Emerging organizational structure for knowledge-oriented teamwork using genetic algorithm

Organizations have historically sought efficiency improvements through different combinations of materials, components, production and processes to get better performance. However, in this age of the knowledge economy, the new organizational management has shifted its focus to the proper use of the knowledge of employees to create greater output and performance. There is a recent trend towards flat organizations and team-orientated structures, therefore this study will concentrate on the knowledge-oriented teamwork. To construct the fitting team structure, we solve the problem in two stages. In the first stage, we assign the proper tasks to the proper members to achieve a good match for effective usage of organizational knowledge. In the second stage, we solve the problem of insufficient knowledge within the organizational structure generated in the first stage by adjusting the positions of members to improve the mutual coordination and knowledge sharing and support. We applied a basic genetic algorithm (BGA) to solve the problems in both the stages. Five factors, such as member/task number, the number of knowledge types, the number of task types, the average complexity of each member's knowledge types and the average complexity of task knowledge types, are considered to generate different types of problems. Computational results show that the BGA is able to find optimal knowledge matching for small-sized problems in the first stage, and that the BGA is able to improve the organizational structure generated in the first stage in order to reduce the communication cost of knowledge support among the members in the second stage. (C) 2009 Elsevier Ltd. All rights reserved.