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

被引：8

作者：

Su, Jiafu ^{[1
,2
]}

Yang, Yu ^{[1
]}

Zhang, Xuefeng ^{[3
]}

机构：

[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China

[2] Penn State Univ, Harold & Inge Marcus Dept Ind & Mfg Engn, State Coll, PA 16802 USA

[3] Anhui Polytech Univ, Sch Management Engn, Wuhu, Peoples R China

来源：

JOURNAL OF INTELLIGENT SYSTEMS | 2018年 / 27卷 / 02期

基金：

美国国家科学基金会;

关键词：

Member selection; Co-NPD team; individual knowledge performance; knowledge complementarity; collaboration performance; improved adaptive genetic algorithm;

D O I：

10.1515/jisys-2016-0078

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：213 / 229

页数：17