Team Optimization Method For Crowd Sourcing Cloud Generic Making And Its Application In The Platform Of Innovation,Creation And Entrepreneurship

Large-scale Internet users are not only consumers of Internet applications and services,but also providers of Internet big data and content,which constitute a wealth of crowd sourcing resources.The effective use of crowd sourcing resources requires solving the problem of task assignment.Crowd sourcing cloud generic making refers to transparent collaboration of multiple agents in the cloud to complete "generic making" tasks,based on knowledge guidance and multilayered task decomposition and synthesis.The "generic making" also includes innovation and creation.And the virtual team is an organizational form supporting individuals at different time and in different geographical locations to collaborate in an open Internet environment to complete quite complicated "generic making" tasks.In order to solve the mapping problem from task set to team resource set,that is,task assignment problem,this thesis mainly does three things: 1.Team effectiveness evaluation model is proposed;2.Team optimization model is established and effectiveness analysis is made;3.Crowd sourcing cloud making module in the Sanchuang platform are designed and implemented.This thesis selects appropriate factors which influence effectiveness and extracts four measurable indicators,namely task execution speed,price reasonable degree,ability matching degree and expected completion quality.And corresponding calculation methods are proposed to calculate the expected benefit of the team to complete the task.Expected benefits,along with task priorities,form the data base for model construction and calculation.Then,a multi-objective 0-1 programming model for global optimization team selection is established.The optimization objectives include maximization of assignment coverage(considering quantity,priority),and total assignment benefits(considering time,cost,ability and quality),and the ideal point method is given as an exemplary solution method.This thesis then further analyzes the weights determination of the evaluation function and the selection of the objective function through theoretical argumentation,and then sums up the global optimization team selection method for crowd sourcing cloud generic making called GO-TSM assignment method.Subsequently,this thesis analyzes and verifies the global optimization team selectionmodel through multiple calculation examples,and uses MATLAB and LINGO for the simulation.The MATLAB script implementation method and simulation data generation method of GO-TSM assignment method are provided.Simulation experiments are demonstrated and comparative analysis are made with the other two feasible assignment methods,namely priority prioritization and benefit prioritization methods.The proportion of tasks assigned,the proportion of high-priority tasks,and the total benefits are better than the other two methods,which verify the feasibility and effectiveness of the GO-TSM assignment method.Finally,based on the ideas of "generic making" in the cloud and group wisdom task execution mechanism,this thesis designs and implements the crowd sourcing cloud making module in the platform of innovation,creation and entrepreneurship which we also called Sanchuang platform,and applies the GO-TSM assignment method to the task dispatcher of the crowd sourcing cloud making module using LINGO's Java API.The method provides an effective reference scheme for bidding and selection in the crowd sourcing cloud making module in the Sanchuang platform,and provides the tender side with effective decision support.