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
A hybrid grouping genetic algorithm for the cell formation problem
Computers and Operations Research
A simulated annealing algorithm for manufacturing cell formation problems
Expert Systems with Applications: An International Journal
Binary Particle Swarm Optimization with Bit Change Mutation
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Implementing a data mining solution for enhancing carpet manufacturing productivity
Knowledge-Based Systems
Review: Data mining techniques and applications - A decade review from 2000 to 2011
Expert Systems with Applications: An International Journal
Cell formation in group technology using constraint programming and Boolean satisfiability
Expert Systems with Applications: An International Journal
Solving manufacturing cell design problems using constraint programming
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Computers and Operations Research
Hi-index | 12.05 |
In recent years, different metaheuristic methods have been used to solve clustering problems. This paper addresses the problem of manufacturing cell formation using a modified particle swarm optimization (PSO) algorithm. The main modification that this work made to the original PSO algorithm consists in not using the vector of velocities that the standard PSO algorithm does. The proposed algorithm uses the concept of proportional likelihood with modifications, a technique that is used in data mining applications. Some simulation results are presented and compared with results from literature. The criterion used to group the machines into cells is based on the minimization of intercell movements. The computational results show that the PSO algorithm is able to find the optimal solutions in almost all instances, and its use in machine grouping problems is feasible.