A chaotic Ant Colony Optimization method for scheduling a single batch-processing machine with non-identical job sizes

The problem of minimizing makespan on a single batch-processing machine with non-identical job sizes is strongly NP-hard. This paper proposes an ant colony optimization (ACO) algorithm with chaotic control to solve the problem. The metropolis criterion is adopted to select the paths of ants to escap...

Full description

Saved in:
Bibliographic Details
Published in2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) pp. 40 - 43
Main Authors Ba-Yi Cheng, Hua-Ping Chen, Hao Shao, Rui Xu, Huang, G.Q.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2008
Subjects
Ant colony optimization
Chaos
Convergence
Gallium
Job shop scheduling
Mathematical model
Optimization
Online AccessGet full text

Cover

More Information
Summary:The problem of minimizing makespan on a single batch-processing machine with non-identical job sizes is strongly NP-hard. This paper proposes an ant colony optimization (ACO) algorithm with chaotic control to solve the problem. The metropolis criterion is adopted to select the paths of ants to escape immature convergence. In order to improve the solutions of ACO, a chaotic optimizer is designed and integrated into ACO to reinforce the capacity of global optimization. Batch first fit is introduced to decode the paths into feasible solutions of the problem. In the experiment, the instances of 24 levels are simulated and the results show that the proposed CACO outperforms genetic algorithm and simulated annealing on all the instances.
ISBN:1424418224
9781424418220
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2008.4630773