be randomly selected by the system, or can be selected based on decision-making preferences. The specific evaluation dimensions can be determined based on the project evaluation requirements. The weight set of the project indicator system can be a system-preset weight based on the evaluation dimensions, or can be calculated based on the key elements to be evaluated included in the project to be evaluated. Therefore, the present invention supports decision-making preference balance and weight analysis for project optimization.

[0052] S2. For each project, perform fuzzy evaluation on each project according to the fuzzy evaluation level corresponding to the evaluation dimension, determine the membership degree of the evaluation index of the last level of each project on different fuzzy evaluation levels, and form a fuzzy evaluation matrix set for each project;

[0053] In an embodiment of the present invention, a degree of membership is assigned to each level of each evaluation indicator of each project by voting, and a fuzzy evaluation matrix of each project is determined. Specifically, the present invention divides the evaluation factors contained in each evaluation indicator into evaluation criteria of different evaluation levels that can be quantified. In an optional embodiment, experts can compare and vote on the specific content of the evaluation factors contained in each project and the evaluation criteria of different evaluation levels to determine the specific evaluation level of the target project. Alternatively, intelligent matching or expert system voting methods can be used to vote multiple times on each evaluation indicator of each project, thereby determining the degree of membership of each evaluation level of each evaluation indicator of each project, and then determining the fuzzy evaluation matrix of each project.

[0054] S4. Calculate the final score of each project based on the score set corresponding to the fuzzy evaluation level and the comprehensive evaluation vector of each project to determine the preferred decision level of each project.

[0055] The multi-project multi-objective optimization decision-making method provided in the embodiment of the present invention comprehensively utilizes model algorithms such as decision constraint evaluation method, cost-effectiveness method, hierarchical analysis method, etc. to realize the method flow of optimization of construction projects, and provides a comprehensive optimization decision-making method for multiple projects and multiple objectives. The mathematical models used in each step are not strongly correlated, so the model is scalable.

[0056] The multi-project multi-objective optimal decision-making method is further introduced in detail below in conjunction with a specific embodiment of the present invention.

[0057] In a specific embodiment of the present invention, before executing step S1, it is necessary to select the projects to be optimized. In this specific embodiment, the projects to be evaluated in this multi-project decision-making process are selected from the project library, namely emergency command, collaborative skills and early warning technology.

[0058] Furthermore, in addition to the method for determining the evaluation dimensions in the above-mentioned embodiment, in an optional embodiment of the present invention, obtaining the evaluation dimensions that need to be paid attention to in making a preferential decision on a project may also include: scoring different initial evaluation dimensions in the preset four categories of thirteen-degree indicator systems, and calculating the total scores of different initial evaluation dimensions; sorting each initial evaluation dimension in order from high to low according to the total score, and selecting the initial evaluation dimension ranked before the preset first numerical value as the evaluation dimension that needs to be paid attention to in making a preferential decision on the project; using the first-level indicator in each evaluation dimension as the first-level evaluation indicator in the project indicator system; and selecting at least one level of secondary indicators from the evaluation indicators of the subsequent levels cascaded with the first-level indicators in the evaluation dimension as the evaluation indicator of the corresponding level in the project indicator system.

[0059] Specifically, the four-category thirteen-degree index system provided by a specific embodiment of the present invention is shown in Table 1.

[0060] Table 1: Four categories of thirteen-degree indicator system

[0061]

[0062]

[0063] In the embodiment of the present invention, specific evaluation standards are set for the 13 evaluation dimensions of the four categories of thirteen-degree index system in the above table, that is, corresponding evaluation factors are set for each evaluation dimension, and evaluation levels that can be quantified and calculated are set for each evaluation factor, so that different evaluation indicators of the project can be objectively and quantitatively evaluated.

[0064] Specifically, the first type of necessity evaluation indicators includes demand satisfaction, technological advancement, technical and tactical indicator compliance, and system integration.

[0065] 1) Demand satisfaction refers to the degree to which the proposed project meets the requirements of the project guidelines. Its evaluation factors may include:

[0066] Application topic: the degree of match between the project subject and the topics covered in the guidelines;

[0067] Objectives: This refers to the degree to which the project's intended goals match the problems that the guidelines are intended to solve.

