CN106503895A - A kind of virtual contract network Team Member method for optimizing - Google Patents

Abstract

The present invention proposes a method for selecting members of a virtual collaborative network team. From the candidates on the virtual network platform, according to the individual ability and collaborative ability of the members, the comprehensive performance information model of the candidate members is constructed, and the personnel who meet the project requirements are selected according to the order, so as to reduce the number of network members. Uncertainty and conflicts in collaboration, and the purpose of improving the efficiency of project cooperation. The steps are: build the index system of candidate members' personal ability and collaborative cooperation ability, establish a virtual collaborative network team member selection model based on the comprehensive performance of members, and use the multi-objective optimization algorithm based on the personal information of the candidate members and the project participation information of the candidate members to score according to the indicators Solve the model to obtain the Pareto optimal solution. Candidate ranking based on the comprehensive performance information of members is obtained, and suitable members are selected to form a virtual collaborative network team. The invention embodies the comprehensive ability of candidate members, realizes the complementarity and collaboration of knowledge and abilities among team members, avoids personnel conflicts in the later stage of collaboration from the source, improves team performance, and has good application value.

Description

A Method for Optimizing Team Members in Virtual Collaborative Network

technical field

The invention relates to the field of information technology, in particular to a method for optimizing team members in a virtual collaborative network.

Background technique

With the development of cloud computing, big data and other information technologies, virtual network platforms, as an innovative service model that realizes resource optimization and integration, collaborative allocation, and on-demand use, have emerged in various industries. At present, the virtual network platform widely adopts the virtual collaborative network team work mode. By convening cross-regional, cross-professional, and cross-industry members to collaborate with each other, a temporary collaborative team is formed to complete innovation tasks through knowledge sharing and complementary advantages. Proper selection of virtual collaborative network team members is the premise and basis for the smooth progress and completion of project tasks.

The selection of virtual collaborative network team members not only needs to consider the individual ability of the members, but also needs to consider the collaborative ability among candidate members. The excellent professional skills of team members and the good cooperative relationship among members can promote communication among members and improve satisfaction, thereby reducing uncertainty and conflicts in network collaboration, realizing the complementarity of resources, capabilities and advantages, and finally achieving synergy The success of the work, to achieve the goal that is difficult to achieve only by the strength of a single subject.

Existing studies have explored this issue from the perspectives of member optimization index system establishment, qualitative analysis and quantitative analysis, but they have not reflected the complex collaborative network factors in the virtual network platform well, and most decision-making methods only Considering the individual professional ability of the candidate members rarely reflects the comprehensive ability performance of the members, and it cannot guarantee that the selected members can exert better collaboration ability in the later project cooperation.

Contents of the invention

Aiming at the problems existing in the above-mentioned prior art, the present invention studies the workflow of a virtual collaborative network team in a virtual network platform environment, builds a comprehensive index system that reflects the individual capabilities of team members and the collaborative cooperation capabilities among members, and establishes cross-regional, Cross-professional, cross-industry virtual collaborative network team to achieve knowledge and ability complementation and effective collaboration among team members, avoiding personnel conflicts in the later stage of collaboration from the source, and ultimately improving team performance.

In order to achieve the above purpose, the present invention proposes a method for optimizing team members in a virtual collaborative network, the technical solution of which is:

The method for optimizing team members in a virtual collaborative network is characterized in that: comprising the following steps:

Step 1: Construct the index system of individual ability and collaborative ability of project candidate members;

Step 2: Establish a virtual collaborative network team member selection model based on the comprehensive performance of members;

Step 3: Get the personal data information of the candidate members from the platform user database, get the project participation information of the candidate members from the project database, and score according to each indicator in the indicator system;

Step 4: Substitute the index scores of candidate members obtained in step 3 into the model established in step 2, and use the multi-objective optimization algorithm to solve the model to obtain the Pareto optimal solution; select members according to the ranking of candidates represented by the obtained Pareto optimal solution, Form a virtual collaborative network team.

A further preferred solution, the method for selecting members of a virtual collaborative network team, is characterized in that: in step 1, the individual ability indicators of the candidate members are divided into work experience, professional ability, and professional knowledge; in step 1, the collaborative cooperation ability of the candidate members The indicators are divided into member evaluation grades, the number of projects completed in cooperation with others, and the number of problems solved by assisting other members; the member evaluation grades are divided into collaboration attitude, communication ability, and teamwork ability.

A further preferred solution, the method for selecting members of a virtual collaborative network team, is characterized in that: in step 2, the establishment of a virtual collaborative network team member optimal model based on the comprehensive performance of members includes the following steps:

Step 2.1: Establish a member selection model based on personal ability information;

Step 2.2: Establish a member selection model based on collaborative capability information;

Step 2.3: Synthesize the member selection model based on personal ability information and the member selection model based on collaborative cooperation ability information to obtain a member selection model based on member comprehensive performance information, which is a dual-objective 0-1 quadratic programming model.

