Hybridization of PSO and a Discrete Position Update Scheme Techniques for Manufacturing Cell Design

Authors:
Orlando Duran;Nibaldo Rodriguez;Luiz Airton Consalter
Affiliations:
Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile;Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile;Universidade de Passo Fundo, Passo Fundo (RS), Brasil
Venue:
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

Citing 5
Cited 0

A genetic algorithm approach to the machine-component grouping problem with multiple objectives

Computers and Industrial Engineering
A tabu search approach to the cell formation problem

Computers and Industrial Engineering
Cell formation in group technology: review, evaluation and directions for future research

Computers and Industrial Engineering - Cellular manufacturing systems: design, analysis and implementation
A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set

Proceedings of the 8th annual conference on Genetic and evolutionary computation
Binary Particle Swarm Optimization with Bit Change Mutation

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes an hybrid algorithm for Manufacturing Cell Formation. The two techniques that are combined to address this problem correspond to Particle Swarm Optimization (PSO) and a Data Mining Clustering application. The criterion used to group the machines in cells is based on the minimization of inter-cell movements. A maximum cell size is imposed and the number of cell is parameterizable. Some published exact results have been used as benchmarks to assess the proposed algorithm. The computational results show that the proposed algorithm is able to find the optimal solutions on almost all instances with low variability and stability.