[0068] The evaluation levels of demand satisfaction can be specifically divided into: very satisfied, relatively satisfied, satisfied, dissatisfied and completely dissatisfied; among them, very satisfied specifically means that the expected results of the application project are rich and organized, highly matched with the theme and objectives of the project guidelines, and provide detailed solutions to various needs. At the same time, it can propose and meet additional valuable new needs, and even play a certain demonstration role in construction. Relatively satisfied specifically means that the expected results of the application project are relatively rich, highly matched with the theme and objectives of the project guidelines, can meet the expected needs of the project application guidelines and provide solutions, and have a relatively comprehensive response to various technical indicators. Completely dissatisfied specifically means that the expected results of the application project cannot identify the basic needs of the project guidelines, are seriously disconnected from actual needs, and lack effective solutions to various technical indicators.

[0069] 2) Technological advancement refers to the cutting-edge and innovative nature of the technology involved in the project, reflecting the latest development level, innovation capability, and driving force behind the development of the field. Evaluation factors may include:

[0070] Key technologies, the comparison between the key technologies used and the cutting-edge nature of existing technologies, can be specifically divided into excellent, leading, advanced, general, and backward evaluation levels. The quantitative standard for excellence is: the latest results published in the latest journals; the quantitative standard for leading is being in the leading position in the industry, with a large number of experimental products emerging, etc. The quantitative standard for advanced is that the technology adopted or developed by the project is in line with the development trend of the industry / field, has a certain degree of innovation, and the technical level can have a certain impact on the development of the field. The quantitative standard for general is that the technology adopted or developed by the project is relatively conservative, the innovation points are not clear enough, the technical level has little impact on the industry, and there is a lack of significant breakthroughs. The quantitative standard for backward is that the technology adopted or developed by the project is outdated, lacks technological innovation applications, lacks innovative points, and the technical level cannot bring about industry impact.

[0071] The degree of conformity of technical and tactical indicators refers to the degree to which the project complies with the technical and tactical indicators designed in the application guidelines. Its evaluation factors may include:

[0072] Budget, capacity, and effectiveness can be evaluated at different levels: very consistent, relatively consistent, consistent, inconsistent, and completely inconsistent. This refers to the degree to which the project's budget matches the budget outlined in the guidelines. The proposed project includes clear and well-organized technical indicators that are highly consistent with the technical indicators required by the project guidelines. A highly consistent quantitative standard is one in which the proposed project includes clear and well-organized technical indicators that are highly consistent with the technical indicators required by the project guidelines. A relatively consistent quantitative standard is one in which the proposed project includes a large number of various technical indicators, including most of the technical indicators required by the project guidelines. A consistent quantitative standard is one in which the technical indicators included in the proposed project are basically consistent with the project guidelines. A non-compliant quantitative standard is one in which the technical indicators included in the proposed project meet some of the requirements, but there are many omissions or deviations. A completely non-compliant quantitative standard is one in which the proposed project includes fewer technical indicators and has a low degree of consistency with the project requirements.

[0073] 4) System integration refers to the level of compatibility and integration between the project's expected outcomes and the current equipment development. Evaluation factors may include the degree of compatibility between the project's objectives and the current system.

[0074] The second category is feasibility evaluation indicators, including technical maturity, team support, and organizational orderliness.

[0075] 5) Technology maturity is a set of systematic standards, methods and tools for determining key technologies in equipment development and quantitatively evaluating their maturity. Its evaluation factors may include:

[0076] The R&D stage of the key technology being applied, such as report level (no product, newly discovered new method), solution level (specific solution already exists, no product), simulation level (simulation results available)...system level (large-scale products already exist).

[0077] 6) Team support refers to the comprehensiveness and rationality of the project team in terms of talent structure, professional skills, project experience, collaboration ability, domain knowledge, and innovation ability, reflecting the team's ability to support the execution of the project. Evaluation factors may include:

[0078] The number and structure of team members (age, skills, professional titles, etc.), the number of personnel (number of professors, number of graduate students), etc., and the level of the project (national, provincial, ministerial, etc.).

[0079] 7) Organizational orderliness refers to the degree of orderliness and standardization of the project team's execution process (progress, finance, evaluation, acceptance, etc.) and organizational management (task allocation, execution plan, supervision feedback, etc.).

[0080] The third category is economic index evaluation, including cost effectiveness, element completeness, and construction content duplication.

