Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Future Generation Computer Systems
Manufacturing cell formation using a new self-organizing neural network
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Ant colony system with communication strategies
Information Sciences—Informatics and Computer Science: An International Journal
An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups
Computers and Operations Research
Exchange strategies for multiple Ant Colony System
Information Sciences: an International Journal
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A hybrid heuristic for the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers and Industrial Engineering
| Hi-index | 0.01 |
In this work, one-piece flow production system is designed with the purpose of ensuring just-in-time production. Three approaches are applied to achieve the goal: adopting straightforward schedule policies, relaxing the Takt time and decreasing the risk of machine failures and operator mistakes. Consequently, a multi-objective design model is proposed, whose aim is to minimize cycle time, changeover count, cell load variation and the number of cells and maximize the extent to which items are completed in a cell. The fuzzy ant colony optimization (FACO) is also presented to solve the formulated problem. In FACO, the fuzzy logic controller (FLC) is used to adapt the evaporated and deposited value of pheromone trail based on the ant's fitness and pheromone trail age. Furthermore, domain knowledge of facility layout, generated based on the travel chart method, is also adaptively injected to improve the performance of FACO. The proposed method is evaluated with the real-world data and experimental results demonstrate that our method outperforms many other methods in efficiency, solution quality and facilitation measures.