Abstract
At present, there are very few enterprises with wholeprocess engineering consulting capability and qualification. To carry out consulting work in a consortium is a necessary way to meet the actual situation of the industry and promote wholeprocess consulting quickly, and the selection of consortium members is the key issue. First of all, this paper summarizes the construction principle of the index system of consortium member selection, and describes the index system. Aiming at the consortium member selection model, on the basis of comprehensive consideration of 9 indicators, the evaluation value of ZNumbers fuzzy numbers is obtained by using sevenpoint language terms, the index weight and expert weight are obtained by using the sequential method and entropy weight method, and the data are processed to get the alternative enterprise evaluation matrix. Fuzzy TOPSIS method suitable for ZNumbers was introduced to solve the positive ideal solution closeness degree of each enterprise, so as to rank the alternative enterprises.
Highlights
 From three aspects of team cooperation, technical ability, organization and management, the whole process of project consulting consortium member selection evaluation index system including 9 indexes is put forward.
 Znumbers combined with entropy weight method were used to obtain the weight of expert opinions, and ZNumbers fuzzy TOPSIS model was introduced to solve the ranking of alternative enterprises.
 The uncertainty and reliability of decision information were fully considered. Fuzzy TOPSIS was used to calculate the distance between fuzzy numbers, reducing the loss of information.
1. Introduction
The interest distribution of relevant stakeholders is an important part of the whole process of engineering consultation in the form of a consortium. The interest distribution of all parties in the consortium is crucial to the smooth implementation of the project. Reasonable interest distribution can improve the enthusiasm of all parties for cooperation. Dissatisfaction of any party may lead to the dissolution of the consortium or the reduction of service quality [1].
Xu et al. [2] established a threestage evaluation model for the selection of members in the design team, and conducted preliminary analysis, fuzzy comprehensive evaluation, and dynamic adjustment of candidate members. Through case studies, it has been verified that this method has strong flexibility and is suitable for various evaluation situations. Fan et al. [3] established a dual objective 01 model based on comprehensive consideration of personal information and collaboration ability for the screening problem of R&D team members, and applied it to an example to solve it using genetic algorithm, verifying the feasibility of the model. Watanabe et al. [4] introduced visual analysis methods into the study of member selection. Based on the collected information, they systematically analyzed the basic indicators of candidate members and visualized the evaluation process and prediction results of candidate members. Kumar et al. [5] used the fuzzy analytic hierarchy process to calculate indicator weights, combined with the decisionmaking method of fuzzy multiobjective optimization, established an optimization model, and obtained the ranking of candidate members. Cheng et al. [6] established a partner selection model in the supplier selection problem, with the constraints of cost, quality, completion time, and service quality in lean systems, and the goal of reducing time consumption.
In summary, research on the selection of consortium members in supply chain, transportation, virtual enterprises, and other fields is extensive and indepth. However, the acquisition of decisionmaking information lacks judgment on the reliability of information, and there is a possibility of information loss during the evaluation process. China's whole Process engineering consulting is still in its infancy. For small and mediumsized consulting enterprises to form a consortium, the lead enterprise should also establish a member selection model based on the internal characteristics and development status of the whole Process engineering consulting
2. The whole process of project consulting consortium member selection index system
The selection index of engineering consulting members in the whole process is screened and the index system is established. Member Member selection indicators are shown in Table 1. The meanings of specific indicators are as follows.
Table 1Member selection evaluation index
Target  Firstorder index  Secondary index 
Whole process project consulting consortium member selection evaluation index  Collaboration ability  Collaborative experience 
Ability to communicate  
Teamwork ability  
Technical capacity  Enterprise qualification  
Quality of personal  
Relevant project experience  
Ability to organize and manage  Organization chart  
Incentive mechanism  
Enterprise culture 
2.1. Index weight determination
The sequential method is a subjective weighting method. In the process of implementation, there is no need to construct judgment matrix and consistency test, and expert opinions can be fully expressed. There is no restriction on the number of indicators, and it has a good effect in determining the weight of indicators. The method firstly sorted the whole index, then compared the importance of adjacent indexes from weak to strong in pairs, and finally determined the weight of indexes by quantitative calculation by substituting the formula.
1) Deterministic order relation.