[0081] 8) Cost-effectiveness refers to the relative size of the effectiveness generated by the project's expected outcomes compared to its cost (or budget). Evaluation levels include: High effectiveness, which refers to a reasonable assessment of the cost budget and expected effectiveness, high project economics, and detailed and reliable cost control and cost management plans. Very high capital utilization with no waste. High effectiveness, which refers to a reasonable assessment of the cost budget and expected effectiveness, reasonable project economics, and the inclusion of a cost control and cost management plan. High capital utilization with little waste. Medium effectiveness, which refers to a reasonable assessment of the cost budget and expected effectiveness, average project economics, and room for improvement in the cost control and cost management plan. Average capital utilization with some minor waste. Low effectiveness, which refers to an inaccurate assessment of the cost budget and expected effectiveness, average project economics, and loopholes in the cost control and cost management plans. Poor capital utilization with significant waste. Low effectiveness, which refers to an inaccurate assessment of the cost budget and expected effectiveness, poor project economics, and the lack of a cost control and cost management plan. The utilization rate of funds is low and a large amount of funds are wasted.

[0082] 9) Element completeness refers to the extent to which the proposed project meets the requirements of the eight elements in the project guidelines. The evaluation levels include very high completeness, relatively high completeness, medium completeness, relatively low completeness, and very low completeness.

[0083] 10) Construction Content Duplication refers to the degree of overlap between the project's proposed content (technology, solutions, etc.) and existing projects (or projects proposed at the same time). Scoring is based on three aspects: duplication of pre-research / construction / study content, oversaturation of construction requirements, and oversaturation of technical and tactical indicators. Evaluation levels include complete independence, relative independence, partial duplication, significant duplication, and high duplication.

[0084] The fourth category is risk assessment indicators, including risk safety, schedule rationality, and quality control.

[0085] 11) Risk safety refers to the degree of completeness of the project's solutions (including identification, assessment, response, and monitoring) for technical risks, schedule risks, and quality risks that may be faced during implementation. Based on GJB9001 "Quality Management System Requirements" and GJB / Z 171 "Guidelines for Risk Management of WQ Equipment Development Projects," the project is scored based on three aspects: comprehensive and accurate risk identification and prevention measures, effective and complete risk response plan development, and clear documentation of risk response processes and results. Evaluation levels include extremely high safety, relatively high safety, moderate safety, relatively low safety, and extremely low safety.

[0086] 12) Schedule rationality refers to whether the schedule arrangement of the application project can meet the general rules of project construction and meet the progress requirements such as milestones, mid-term evaluation, delivery and acceptance in the application guidelines.

[0087] 13) Quality Control refers to the rationality and effectiveness of the project's quality control over its expected outcomes. Based on GJB9001 (Quality Management System Requirements) and GJB 1406 (Product Quality Assurance Program Requirements), scores are awarded based on five key areas: quality plan development, quality risk identification, quality risk control, implementation of quality assurance clauses, and continuous improvement of quality issues.

[0088] The present invention divides project optimization into 13 evaluation dimensions, and selects different evaluation dimensions to evaluate the projects according to the actual content of the specific projects. It can make optimization decisions on multiple projects based on the needs and the decision-making is more flexible.

[0089] Based on the above embodiments, the projects that need to be evaluated in the present invention are emergency command, collaborative skills and early warning technology. Since the evaluation factors included in emergency command, collaborative skills and early warning technology all include a number of different technologies applied, that is, they are strongly related to the evaluation dimension of technological advancement, each key project is based on the technical optimization of the previous project, that is, it is strongly related to the evaluation dimension of construction content duplication, as well as the optimization of cost-effectiveness, the evaluation factors included in several projects include multiple indicators of economic efficiency, multiple indicators of equipment utilization, etc., that is, they are strongly related to the evaluation dimension of cost-effectiveness, and therefore the three evaluation dimensions of technological advancement, construction content duplication and cost-effectiveness are selected for project evaluation. It should be noted that for relatively simple projects, the required evaluation dimensions can be obtained through simple screening. For complex projects, it is also possible to use expert voting or set corresponding weights for different evaluation dimensions. According to the number of evaluation factors contained in each evaluation dimension and its weight, the specific evaluation dimension can be comprehensively calculated.

[0090] It should be noted that technological advancement, construction content duplication, and cost-effectiveness are evaluation dimensions, and are also the first-level evaluation indicators of the three evaluation dimensions, namely the first-level indicators. After determining the evaluation indicators of the first level, it is necessary to select at least one level of secondary indicators as the evaluation indicators of the corresponding level in the project indicator system.

