A task-oriented approach to select experts for aerospace Cops projects

The expert selection is an important decision problem in the research and development process of complex product systems (CoPS) projects and suitable experts will facilitate the successful task achievement. Existing methods for the expert selection are mostly based on the individual performance, whereas the task characteristics of CoPS projects and the knowledge correlationship between candidates and CoPS projects are seldom considered. This paper generates a task-oriented method to select experts for CoPS projects in the background of aerospace engineering management. A fuzzy multi-stage approach for the knowledge correlationship measurement is proposed and furthermore an illustrative example is employed for the potential application of this method.

[1]  Ling Xia,et al.  The interplay between interpersonal and electronic resources in knowledge seeking among co-located and distributed employees , 2011, J. Assoc. Inf. Sci. Technol..

[2]  Y. Connie Yuan,et al.  Expertise Directory Development, Shared Task Interdependence, and Strength of Communication Network Ties as Multilevel Predictors of Expertise Exchange in Transactive Memory Work Groups , 2010, Commun. Res..

[3]  Carol Tenopir,et al.  NASA/DoD Aerospace Knowledge Diffusion Research Project: Chronology of Selected Literature, Reports, Policy Instruments, and Significant Events Affecting Federal Scientific and Technical Information (STI) in the United States, 1945-1990. Report Number 11. , 1991 .

[4]  Wen-Xiu Zhang,et al.  Theory of including degrees and its applications to uncertainty inferences , 1996, Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium.

[5]  E. Hippel Sticky Information and the Locus of Problem Solving: Implications for Innovation , 1994 .

[6]  Karen Lee Hansen,et al.  Hotspots in complex product systems: emerging issues in innovation management , 1998 .

[7]  Jeff A. Estefan,et al.  of Model-Based Systems Engineering ( MBSE ) Methodologies , 2008 .

[8]  Yuh-Jen Chen,et al.  Development of a method for ontology-based empirical knowledge representation and reasoning , 2010, Decis. Support Syst..

[9]  Morten Hertzum,et al.  Expertise seeking: A review , 2014, Inf. Process. Manag..

[10]  T. Abma Situated Learning in Communities of Practice , 2007 .

[11]  Li Yang,et al.  Who will you ask? An empirical study of interpersonal task information seeking , 2006, J. Assoc. Inf. Sci. Technol..

[12]  Kiyoshi Itoh,et al.  Multi-Objective Optimization for Software Development Projects , 2010 .

[13]  Wen-Xiu Zhang,et al.  A knowledge processing method for intelligent systems based on inclusion degree , 2003, Expert Syst. J. Knowl. Eng..

[14]  Naresh Kumar Agarwal,et al.  A context-based investigation into source use by information seekers , 2011, J. Assoc. Inf. Sci. Technol..

[15]  M. Hobday Product complexity, innovation and industrial organisation , 1998 .

[16]  T. E. Pinelli,et al.  NASA/DoD aerospace knowledge diffusion research project , 1996 .

[17]  Jing Zhao,et al.  The framework and key technologies of an experts selection dynamic management system with application in electronic components verification , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[18]  Andrea B. Hollingshead,et al.  Transactive Memory Systems in Organizations: Matching Tasks, Expertise, and People , 2004, Organ. Sci..