Suppose there are $n$ indicators in the index system. Respectively expressed as ${x}_{1}$,${x}_{2}$…${x}_{3}$. Its weight is expressed as ${w}_{1}$,${w}_{2}$…${w}_{3}$. The importance of indicators is ranked by experts, and after ranking, it is represented by ${x}_{1}^{\mathrm{*}}>{x}_{2}^{\mathrm{*}}\cdots {x}_{n}^{\mathrm{*}}$. The weight is expressed as ${w}_{1}^{\mathrm{*}},{w}_{2}^{\mathrm{*}}\cdots {w}_{n}^{\mathrm{*}}$．
2) Comparison of the importance of adjacent indicators.
Expert on adjacent indicators ${x}_{j1}^{\mathrm{*}}$ and ${x}_{j}^{\mathrm{*}}$ Compared to the importance of the evaluation, Getting the importance assignment ${r}_{j}$,${r}_{j}={w}_{j1}/{w}_{j}(j=\mathrm{2,3}\cdots ,n)$, at the same time must satisfy ${r}_{j1}>1/{r}_{j}$, degree of importance. The assignments ${r}_{j}$ are shown in Table 2.
Table 2Importance assignment
${r}_{j}$  Assignment specification 
1.0  Indicator ${x}_{j1}^{*}$ is as important as indicator ${x}_{j}^{*}$ 
1.2  Indicator ${x}_{j1}^{*}$ is slightly more important than indicator 
1.4  Indicator ${x}_{j1}^{*}$ is significantly more important than indicator ${x}_{j}^{*}$ 
1.6  Indicator ${x}_{j1}^{*}$ is more important than indicator ${x}_{j}^{*}$ 
1.8  Indicator ${x}_{j1}^{*}$ is extremely important than indicator ${x}_{j}^{*}$ 
3) Calculated weight.
According to Eq. (1), the weight of the least important index is obtained, and according to formula (2), the weight of other indicators is obtained successively [6]:
3. Whole process engineering consulting consortium member selection model
3.1. Modeling idea
The leading unit of the consortium selects the best partners for the whole process engineering consulting project, which is not only conducive to the smooth development of the project, but also conducive to the smooth implementation of the whole process engineering consulting model in the form of a joint body. Therefore, in view of the principle of selecting the best members, several experts were invited to evaluate and score the indicators of the alternative members based on Znumbers method. The weight of each indicator was calculated by the sequential method. The weight of each expert was obtained by using the information reliability part of Znumbers combined with the entropy weight method, and the data were summarized and substituted into fuzzy TOPSIS method. The positive and negative ideal distance of each member is calculated, and the member is ranked to determine the consortium cooperative enterprises
Fig. 1Member selection flowchart
3.2. Znumbers obtaining and processing data
3.2.1. The transformation of language variables
Language terms have fuzziness and uncertainty, which conform to the fuzzy limitation and reliability principle of Znumbers. Studies show that language variables have the best effect between 610 partitions. If the score interval of language terms is too small, experts will not consider the relevant information of the evaluation object comprehensively, which will affect the accuracy of data. If the score interval of language terms is too large, experts should have high evaluation experience, evaluation efficiency, knowledge reserve and other conditions, and the amount of evaluation tasks of experts should be increased. Therefore, the 7point conversion rule of language variables is adopted in this paper to convert the first part and the second part of Znumbers into trapezoidal fuzzy numbers and triangular fuzzy numbers respectively. The conversion rules of language variables are shown in Table 3 and Table 4.