[0091] For example, taking technological advancement as an example, the first-level evaluation indicator is technological advancement. The second-level evaluation indicators cascaded with technological advancement can be essential technologies, positive technologies, and negative technologies. The evaluation factors involved are the specific technologies required, and this document will not elaborate on them. If needed, a third-level evaluation indicator cascaded with the essential technologies can be added to form a hierarchical model diagram, facilitating comprehensive evaluation and analysis of each project.

[0092] Furthermore, in step S2, before obtaining the weight set of the project indicator system, the method of the embodiment of the present invention also includes: obtaining the evaluation factors contained in each project, judging the category of the evaluation index of the target level to which each evaluation factor belongs, so as to obtain the number of evaluation factors occupied by each evaluation indicator of the target level; determining the relative importance of each evaluation factor in the target level according to the number of evaluation factors occupied by each evaluation indicator and the preset weight of each evaluation factor, and determining the importance judgment matrix of the evaluation indicators of the target level through the 1-9 scale method; solving the maximum eigenvalue and eigenvector of the importance judgment matrix of the evaluation indicators of the target level; normalizing the eigenvector of the importance judgment matrix of the evaluation indicators of the target level to obtain the weight of each evaluation indicator of the target level.

[0093] Specifically, since the project indicator system for project optimization decision-making has been determined in step S1, and the project indicator system is a hierarchical model structure, for the evaluation indicators at a certain level, based on the understanding of the relative importance of each indicator, a 1-9 scale method is adopted, that is, the scale is divided into 9 levels, where 9, 7, 5, 3, and 1 correspond to absolutely important, very important, relatively important, slightly important, and equally important, respectively, and 8, 6, 4, and 2 are located between two adjacent levels. The judgment matrix of each evaluation indicator at the same level is determined.

[0094] In a specific embodiment of the present invention, taking the weights of the three evaluation indicators of "technical advancement", "team support" and "cost effectiveness" as an example,

[0095] If there are 6 technology-related evaluation factors, 2 team support-related evaluation factors, and 3 cost-effectiveness-related evaluation factors in each project to be evaluated, then the relative importance of the three dimensions can be compared to construct a judgment matrix as shown in Table 2:

[0096] Table 2 Judgment Matrix

[0097] Advanced technology Team support Cost-effectiveness Technological advancement 1 1 / 3 1 / 2 Construction content duplication 3 1 3 Cost-effectiveness 2 1 / 3 1

[0098] Furthermore, for the judgment matrix, the maximum eigenvalue and the eigenvector corresponding to it are calculated. The eigenvectors are then normalized to obtain the weights of each factor. For example, based on the judgment matrix in Table 2, the weights of the three evaluation indicators—"Technology Advancement," "Construction Content Duplication," and "Cost-Effectiveness"—can be expressed as w = (0.11, 0.67, 0.22).

[0099] Furthermore, the project indicator system has multiple levels of evaluation indicators. When solving the weights of the evaluation indicators of the next level, the importance of the evaluation indicators of the current level is compared with each other based on a certain evaluation indicator of the previous level to determine the relative importance of the evaluation indicators of the current level. The relative importance judgment matrix of the evaluation indicators of the current level is obtained, and the weights of the evaluation indicators of the same level are calculated. Among them, the weights of the evaluation indicators of different levels meet the following calculation formula:

[0100]

[0101] Where, is the weight of an evaluation indicator at level b on the xth evaluation dimension, is the xth evaluation dimension in the ath level and The weight of the mth evaluation indicator in the cascade, M ax is the xth evaluation dimension in level a The total number of evaluation metrics in the cascade.

[0102] Furthermore, in step S3, before performing a fuzzy rating evaluation on each project based on the fuzzy rating corresponding to the evaluation dimension, the method provided by the embodiment of the present invention further includes: for each evaluation dimension, selecting the same number of different-level ratings as the fuzzy rating of each evaluation dimension; assigning the same evaluation score to the fuzzy ratings at the same level in each evaluation dimension, thereby obtaining a score set corresponding to the fuzzy rating of each evaluation dimension. In the four-category thirteen-degree indicator system in the aforementioned embodiment, each evaluation dimension is assigned a corresponding evaluation grade, and different evaluation grades have different scores, which facilitates fuzzy comprehensive evaluation of each project.

