Binary integer formulation for mixed-model assembly line balancing problem
Computers and Industrial Engineering
Ant algorithms for discrete optimization
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Mixed model assembly line design in a make-to-order environment
Computers and Industrial Engineering
Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment
Applied Soft Computing
2-ANTBAL: An ant colony optimisation algorithm for balancing two-sided assembly lines
Computers and Industrial Engineering
Balancing of mixed-model two-sided assembly lines
Computers and Industrial Engineering
Combining heuristic procedures and simulation models for balancing a PC camera assembly line
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Mixed-model assembly line balancing using a multi-objective ant colony optimization approach
Expert Systems with Applications: An International Journal
A unified view on hybrid metaheuristics
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
This paper presents a new hybrid algorithm, which executes ant colony optimization in combination with genetic algorithm (ACO-GA), for type I mixed-model assembly line balancing problem (MMALBP-I) with some particular features of real world problems such as parallel workstations, zoning constraints and sequence dependent setup times between tasks. The proposed ACO-GA algorithm aims at enhancing the performance of ant colony optimization by incorporating genetic algorithm as a local search strategy for MMALBP-I with setups. In the proposed hybrid algorithm ACO is conducted to provide diversification, while GA is conducted to provide intensification. The proposed algorithm is tested on 20 representatives MMALBP-I extended by adding low, medium and high variability of setup times. The results are compared with pure ACO pure GA and hGA in terms of solution quality and computational times. Computational results indicate that the proposed ACO-GA algorithm has superior performance.