Table 3Constraint language variables and trapezoidal fuzzy number conversion rules
Serial number  Constrains some language variables  Corresponding fuzzy number 
1  VP  (0,0,0.1,0.2) 
2  P  (0.1,0.2,0.2,0.3) 
3  MP  (0.2,0.3,0.4,0.5) 
4  M  (0.4,0.5,0.5,0.6) 
5  MG  (0.5,0.6,0.7,0.8) 
6  G  (0.7,0.8,0.8,0.9) 
7  VG  (0.8,0.9,1,1) 
Table 4Conversion rules of reliability language variables and triangular fuzzy numbers
Serial number  Constrains some language variables  Corresponding fuzzy number 
1  VL  (0,0,0.2) 
2  L  (0.05,0.2,0.35) 
3  ML  (0.2,0.35,0.5) 
4  M  (0.35,0.5,0.65) 
5  MH  (0.5,0.65,0.8) 
6  H  (0.65,0.8,0.95) 
7  VH  (0.8,1,1) 
3.2.2. ZNumbers are converted to classical fuzzy numbers
Kang proposed a method that can approximate ZNumbers into classical fuzzy numbers under the premise of partial loss of information, which is conducive to improving decision efficiency and practical application. Its basic thinking path is as follows: First, let $Z=(A,B)$ be a ZNumber, Where A is the restrictive part, and the membership function is 1 $\left\{A=(x,{\mu}_{A})\leftx\in \left[\mathrm{0,1}\right]\right.\right\}$, $B$ is the reliability part. The membership function is expressed as $\left\{B=(x,{\mu}_{B})\leftx\in \left[\mathrm{0,1}\right]\right.\right\}$; Part $B$ of reliability judgment is transformed according to gravity center method. You get a clear number $\lambda $ for a fuzzy number $B$, As shown in Eq. (3). For triangular fuzzy numbers the results of centroid calculation formula are shown in Eq. (4):
Then, the barycenter value $\lambda $ of reliability part $B$ can be used as the weight of restriction part $A$. Then $Z=(A,B)$ can be converted into the classical trapezoidal fuzzy number ${Z}^{\mathrm{\text{'}}}$. As shown in Eq. (5). If the constraint part $A$ is a trapezoidal fuzzy number, it can be expressed as Eq. (6):
3.3. Expert weight calculation
The initial evaluation data contains the opinions of several experts, which need to be integrated to calculate the weight of each expert opinion. The reliability part of ZNumbers represents the degree of experts' understanding of the information of evaluation objects. It can be used to calculate the weight of experts by combining the entropy weight method with the reliability part. When an expert has a full understanding of the information of evaluation objects, the accuracy of his evaluation will be higher, so the weight of the expert will be higher. The expert weight is calculated as shown in Eqs. (7), (8) and (9):
where ${\beta}_{x}$ represents the weight of the $x$ TH expert, ${\partial}_{x}^{Z}$ represents the reliability function of the $x$ TH expert opinion, ${Z}_{ij}^{x}$ represents the reliability value of the evaluation opinion of the $x$ TH expert on the $j$ TH index of the $i$ TH evaluation object. That is, the reliability of ZNumbers is partially clarified. There are a total of $k$ experts, $m$ evaluation objects and $n$ evaluation indicators. Eqs. (10) and (11) were used to integrate the opinions of several evaluation experts into the evaluation matrix of alternative members:
3.4. TOPSIS determines the member ordering
1) The index weight obtained by the sequential method is weighted to the evaluation matrix of alternative members, as shown in Eq. (12), where ${\alpha}_{j}$ represents the weight of the $j$th index:
2) Determine the most ideal index ${A}^{+}$ and the least ideal index ${A}^{}$:
When the $j$ indicator is a benefit indicator, it is shown in Eq. (15). When the $j$ indicator is a costtype indicator, it is shown in Eq. (16):
3) Solve the positive and negative ideal distance. The distance calculation of two trapezoidal fuzzy numbers is shown in Eqs. (17) and (18), and the distance calculation of two trapezoidal fuzzy numbers is shown in Eq. (19):
4) Solve the ideal point distance. Where, ${\theta}_{i}$ represents the degree of closeness between the $i$ TH evaluation object and the positive ideal solution:
5) When sorting alternative units, the larger the ${\theta}_{i}$ value is, the better the evaluation object is and the more it meets the selection demand of the lead unit. The largest ${\theta}_{i}$ value belongs to the consortium partner.
4. Example
Currently, a design institute, as the lead enterprise, has formed a consortium to carry out the whole process of CBD construction engineering consulting project of a city business district. The design institute can provide design services and construction cost services. The project owner requires the whole process of engineering consulting services including supervision services. All submitted relevant information about their enterprises.
4.1. Determine index weight
The weight of each index is determined by the sequential method, as shown in Table 5.
Table 5Weight of each indicator
Firstorder index  Firstorder index weight  Secondary index  Secondary index weight  Comprehensive weight 
Collaboration ability  0.31  Collaborative experience  0.396  0.123 
Ability to communicate  0.275  0.085  
Teamwork ability  0.33  0.102  
Technical capacity  0.434  Enterprise qualification  0.245  0.106 
Quality of personal  0.412  0.179  
Relevant project experience  0.343  0.149  
Ability to organize and manage  0.258  Organization chart  0.467  0.120 
Incentive mechanism  0.292  0.075  
Enterprise culture  0.243  0.063 
4.2. Evaluation and scoring
4 industry experts are invited to evaluate A, B and C enterprises according to the 7point scale terms, as shown in Table 6, 7 and 8.
4.3. Znumbers transform
ZNumbers are converted into classical fuzzy numbers, as shown in Table 9, Table 10 and Table 11.
