Record breaking optimization results using the ruin and recreate principle
Journal of Computational Physics
Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Automated rip-up and reroute techniques
DAC '82 Proceedings of the 19th Design Automation Conference
An evolutionary algorithm for manufacturing cell formation
Computers and Industrial Engineering
A hybrid grouping genetic algorithm for the cell formation problem
Computers and Operations Research
A general heuristic for vehicle routing problems
Computers and Operations Research
Genetic algorithm approach for solving a cell formation problem in cellular manufacturing
Expert Systems with Applications: An International Journal
IEEE Transactions on Evolutionary Computation
Implementation of cellular genetic algorithms on a CNN chip: Simulations and experimental results
International Journal of Circuit Theory and Applications
Computers and Industrial Engineering
| Hi-index | 12.05 |
We first introduce a local search procedure to solve the cell formation problem where each cell includes at least one machine and one part. The procedure applies sequentially an intensification strategy to improve locally a current solution and a diversification strategy destroying more extensively a current solution to recover a new one. To search more extensively the feasible domain, a hybrid method is specified where the local search procedure is used to improve each offspring solution generated with a steady state genetic algorithm. The numerical results using 35 most widely used benchmark problems indicate that the line search procedure can reduce to 1% the average gap to the best-known solutions of the problems using an average solution time of 0.64s. The hybrid method can reach the best-known solution for 31 of the 35 benchmark problems, and improve the best-known solution of three others, but using more computational effort.