A Fuzzy Multi-Objective Linear Programming Model for Manufacturing Cell Formation
Fuzzy mathematical programming (FMP) has been shown not only providing a better and more flexible way of representing the cell formation (CF) problem of cellular manufacturing, but also improving solution quality and computational efficiency. However, FMP cannot meet the demand of real-world applications because it can only be used to solve small-size problems. In this paper, we propose a heuristic genetic algorithm (HGA) as a viable solution for solving large-scale fuzzy multi-objective CF problems. Heuristic crossover and mutation operators are developed to improve computational efficiency. Our results show that the HGA outperforms the FMP and goal programming (GP) models in terms of clustering results, computational time, and user friendliness.
Artificial Intelligence and Robotics
Simulated Evolution and Learning
TSAI, C.C. and Chu, Chao-Hsien.
A Fuzzy Multi-Objective Linear Programming Model for Manufacturing Cell Formation. (2006). Simulated Evolution and Learning. 377-383. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/221