4.4. Calculated expert weight
The reliability part of Znumbers is clarified according to Eq. (7), the expert weight is calculated according to Eqs. (8) and (9), and the member selection evaluation matrix is obtained according to the index weight, as shown in Table 12.
4.5. TOPSIS calculates the rankings
Since indexes in this paper are benefit indexes, the values of positive and negative ideal solutions are treated as ${J}_{1}$. The positive and negative ideal distance of each unit in TOPSIS is shown in Table 13. The value of the alternative units is ${\theta}_{1}=$ 0.850, ${\theta}_{2}=$ 0.154, ${\theta}_{3}=$ 0.625. Among them, Unit A has the highest evaluation value, that is, Unit A is the optimal partner.
The calculation results show that, after the evaluation of the indicators of the three supervision units A, B and C by four experts, the modelbased calculation and analysis results show that unit A is the best and has obvious advantages. In reality, Unit A has advantages in technical ability and team cooperation ability, which is in line with the realistic results and verifies the applicability of the model.
Table 6A Unit expert evaluation language value
Expert 1  Expert 2  Expert 3  Expert 4  
U_{1}  U_{11}  (MG,H)  (G,H)  (G,MH)  (MG,MH) 
U_{12}  (MG,MH)  (M,H)  (MG,M)  (MG,MH)  
U_{13}  (MG,VH)  (M,M)  (MG,M)  (MG,H)  
U_{2}  U_{21}  (MG,ML)  (G,H)  (MG,H)  (VG,ML) 
U_{22}  (M,MH)  (MG,ML)  (MG,H)  (VG,H)  
U_{23}  (MG,M)  (G,MH)  (G,ML)  (M,M)  
U_{3}  U_{31}  (MG,M)  (M,M)  (MG,MH)  (M,MH) 
U_{32}  (M,VH)  (MP,MH)  (MG,H)  (MP,MH)  
U_{33}  (MG,M)  (MP,H)  (MP,M)  (M,M) 
Table 7B Unit expert evaluation language value
Expert 1  Expert 2  Expert 3  Expert 4  
U_{1}  U_{11}  (M, H)  (M, M)  (P,MH)  (MP,MH) 
U_{12}  (P,MH)  (MP,MH)  (M,M)  (M,H)  
U_{13}  (M,H)  (M, MH)  (MP,ML)  (P,MH)  
U_{2}  U_{21}  (MG,H)  (M, MH)  (MG,H)  (M,H) 
U_{22}  (MP,MH)  (M,ML)  (MP,H)  (M,M)  
U_{23}  (M,M)  (P,MH)  (MP,MH)  (P,MH)  
U_{3}  U_{31}  (MG,H)  (M,MH)  (MG,H)  (MG,M) 
U_{32}  (MP,M)  (M,M)  (G,ML)  (M,ML)  
U_{33}  (G,H)  (MG,H)  (M,MH)  (MG,H) 
Table 8C Unit expert evaluation language value
Expert 1  Expert 2  Expert 3  Expert 4  
U_{1}  U_{11}  (MG,MH)  (MG,M)  (M,H)  (M,MH) 
U_{12}  (M,M)  (MG,VH)  (MG,M)  (MG,ML)  
U_{13}  (M,MH)  (MG,ML)  (MP,M)  (G,M)  
U_{2}  U_{21}  (M,MH)  (MG,M)  (G,ML)  (MG,M) 
U_{22}  (MG,M)  (M,M)  (G,MH)  (M,MH)  
U_{23}  (M,H)  (MG,MH)  (M,ML)  （MG,MH）  
U_{3}  U_{31}  (MG,MH)  (M,M)  (MG,H)  (MG,VH) 
U_{32}  (M,ML)  (MP,M)  (G,H)  (MG,MH)  
U_{33}  (M,M)  (MG,MH)  (MG,M)  (M,M) 
Table 9A unit classical fuzzy number
Expert 1  Expert 2  Expert 3  Expert 4  
U_{11}  0.447,0.537,0.626,0.716  0.626,0.716,0.716,0.805  0.564,0.645,0.645,0.726  0.403,0.484,0.564,0.645 
U_{12}  0.403,0.484,0.564,0.645  0.358,0.447,0.447,0.537  0.354,0.424,0.495,0.566  0.403,0.484,0.564,0.645 
U_{13}  0.483,0.58,0.676,0.773  0.283,0.354,0.354,0.424  0.354,0.424,0.495,0.566  0.447,0.537,0.626,0.716 
U_{21}  0.296,0.355,0.414,0.473  0.626,0.716,0.716,0.805  0.447,0.537,0.626,0.716  0.473,0.532,0.592,0.592 
