ACO BASED METHOD COMPARATION APPLIED TO FLEET MANAGEMENT PROBLEM

M. Antón-Rodríguez, D. Boto-Giralda, F. J. Díaz Pernas, J. F. Díez Higuera

2006

Abstract

Road Transport enterprises do have the need of fleet management applications in order to upgrade their efficiency; the fulfilment of that need takes us in the search of optimization algorithms whose performance better suits not only the optimal route search problem, but the resource allocation too. ACO (Ant Colony Optimization) meta-heuristic has proven to be very useful when solving similar problems, but as ACO comes in several different flavours, to make the right algorithm choice is the first step in the search for a solution. This document presents a performance study made upon several ACO algorithms over the fleet management problem, with the objective of determining which one is the best finding the optimal solution in a reasonable amount of time.

References

  1. Asmar, D. C., Elshamli, A., Areibi, S. A Comparative Assessment of ACO Algorithms Within a TSP Environment. DCDIS 2005, Guelph, Ontario, Canada. July 2005.
  2. Bonabeau, E., Dorigo, M. and Theraulaz, G. Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, 1999.
  3. Bullnheimer, B., Hartl, R. F., Strauß, C. A new RankBased Version of the Ant System - A Computational Study. Technical Report, Institute of Management Science, University of Vienna, 1997.
  4. Cordón, O., Fernández, I., Herrera, F. and Moreno, L. A New ACO Model Integrating Evolutionary Computation Concepts: The Best-Worst Ant System. From Ant Colonies to Artificial Ants: Second International Workshop on Ant Algorithms (ANTS'2000), pp. 22-29. Brussels (Belgium), 2000.
  5. Corne, D., Dorigo, M. and Glover, F. New Ideas in Optimization, McGraw-Hill. 1999.
  6. Dorigo, M. and Di Caro, G. and Gambardella, L. M. Ant Algorithms for Discrete Optimization. Artificial Life, 5(2), 137-172. 1999.
  7. Dorigo, M. and Gambardella, L.M. Ant colony system: a cooperative learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation,1(1):53-66. 1997.
  8. Dorigo, M. and Stützle, T. Ant Colony Optimization. Massachussets Institute of Technology. 2004.
  9. Perozo Rondón, F. J. Sistema de gestión dinámica de flotas usando localización GPS/GSM y programación evolutiva para la optimización de recursos. Tesis doctoral. University of Valladolid, 2002.
  10. Stützle, T. and Hoss, H. MAX-MIN Ant System. Preprint submitted to Elsevier Science, 5. November 1999.
Download


Paper Citation


in Harvard Style

Antón-Rodríguez M., Boto-Giralda D., J. Díaz Pernas F. and F. Díez Higuera J. (2006). ACO BASED METHOD COMPARATION APPLIED TO FLEET MANAGEMENT PROBLEM . In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-972-8865-60-3, pages 535-539. DOI: 10.5220/0001216105350539


in Bibtex Style

@conference{icinco06,
author={M. Antón-Rodríguez and D. Boto-Giralda and F. J. Díaz Pernas and J. F. Díez Higuera},
title={ACO BASED METHOD COMPARATION APPLIED TO FLEET MANAGEMENT PROBLEM},
booktitle={Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2006},
pages={535-539},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001216105350539},
isbn={978-972-8865-60-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - ACO BASED METHOD COMPARATION APPLIED TO FLEET MANAGEMENT PROBLEM
SN - 978-972-8865-60-3
AU - Antón-Rodríguez M.
AU - Boto-Giralda D.
AU - J. Díaz Pernas F.
AU - F. Díez Higuera J.
PY - 2006
SP - 535
EP - 539
DO - 10.5220/0001216105350539