[0103] In a specific embodiment of the present invention, fuzzy evaluation levels are determined for the three evaluation factors of technological advancement, construction content duplication, and cost-effectiveness. Although the specific level names of each evaluation indicator are different, they can be normalized. The five evaluation levels of each indicator can be represented by fuzzy sets.

[0104] Evaluation level of technological advancement: V1 = {excellent, leading, advanced, average, backward};

[0105] Evaluation level of construction content duplication: V2 = {completely independent, relatively independent, partially independent, significantly repeated, highly repeated};

[0106] Evaluation level of cost-effectiveness: V3 = {high efficiency, relatively high efficiency, medium efficiency, relatively low efficiency, low efficiency}.

[0107] Furthermore, the score set of the above evaluation levels is expressed as: S = {s1, s2, ...s5}.

[0108] Specifically, the scores corresponding to different evaluation levels are shown in Table 4.

[0109] Table 4 Evaluation grade scores

[0110]

[0111] Furthermore, in step S3, the determination of the membership of the evaluation indicators of the last level of each project at different fuzzy evaluation levels includes: selecting the evaluation indicators of the last level of the target project as the target evaluation indicators, and obtaining the number of votes for each target evaluation indicator of the target project at different evaluation levels; wherein the number of votes for each target evaluation indicator of the target project at different evaluation levels is the vote made by multiple experts based on the degree of matching between the specific content of the evaluation elements contained in the target evaluation indicators of the target project and the evaluation criteria of different evaluation levels; normalizing the number of votes for the target evaluation indicators at different evaluation levels to obtain the membership of the target evaluation indicators at different evaluation levels; repeating the above operations until the membership of the evaluation indicators of the last level of the target project at different evaluation levels is obtained, and the fuzzy evaluation matrix set of the target project is obtained.

[0112] In a specific embodiment of the present invention, a membership degree is assigned to each level of each indicator by an expert voting method. Taking the necessary requirements in the degree of technological advancement as an example (see Table 5), assuming that the total number of experts in the evaluation team is 10, 3 experts evaluate the results as "excellent", two experts evaluate it as "leading", two experts evaluate it as "advanced", two experts evaluate it as "average", and one expert evaluates it as "backward", then the membership matrix of "necessary technology" is (0.3, 0.2, 0.2, 0.2, 0.1). According to this method, the fuzzy evaluation matrix of other evaluation factors is obtained in the same way. It should be noted here that for the grade evaluation of necessary technology, the experts compare the actual technology (evaluation factor) applied in the necessary technology with the existing technology to determine which grade the actual technology (evaluation factor) specifically belongs to. Since the standard of quantitative grade is unified, each evaluation indicator can be evaluated objectively and consistently.

[0113] Table 5 Membership of necessary requirements

[0114]

[0115] Repeat the above operation until the membership of all evaluation indicators of all projects is obtained, and the fuzzy evaluation matrix set of each project is obtained (as shown in Table 6).

[0116] Table 6 Fuzzy evaluation matrix set of each project:

[0117]

[0118]

[0119] Furthermore, the present invention performs a fuzzy comprehensive evaluation operation on each project in the present invention in accordance with the cost-effectiveness method. Specifically, in one embodiment of the present invention, in step S4, the fuzzy comprehensive evaluation operation is performed on each project based on the weight set of the project index system and the fuzzy evaluation matrix set of each project to determine the comprehensive evaluation vector of each project, including:

[0120] The total membership of the target project at different fuzzy evaluation levels in each evaluation dimension is calculated based on a preset first evaluation vector calculation model, wherein the first evaluation vector calculation model is expressed as:

[0121]

[0122] Where r xi is the total membership degree of the target project on the i-th fuzzy evaluation level of the x-th evaluation dimension, is the membership degree of the i-th fuzzy evaluation level of the m-th evaluation indicator in the last level of the x-th evaluation dimension of the target project, M x is the total number of evaluation indicators at the last level of the x-th evaluation dimension of the target project, is the weight of the mth evaluation indicator in the last level of the xth evaluation dimension, N is the number of levels of evaluation indicators in the project indicator system, is the weight of the evaluation indicator of the jth level in the xth evaluation dimension that has a cascade relationship with the mth evaluation indicator in the last level;

[0123] According to the total membership of different fuzzy evaluation levels of the target project in each evaluation dimension, the evaluation dimension vector of the target project in each evaluation dimension is determined to form a comprehensive evaluation vector of the target project; wherein the comprehensive evaluation vector of the target project is expressed as:

[0124] R x =[r x1 ,r x2 ,...,r xK ],x=1,2,...,X (3)

[0125] Where R x is the evaluation dimension vector of the target project on the xth evaluation dimension, K is the total number of fuzzy evaluation levels, and X is the total number of evaluation dimensions.