U_{22}  0.322,0.403,0.403,0.484  0.296,0.355,0.414,0.473  0.447,0.537,0.626,0.716  0.716,0.805,0.894,0.894 
U_{23}  0.354,0.424,0.495,0.566  0.564,0.645,0.645,0.726  0.414,0.473,0.473,0.532  0.283,0.354,0.354,0.424 
U_{31}  0.354,0.424,0.495,0.566  0.283,0.354,0.354,0.424  0.414,0.473,0.473,0.532  0.322,0.403,0.403,0.484 
U_{32}  0.386,0.483,0.483,0.58  0.161,0.242,0.322,0.403  0.447,0.537,0.626,0.716  0.161,0.242,0.322,0.403 
U_{33}  0.354,0.424,0.495,0.566  0.179,0.268,0.358,0.447  0.141,0.212,0.283,0.354  0.283,0.354,0.354,0.424 
Table 10B unit classical fuzzy number
Expert 1  Expert 2  Expert 3  Expert 4  
U_{11}  0.358,0.447,0.447,0.537  0.283,0.354,0.354,0.424  0.081,0.161,0.161,0.242  0.161,0.242,0.322,0.403 
U_{12}  0.081,0.161,0.161,0.242  0.161,0.242,0.322,0.403  0.283,0.354,0.354,0.424  0.358,0.447,0.447,0.537 
U_{13}  0.358,0.447,0.447,0.537  0.322,0.403,0.403,0.484  0.118,0.177,0.237,0.296  0.081,0.161,0.161,0.242 
U_{21}  0.447,0.537,0.626,0.716  0.322,0.403,0.403,0.484  0.447,0.537,0.626,0.716  0.358,0.447,0.447,0.537 
U_{22}  0.161,0.242,0.322,0.403  0.237,0.296,0.296,0.355  0.179,0.268,0.358,0.447  0.283,0.354,0.354,0.424 
U_{23}  0.283,0.354,0.354,0.424  0.081,0.161,0.161,0.242  0.161,0.242,0.322,0.403  0.081,0.161,0.161,0.242 
U_{31}  0.447,0.537,0.626,0.716  0.322,0.403,0.403,0.484  0.447,0.537,0.626,0.716  0.354,0.424,0.495,0.566 
U_{32}  0.141,0.212,0.283,0.354  0.283,0.354,0.354,0.424  0.414,0.473,0.473,0.532  0.237,0.296,0.296,0.355 
U_{33}  0.626,0.716,0.716,0.805  0.447,0.537,0.626,0.716  0.322,0.403,0.403,0.484  0.447,0.537,0.626,0.716 
Table 11C unit classical fuzzy number
Expert 1  Expert 2  Expert 3  Expert 4  
U_{11}  0.403,0.484,0.564,0.645  0.354,0.424,0.495,0.566  0.358,0.447,0.447,0.537  0.322,0.403,0.403,0.484 
U_{12}  0.283,0.354,0.354,0.424  0.483,0.58,0.676,0.773  0.354,0.424,0.495,0.566  0.296,0.355,0.414,0.473 
U_{13}  0.322,0.403,0.403,0.484  0.296,0.355,0.414,0.473  0.141,0.212,0.283,0.354  0.495,0.566,0.566,0.636 
U_{21}  0.322,0.403,0.403,0.484  0.354,0.424,0.495,0.566  0.414,0.473,0.473,0.532  0.354,0.424,0.495,0.566 
U_{22}  0.354,0.424,0.495,0.566  0.283,0.354,0.354,0.424  0.564,0.645,0.645,0.726  0.322,0.403,0.403,0.484 
U_{23}  0.358,0.447,0.447,0.537  0.403,0.484,0.564,0.645  0.237,0.296,0.296,0.355  0.403,0.484,0.564,0.645 
U_{31}  0.403,0.484,0.564,0.645  0.283,0.354,0.354,0.424  0.447,0.537,0.626,0.716  0.483,0.58,0.676,0.773 
U_{32}  0.237,0.296,0.296,0.355  0.141,0.212,0.283,0.354  0.626,0.716,0.716,0.805  0.403,0.484,0.564,0.645 
U_{33}  0.283,0.354,0.354,0.424  0.403,0.484,0.564,0.645  0.354,0.424,0.495,0.566  0.283,0.354,0.354,0.424 
Table 12Alternative enterprise member selection evaluation matrix
Company A  Company B  company C  
U_{11}  0.0628, 0.0734, 0.0785, 0.0890  0.0270, 0.0369, 0.0393, 0.0492  0.0442, 0.0540, 0.0586, 0.0686 
U_{12}  0.0322, 0.0391, 0.0439, 0.0508  0.0188, 0.0257, 0.0274, 0.0342  0.0302, 0.0365, 0.0413, 0.0476 
U_{13}  0.0399, 0.0482, 0.0547, 0.0630  0.0223, 0.0302, 0.0317, 0.0397  0.0319, 0.0391, 0.0424, 0.0496 
