The optimized selection strategy of crowdsourcing members in cloud-based design and manufacturing platform

Abstract

Crowdsourcing services and cloud-based design and manufacturing platforms have been combined into an emerging network service model. Due to the diversity and differences of crowdsourcing members in the platform, member optimized selection process is uncertain and discrete, and it is difficult to find the most satisfying solution. In view of this, this article proposed an optimized selection strategy of crowdsourcing members to help users select reasonable crowdsourcing members to form the cooperative team with the most satisfaction. The research steps in this article are as follows. The first step is to analyze the characteristics of crowdsourcing services and crowdsourcing members in the cloud-based design and manufacturing platform. In the second step, according to the characteristics of crowdsourcing members, the evaluation index and target variables of members in the optimized process are proposed, the optimized selection system is established, and the calculation based on each target variable is given. In the third step, the decision-making model is established based on the optimized selection index system. The model decomposes the task-oriented global optimized selection indexes for crowdsourcing members into subtask-oriented local optimized selection indexes, and the gray relational analysis method is used to solve the model. Finally, the effectiveness of the proposed method is verified by taking the crowdsourcing member optimized selection process in the medical product research and development task as an example.