[0126] Furthermore, the final score of each project is calculated respectively according to the score set corresponding to the fuzzy evaluation level and the comprehensive evaluation vector of each project to determine the preferred decision level of each project, including: performing multiplication operations on the evaluation dimension vector of the target project in each evaluation dimension and the score set respectively to obtain the evaluation dimension scores of the target project in different evaluation dimensions; performing weighted calculations on the evaluation dimension scores in different evaluation dimensions according to the weights of the target project in different target evaluation dimensions to obtain the final score of the target project; and sorting the final scores from high to low to obtain the preferred decision level of each project.

[0127] In a specific embodiment of the present invention, the evaluation dimension vector corresponding to each evaluation dimension of each item is calculated based on the fuzzy evaluation matrix set of each item in Table 6 and is expressed as:

[0128] Comprehensive evaluation vector of command and control:

[0129] B1=(0.17,0.19,0.21,0.13,0.07)

[0130] B2=(0.18,0.28,0.12,0.34,0.08)

[0131] B3=(0.38,0.24,0.26,0.06,0.06)

[0132] Comprehensive evaluation vector of collaborative skills:

[0133] B1=(0.18,0.18,0.24,0.06,0.04)

[0134] B2=(0.20,0.30,0.180,0.20,0.12)

[0135] B3=(0.34,0.24,0.26,0.10,0.06)

[0136] Comprehensive evaluation vector of early warning technology:

[0137] B1=(0.24,0.11,0.18,0.13,0.04)

[0138] B2=(0.18,0.30,0.16,0.22,0.12)

[0139] B3=(0.340,0.26,0.24,0.10,0.06)

[0140] Furthermore, taking command and control as an example, the evaluation dimension scores of the target project in different evaluation dimensions are calculated as follows:

[0141] Technological advancement = 0.17*100+0.19*90+0.21*80+0.13*70+0.07*60=64.2

[0142] Construction content duplication = 0.18*100+0.28*90+0.12*80+0.34*70+0.08*60=81.4

[0143] Cost-effectiveness = 0.38*100+0.24*90+0.26*80+0.06*70+0.06*60 = 88.2

[0144] Furthermore, the evaluation dimension scores on different evaluation dimensions are weighted and calculated according to the weights of the target items on different target evaluation dimensions:

[0145] 64.2*0.11+81.4*0.67+88.2*0.22=81.00

[0146] Furthermore, the final scores of all items are calculated and sorted from high to low to obtain the preferred decision level of each item, as shown in Table 7:

[0147] Table 7 Comprehensive evaluation calculation ranking

[0148]

[0149] Then, the priority decision level of each project can be obtained according to Table 7. This method can not only obtain the total score of each project, but also obtain the score of each project in different evaluation dimensions, which is convenient for further analysis and comparison of each project.

[0150] In addition, it should be noted that, in the case where it is not necessary to analyze the scores of each project in different evaluation dimensions, the present invention also provides another factual method, which is specifically, based on the weight set of the project indicator system and the fuzzy evaluation matrix set of each project, a fuzzy comprehensive evaluation operation is performed on each project to determine the comprehensive evaluation vector of each project, including:

[0151] The comprehensive membership of the target project at different fuzzy evaluation levels is calculated based on a preset second evaluation vector calculation model, wherein the second evaluation vector calculation model is expressed as:

[0152]

[0153] Where r i is the comprehensive membership of the target project on the i-th fuzzy evaluation level, r mi is the membership of the mth evaluation index in the last level to the ith fuzzy evaluation level, M is the total number of evaluation indexes in the last level, ω Nm is the weight of the mth evaluation indicator in the last level, N is the number of levels of evaluation indicators in the project indicator system, is the weight of the evaluation index of the jth level that has a cascade relationship with the mth evaluation index in the last level;

[0154] According to the comprehensive membership of the target project at different fuzzy evaluation levels, a comprehensive evaluation vector of the target project is determined; wherein the comprehensive evaluation vector of the target project is expressed as:

[0155] R=[r1,r2,...,r K ] (5)

[0156] Where K is the total number of fuzzy evaluation levels.