U_{21}  0.0490, 0.0569, 0.0624, 0.0687  0.0417, 0.0509, 0.0556, 0.0649  0.0383, 0.0457, 0.0495, 0.0570 
U_{22}  0.0798, 0.0940, 0.1047, 0.1150  0.0385, 0.0520, 0.0595, 0.0729  0.0682, 0.0818, 0.0849, 0.0985 
U_{23}  0.0603, 0.0707, 0.0733, 0.0838  0.0224, 0.0341, 0.0371, 0.0487  0.0522, 0.0637, 0.0697, 0.0812 
U_{31}  0.0412, 0.0496, 0.0517, 0.0601  0.0471, 0.0570, 0.0644, 0.0744  0.0485, 0.0586, 0.0665, 0.0767 
U_{32}  0.0216, 0.0282, 0.0329, 0.0394  0.0202, 0.0251, 0.0264, 0.0313  0.0264, 0.0321, 0.0349, 0.0406 
U_{33}  0.0150, 0.0197, 0.0234, 0.0281  0.0289, 0.0344, 0.0373, 0.0428  0.0209, 0.0255, 0.0279, 0.0325 
Table 13Positive and negative ideal distances in fuzzy topsis
Company A  Company B  Company C  
$d\left({p}_{1j},{p}_{j}^{+}\right)$  $d\left({p}_{1j},{p}_{j}^{}\right)$  $d\left({p}_{2j},{p}_{j}^{+}\right)$  $d\left({p}_{2j},{p}_{j}^{}\right)$  $d\left({p}_{3j},{p}_{j}^{+}\right)$  $d\left({p}_{3j},{p}_{j}^{}\right)$  
T_{1}  T_{11}  0.0000  0.0409  0.0409  0.0000  0.0212  0.0197 
T_{12}  0.0000  0.0163  0.0163  0.0000  0.0028  0.0134  
T_{13}  0.0000  0.0223  0.0223  0.0000  0.0117  0.0106  
T_{2}  T_{21}  0.0000  0.0126  0.0066  0.0063  0.0126  0.0000 
T_{22}  0.0000  0.0463  0.0463  0.0000  0.0166  0.0299  
T_{23}  0.0000  0.0394  0.0394  0.0000  0.0062  0.0337  
T_{3}  T_{31}  0.0134  0.0000  0.0021  0.0114  0.0000  0.0134 
T_{32}  0.0035  0.0057  0.0085  0.0000  0.0000  0.0085  
T_{33}  0.0154  0.0000  0.0000  0.0154  0.0099  0.0056 
5. Conclusions
To carry out consulting work in a consortium is a necessary way to effectively promote the whole process of engineering consulting. From three aspects of team cooperation, technical ability, organization and management, the whole process of project consulting consortium member selection evaluation index system including 9 indexes is put forward. Znumbers combined with entropy weight method were used to obtain the weight of expert opinions, and ZNumbers fuzzy TOPSIS model was introduced to solve the ranking of alternative enterprises. The uncertainty and reliability of decision information were fully considered. Fuzzy TOPSIS was used to calculate the distance between fuzzy numbers, reducing the loss of information. It can reflect the reality well. The applicability of the model is verified by an example. The optimization model of the consortium members in the whole process of engineering consulting provides a feasible method to solve the problem of leading enterprises choosing partners.
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About this article
This research is supported by National Science and Technology support program (2013BAJ10B092).
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Zecheng Liu: data curation, formal analysis, investigation, methodology, visualization, writingoriginal draft preparation. Jianyu Chu: conceptualization, funding acquisition, supervision, validation. Jinjian Du: project administration, software, writingreview and editing.
The authors declare that they have no conflict of interest.