Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A fast taboo search algorithm for the job shop problem
Management Science
ACO algorithms for the quadratic assignment problem
New ideas in optimization
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Computational Optimization and Applications
A genetic algorithm with a mixed region search for the asymmetric traveling salesman problem
Computers and Operations Research
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Optimization of logistic systems using fuzzy weighted aggregation
Fuzzy Sets and Systems
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Soft computing optimization methods applied to logistic processes
International Journal of Approximate Reasoning
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Two cooperative ant colonies for feature selection using fuzzy models
Expert Systems with Applications: An International Journal
Review article: A review of soft computing applications in supply chain management
Applied Soft Computing
ACO-GA approach to paper-reviewer assignment problem in CMS
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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This paper presents a comparative study of genetic algorithms (GA) and ant colony optimization (ACO) applied the online re-optimization of a logistic scheduling problem. This study starts with a literature review of the GA and ACO performance for different benchmark problems. Then, the algorithms are compared on two simulation scenarios: a static and a dynamic environment, where orders are canceled during the scheduling process. In a static optimization environment, both methods perform equally well, but the GA are faster. However, in a dynamic optimization environment, the GA cannot cope with the disturbances unless they re-optimize the whole problem again. On the contrary, the ant colonies are able to find new optimization solutions without re-optimizing the problem, through the inspection of the pheromone matrix. Thus, it can be concluded that the extra time required by the ACO during the optimization process provides information that can be useful to deal with disturbances.