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An enhanced grouping genetic algorithm for solving the multifunctional design team formation problem

Lookup NU author(s): Teerawut Tunnukij, Professor Christian Hicks



In the design or product development process, multifunctional design teams (MDTs) are frequently required to work together simultaneously to create a product that satisfies customers and market requirements. The implementation of MDTs has been shown to significantly improve the new product development performance in many companies. However, the methods in the literature are mostly applied to relatively small problems. There has been little research that has developed methods for generating effective MDTs for large complex design projects. Previous research by the authors has developed an Enhanced Grouping Genetic Algorithm (EnGGA) for solving cell formation problems in manufacturing. It was found that the EnGGA performed better than previous methods. This paper presents a two-stage algorithm that contains the EnGGA and a local search heuristic for solving MDT formation problems. The EnGGA was used to form MDTs and groups of tasks assigned to the MDTs simultaneously. A local search heuristic was used to identify engineering liaisons that facilitated information transfer between the MDTs. The two-stage algorithm was tested using randomly generated problems. The quality of the solutions was evaluated in terms of the modified grouping efficacy. The results show that the two-stage algorithm is effective and it is likely to be a promising method for solving MDT formation problems in complex systems. Keywords: Cell Formation, Genetic Algorithms, Design Teams.

Publication metadata

Author(s): Tunnukij T, Hicks C

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: GT/CM Symposium on Group Technology and Cellular Manufacturing

Year of Conference: 2009