A further preferred solution, the method for selecting members of a virtual collaborative network team, is characterized in that: the member selection model based on personal ability information described in step 2.1 is established through the following steps:

Step 2.1.1: On the virtual collaborative network platform, after the customer submits the design task, the management team will issue the task announcement to the relevant h network subgroups after a quick demand analysis, and the number of candidate members in the network subgroup j is n _j , the total number of candidate members who signed up to participate in the design task The number of selected target members in network subgroup j is q _j , and the total number of selected target members is

Record the n candidate members who signed up to participate in the design task as P _i , i=1,…,n; the individual ability evaluation index is I _g ,g=1,…,l, and the weights of each item are v _g , and x _i is a decision variable, when candidate member P _i is selected, x _i =1, otherwise x _i =0;

Step 2.1.2: Construct the candidate member's personal ability evaluation index matrix R=[r _ig ] _n×l , where r _ig is the score of the candidate member P _i under the personal ability evaluation index I _g ; the personal ability evaluation index is calculated according to Standardize the benefit-type indicators and cost-type indicators to obtain the matrix R'=[r' _ig ] _n×l :

Benefit indicators:

Cost indicators:

in

Step 2.1.3: According to the formula Calculating the individual performance comprehensive value of the candidate member P _i ;

Step 2.1.4: According to the obtained individual performance comprehensive value of each candidate member Get the member's individual performance preference model:

x _i ∈ {0,1},i=1,…,n

in,

A further preferred solution, the method for selecting members of a virtual collaborative network team, is characterized in that: in step 2.2, the member selection model based on collaborative cooperation ability information is obtained through the following steps:

Step 2.2.1: Establish a decision matrix of collaborative performance information for candidate members in is the synergy performance score under the collaborative ability evaluation index C _{k when} the candidate member P _i cooperates with the candidate member P _t ; is w _k , and

Step 2.2.2: Put The elements in the matrix are normalized to obtain the matrix

Benefit indicators:

Cost indicators:

in

Step 2.2.3: According to the formula i≠t calculates the comprehensive value ψ _it of the synergistic performance of the candidate member P _i when cooperating with the candidate member P _t ;

Step 2.2.4: According to the obtained synergy performance comprehensive value ψ _it ,i,t=1,...,n; i≠t, get the member selection model based on the synergy ability information:

x _i ∈ {0,1},i=1,…,n

in,

A further preferred solution, the method for selecting members of a virtual collaborative network team, is characterized in that: the member selection model based on the comprehensive performance information of members described in step 2.3 is established through the following steps:

Integrate the member individual performance optimization model obtained in step 2.1.4 with the member collaboration optimization model obtained in step 2.2.4 to obtain the following dual-objective 0-1 quadratic programming model:

x _i ∈ {0,1},i=1,…,n

in,

Beneficial effect

Aiming at the characteristics of network synergy and task complexity of the virtual network platform, the present invention proposes a virtual collaborative network team member selection method based on members’ comprehensive performance information, which not only considers members’ personal ability information, but also considers members’ collaboration Cooperation ability, constructing an optimal index system and a mathematical model based on the comprehensive performance information of members, and using the multi-objective evolutionary algorithm to solve the model, and obtain the Pareto optimal solution of the virtual team member selection problem. Decision makers can base their individual performance and Synergistically expressed preferences for selection. The project members selected by the present invention have good cooperation attitude, excellent communication ability, members with rich cooperation experience and good cooperation ability, which can effectively promote communication among members, improve satisfaction among members, reduce cooperation conflicts and Uncertainty is of great significance to the smooth development and efficiency improvement of virtual collaborative innovation work. The invention can be further extended to the process of forming virtual teams in other backgrounds, and has certain application significance.

Description of drawings

Fig. 1 is a flow chart of a method for optimizing members of a virtual collaborative network team based on member comprehensive performance information provided by an embodiment of the present invention;

Fig. 2 is an index system of personal ability and collaborative ability of project candidate members provided by the embodiment of the present invention.

detailed description

Embodiments of the present invention are described in detail below, and the embodiments are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

The applicant has studied the workflow of the virtual collaborative network team under the virtual network platform environment, and constructed a comprehensive index system reflecting the individual ability of the team members and the collaborative cooperation ability between members, and based on this, proposed the virtual collaborative network team member of the present invention The optimal method realizes the establishment of cross-regional, cross-professional, and cross-industry virtual collaborative network teams on demand, realizes the complementarity and effective collaboration of knowledge and capabilities among team members, and avoids personnel conflicts in the later stage of collaboration from the source. Ultimately improve team performance.

