Genetic algorithms applied to the continuous flow shop problem
Computers and Industrial Engineering
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
The Importance of Organizational Structure for the Adoption of Innovations
Management Science
Assessing the contribution of knowledge to business performance: the KP3 methodology
Decision Support Systems - Special issue: Knowledge management technique
Project Management: A Systems Approach to Planning, Scheduling, and Controlling
Project Management: A Systems Approach to Planning, Scheduling, and Controlling
Optimum coordination of overcurrent relay timing using continuous genetic algorithm
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.