A Member Selection Model of Collaboration New Product Development Teams Considering Knowledge and Collaboration

Abstract

Member selection to form an effective collaboration new product development (Co-NPD) team is crucial for a successful NPD. Existing researches on member selection mostly focus on the individual attributes of candidates. However, under the background of collaboration, knowledge complementarity and collaboration performance among candidates are important but overlooked. In this paper, we propose a multi-objective optimization model for member selection of a Co-NPD team, considering comprehensively the individual knowledge competence, knowledge complementarity, and collaboration performance. Then, to solve the model, an improved adaptive genetic algorithm (IAGA) is developed. Finally, a real case is provided to illustrate the application of the model, and the IAGA is implemented to select the desired team members for optimal team composition. Additionally, the standard generic algorithm and particle swarm optimization are used to compare with the IAGA to further verify the effectiveness of the IAGA.

Keywords

• member selection
• co-npd team
• individual knowledge performance
• knowledge complementarity
• collaboration performance
• improved adaptive genetic algorithm