[0157] Furthermore, the final score of each project is calculated based on the score set corresponding to each evaluation level and the comprehensive evaluation vector to determine the preferred decision level of each project, including: performing matrix multiplication operations on the comprehensive evaluation vector of each project and the score set to obtain the final score of each project; and sorting the final scores from high to low to obtain the preferred decision level of each project.

[0158] For simplicity of description, the method embodiments are described as a series of actions. However, those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, as certain steps may be performed in other orders or simultaneously depending on the embodiments of the present invention. Furthermore, those skilled in the art should also be aware that the embodiments described in this specification are preferred embodiments, and the actions described are not necessarily required by the embodiments of the present invention.

[0159] An embodiment of the present invention provides a multi-project multi-objective optimization decision-making method, which first obtains the evaluation dimensions required for making optimization decisions on projects, forms a project indicator system, and obtains a weight set of the project indicator system; the project indicator system is composed of evaluation indicators of different levels, and the evaluation indicators of the latter level have a cascade relationship with an evaluation indicator of the previous level, so as to form a hierarchical model of the project indicator system; for each project, a fuzzy grade evaluation is performed on each project according to the fuzzy evaluation level corresponding to the evaluation dimension, and the membership of the evaluation indicators of the last level of each project at different fuzzy evaluation levels is determined to form a fuzzy evaluation matrix set for each project; based on the weight set of the project indicator system and the fuzzy evaluation matrix set of each project, a fuzzy comprehensive evaluation operation is performed on each project respectively to determine the comprehensive evaluation vector of each project; the final score of each project is calculated respectively according to the score set corresponding to the fuzzy evaluation level and the comprehensive evaluation vector of each project to determine the optimization decision level of each project. The present invention comprehensively utilizes model algorithms such as decision constraint evaluation method, cost-effectiveness method, hierarchical analysis method, etc. to realize the optimization method flow of construction projects, and provides a comprehensive optimization decision-making method for multiple projects and multiple objectives. The mathematical models used in each step are not strongly correlated, so the model is scalable.

[0160] Furthermore, those skilled in the art will appreciate that although some embodiments herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, any of the claimed embodiments may be used in any combination.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A multi-project multi-objective optimization decision-making method, characterized in that: The method comprises: Obtaining the evaluation dimensions required for making optimal decisions on projects, forming a project indicator system, and obtaining a weight set for the project indicator system; the project indicator system is composed of evaluation indicators at different levels, and the evaluation indicators at the latter level have a cascade relationship with an evaluation indicator at the previous level, so as to form a hierarchical structure model of the project indicator system; For each project, a fuzzy evaluation is performed on each project according to the fuzzy evaluation level corresponding to the evaluation dimension, and the membership degree of the evaluation indicators of the last level of each project on different fuzzy evaluation levels is determined to form a fuzzy evaluation matrix set for each project; Based on the weight set of the project indicator system and the fuzzy evaluation matrix set of each project, a fuzzy comprehensive evaluation operation is performed on each project to determine a comprehensive evaluation vector for each project; The final score of each project is calculated according to the score set corresponding to the fuzzy evaluation level and the comprehensive evaluation vector of each project to determine the preferred decision level of each project.

2. The method according to claim 1, characterized in that The weight set of the project index system and the fuzzy evaluation matrix set of each project are used to perform fuzzy comprehensive evaluation operations on each project to determine the comprehensive evaluation vector of each project, including: The total membership of the target project at different fuzzy evaluation levels in each evaluation dimension is calculated based on a preset first evaluation vector calculation model, wherein the first evaluation vector calculation model is expressed as: Where r xi is the total membership degree of the target project on the i-th fuzzy evaluation level of the x-th evaluation dimension, is the membership degree of the i-th fuzzy evaluation level of the m-th evaluation indicator in the last level of the x-th evaluation dimension of the target project, M x is the total number of evaluation indicators at the last level of the x-th evaluation dimension of the target project, is the weight of the mth evaluation indicator in the last level of the xth evaluation dimension, N is the number of levels of evaluation indicators in the project indicator system, is the weight of the evaluation indicator of the jth level in the xth evaluation dimension that has a cascade relationship with the mth evaluation indicator in the last level; According to the total membership of different fuzzy evaluation levels of the target project in each evaluation dimension, the evaluation dimension vector of the target project in each evaluation dimension is determined to form a comprehensive evaluation vector of the target project; wherein the comprehensive evaluation vector of the target project is expressed as: R x =[r x1 ,r x2 ,...,r xK ],x=1,2,...,X Where R x is the evaluation dimension vector of the target project on the xth evaluation dimension, K is the total number of fuzzy evaluation levels, and X is the total number of evaluation dimensions.