The specific implementation steps of the virtual collaborative network team member optimization method of the present invention are as shown in Figure 1:

Step 1: Construct the individual ability and collaborative ability index system of project candidate members, as shown in Figure 2:

Among them, the personal ability index is divided into work experience, professional ability, and professional knowledge. Through work experience, professional ability, and professional knowledge to examine the personal performance ability of candidate members; the collaborative ability index is divided into member evaluation grades, projects completed in cooperation with others Quantity, the number of helping other members to solve problems; use the member evaluation level, the number of projects completed in cooperation with other people in the past, and the number of helping other members to solve problems to examine the information of the collaborative ability of candidate members. The member evaluation level is divided into collaboration attitude and communication ability ,team work.

Step 2: Establish a virtual collaborative network team member selection model based on the comprehensive performance of members; including the following steps:

Step 2.1: Establish a member selection model based on personal ability information through the following steps:

Step 2.1.1: On the virtual collaborative network platform, after the customer submits the design task, the management team will issue the task announcement to the relevant h network subgroups after a quick demand analysis, and the number of candidate members in the network subgroup j is n _j , the total number of candidate members who signed up to participate in the design task The number of selected target members in network subgroup j is q _j , and the total number of selected target members is

Record the n candidate members who signed up to participate in the design task as P _i , i=1,…,n; the individual ability evaluation index is I _g ,g=1,…,l, and the weights of each item are v _g , and x _i is a decision variable, when candidate member P _i is selected, x _i =1, otherwise x _i =0;

Step 2.1.2: Construct the candidate member's personal ability evaluation index matrix R=[r _ig ] _n×l , where r _ig is the score of the candidate member P _i under the personal ability evaluation index I _g ; the personal ability evaluation index is calculated according to The matrix R'=[r' _ig ] _n×l is obtained by normalizing the benefit index and the cost index,

Benefit indicators:

Cost indicators:

in,

Step 2.1.3: According to the formula Calculating the individual performance comprehensive value of the candidate member P _i ;

Step 2.1.4: According to the obtained individual performance comprehensive value of each candidate member Get the member selection model based on the member's comprehensive performance information:

x _i ∈ {0,1},i=1,…,n

Step 2.2: Establish a member selection model based on information on collaborative cooperation capabilities, which can be obtained through the following steps:

Step 2.2.1: Establish a decision matrix of collaborative performance information for candidate members in is the synergy performance score under the collaborative ability evaluation index C _{k when} the candidate member P _i cooperates with the candidate member P _t ; is w _k , and

Step 2.2.2: Put The elements in the matrix are normalized to obtain the matrix

Benefit indicators:

Cost indicators:

in

Step 2.2.3: According to the formula i≠t calculates the comprehensive value ψ _it of the synergistic performance of the candidate member P _i when cooperating with the candidate member P _t ;

Step 2.2.4: According to the obtained synergy performance comprehensive value ψ _it ,i,t=1,...,n; i≠t, get the member selection model based on the synergy ability information:

x _i ∈ {0,1},i=1,…,n

in,

Step 2.3: Synthesize the member selection model based on personal ability information and the member selection model based on collaborative cooperation ability information to obtain a member selection model based on comprehensive performance information of members, which is a dual-objective 0-1 quadratic programming model:

x _i ∈ {0,1},i=1,…,n

in,

Step 3: Get the personal data information of the candidate members from the platform user database, get the project participation information of the candidate members from the project database, and score according to each indicator in the indicator system.

Step 4: Substitute the candidate member index scores obtained in step 3 into the model established in step 2, and use the multi-objective optimization algorithm: the second generation enhanced Pareto evolutionary algorithm (SPEA2) to solve the model and obtain the Pareto optimal solution; according to the design task According to the needs and decision-making preferences, different member combinations are selected according to the matching ratio of candidate members' personal abilities and collaborative cooperation. According to the ranking of candidates represented by the obtained Pareto optimal solution, select members as needed to form a virtual collaborative network team.

Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limitations to the present invention. Variations, modifications, substitutions, and modifications to the above-described embodiments are possible within the scope of the present invention.

Claims (6)

1. A virtual collaborative network team member optimization method is characterized by comprising the following steps: the method comprises the following steps:

step 1: constructing an index system of personal ability and cooperative ability of candidate members of the project;

step 2: establishing a virtual cooperative network team member optimization model based on member comprehensive performance;

and step 3: calling personal data information of candidate members from a platform user database, calling project participation information of the candidate members from a project database, and scoring according to each index in an index system;

and 4, step 4: substituting the candidate member index fraction obtained in the step 3 into the model established in the step 2, and solving the model by adopting a multi-objective optimization algorithm to obtain a Pareto optimal solution; and selecting members according to the candidate sequence represented by the obtained Pareto optimal solution to form a virtual collaborative network team.