3. The method according to claim 2, characterized in that The final score of each project is calculated based on the score set corresponding to the fuzzy evaluation level and the comprehensive evaluation vector of each project to determine the preferred decision level of each project, including: Perform multiplication operations on the evaluation dimension vectors of the target project in each evaluation dimension and the score set respectively to obtain the evaluation dimension scores of the target project in different evaluation dimensions; According to the weight of the target project on different target evaluation dimensions, the evaluation dimension scores on different evaluation dimensions are weighted and calculated to obtain the final score of the target project; The final scores are sorted from high to low to obtain the preferred decision level for each project.

4. The method according to claim 1, wherein The weight set of the project index system and the fuzzy evaluation matrix set of each project are used to perform fuzzy comprehensive evaluation operations on each project to determine the comprehensive evaluation vector of each project, including: The comprehensive membership of the target project at different fuzzy evaluation levels is calculated based on a preset second evaluation vector calculation model, wherein the second evaluation vector calculation model is expressed as: Where r i is the comprehensive membership of the target project on the i-th fuzzy evaluation level, r mi is the membership of the mth evaluation index in the last level to the ith fuzzy evaluation level, M is the total number of evaluation indexes in the last level, ω Nm is the weight of the mth evaluation indicator in the last level, N is the number of levels of evaluation indicators in the project indicator system, is the weight of the evaluation index of the jth level that has a cascade relationship with the mth evaluation index in the last level; According to the comprehensive membership of the target project at different fuzzy evaluation levels, a comprehensive evaluation vector of the target project is determined; wherein the comprehensive evaluation vector of the target project is expressed as: R＝[r1,r2,...,r K ] Where K is the total number of fuzzy evaluation levels.

5. The method according to claim 4, characterized in that The final score of each project is calculated based on the score set corresponding to the fuzzy evaluation level and the comprehensive evaluation vector of each project to determine the preferred decision level of each project, including: Perform matrix multiplication on the comprehensive evaluation vector of each project and the score set to obtain the final score of each project; The final scores are sorted from high to low to obtain the preferred decision level for each project.

6. The method according to claim 1, characterized in that Before obtaining the weight set of the project indicator system, the method further includes: Obtain the evaluation factors included in each project, determine the category of the evaluation indicators of the target level to which each evaluation factor belongs, and obtain the number of evaluation factors occupied by each evaluation indicator of the target level; According to the number of evaluation factors occupied by each evaluation indicator and the preset weight of each evaluation factor, the relative importance of each evaluation factor in the target level is determined, and the importance judgment matrix of the evaluation indicators of the target level is determined by the 1-9 scale method; Calculate the maximum eigenvalue and eigenvector of the importance judgment matrix of the evaluation indicators of the target level; The eigenvectors of the importance judgment matrix of the evaluation indicators of the target level are normalized to obtain the weights of each evaluation indicator of the target level.

7. The method according to claim 6, characterized in that Before performing fuzzy grade evaluation on each project according to the fuzzy evaluation grade corresponding to the evaluation dimension, the method further includes: For each evaluation dimension, the same number of evaluation grades of different levels are selected as the fuzzy evaluation grades of each evaluation dimension; The fuzzy evaluation grades at the same level in each evaluation dimension are assigned the same evaluation score, and a score set corresponding to the fuzzy evaluation grade of each evaluation dimension is obtained.

8. The method according to claim 7, characterized in that The determination of the membership of the evaluation indicators of the last level of each project on different fuzzy evaluation levels includes: The last level evaluation indicator of the target project is selected as the target evaluation indicator, and the number of votes for each target evaluation indicator of the target project at different evaluation levels is obtained; wherein the number of votes for each target evaluation indicator of the target project at different evaluation levels is voted by multiple experts based on the degree of match between the specific content of the evaluation elements included in the target evaluation indicator of the target project and the evaluation criteria of different evaluation levels; Normalize the number of votes for the target evaluation index at different evaluation levels to obtain the membership degree of the target evaluation index at different evaluation levels; Repeat the above operation until the membership of the evaluation indicators of the last level of the target project at different evaluation levels is obtained, and the fuzzy evaluation matrix set of the target project is obtained.

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