2. The method for team member optimization in virtual collaborative network according to claim 1, wherein: in the step 1, personal ability indexes of the candidate members are divided into work experience, professional ability and professional knowledge; in the step 1, the cooperative cooperation capability indexes of the candidate members are divided into member evaluation grades, the number of projects completed by cooperating with other members and the number of problems for assisting other members to solve; the member evaluation level is divided into a cooperation attitude, an exchange communication capability and a team cooperation capability.

3. The method for team member optimization in virtual collaborative network according to claim 1, wherein: the step 2 of establishing the member preference model of the virtual collaborative network team based on the member comprehensive performance comprises the following steps:

step 2.1: establishing a member selection model based on personal ability information;

step 2.2: establishing a member selection model based on the cooperative cooperation capability information;

step 2.3: and synthesizing the member selection model based on the personal ability information and the member selection model based on the cooperative ability information to obtain a member optimization model based on the member comprehensive performance information, wherein the member optimization model is a dual-target 0-1 quadratic programming model.

4. The method for team member preference in virtual collaborative network as claimed in claim 2, wherein: the member selection model based on personal ability information in step 2.1 is built by the following steps:

step 2.1.1: on a virtual cooperative network platform, after a customer submits a design task, a management team analyzes the task through rapid demand analysisThe announcements are sent to h related network subgroups, wherein the number of candidate members in the network subgroup j is n_jTotal number of candidate members for entry application to participate in the design taskThe number of selected target members in the network subgroup j is q_jTotal number of selected target members is

Marking n candidate members participating in design task of registration application as P_iI is 1, …, n; the personal ability evaluation index is I_gG is 1, …, l, each item is weighted by v_gAnd is andx_ias a decision variable, then candidate member P_iWhen selected x_i1, otherwise x_i＝0；

Step 2.1.2: constructing a personal ability evaluation index matrix R ═ R of candidate members_ig]_n×lWherein r is_igIs a candidate member P_iIn the index I of personal ability evaluation_gA score of; normalizing the personal ability evaluation index according to the benefit index and the cost index to obtain a matrix R '═ R'_ig]_n×l：

The benefit type index is as follows:

cost type index:

wherein

Step 2.1.3: according to the formulaComputing candidate members P_i(ii) individual performance composite value of;

step 2.1.4: according to the obtained individual performance comprehensive value of each candidate memberObtaining a personal performance preference model for the member:

$\begin{array}{ccc}s.t.& \underset{i\∈n_j}{\Σ}{x}_i=q_j,& j=1,...,h,\end{array}$

x_i∈{0,1},i＝1,…,n

wherein,

5. the method for team member preference in virtual collaborative network as claimed in claim 2, wherein: the member selection model based on the cooperative cooperation capability information in the step 2.2 is obtained through the following steps:

step 2.2.1: establishing a collaborative performance information decision matrix of candidate membersWhereinIs a candidate member P_iIn the presence of candidate member P_tWhen performing cooperation, corresponding cooperative ability evaluation index C_k(ii) a synergy performance score; evaluation index C of cooperative ability_kK is 1, …, m, each item weight is w_kAnd is and

step 2.2.2: will be provided withNormalizing the elements in the matrix to obtain the matrix

The benefit type index is as follows:

cost type index:

wherein

Step 2.2.3: according to the formulaComputing candidate members P_iIn the presence of candidate member P_tCollaborative representation of composite value psi when performing collaboration_it；

Step 2.2.4: according to the obtained comprehensive value psi of the cooperative expression_itI, t ═ 1, …, n; and i ≠ t, and a member selection model based on the cooperative cooperation capability information is obtained:

$\max Z_2=\underset{i=1}{\overset{n}{\Σ}}\underset{t\≠i}{\overset{n}{\underset{t=1}{\Σ}}}{\mathrm{\ψ}}_{it}{x}_i{x}_t$

$\begin{array}{ccc}s.t.& \underset{i\∈n_j}{\Σ}{x}_i=q_j,& j=1,...,h\end{array}$

x_i∈{0,1},i＝1,…,n

wherein,

6. the method for team member preference in virtual collaborative network as claimed in claim 2, wherein: the member optimization model based on the member comprehensive performance information in the step 2.3 is established through the following steps:

integrating the member individual performance optimization model obtained in the step 2.1.4 and the member cooperation optimization model obtained in the step 2.2.4 to obtain a double-target 0-1 quadratic programming model as follows:

$\max Z_2=\underset{i=1}{\overset{n}{\Σ}}\underset{t\≠i}{\overset{n}{\underset{t=1}{\Σ}}}{\mathrm{\ψ}}_{it}{x}_i{x}_t$

$\begin{array}{ccc}s.t.& \underset{i\∈n_j}{\Σ}{x}_i=q_j,& j=1,...,h\end{array}$

x_i∈{0,1},i＝1,…,n

wherein,