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Impact of the pheromone trail on the performance of ACO algorithms for solving the car-sequencing problem

Author

Listed:
  • C Gagné

    (Université du Québec à Chicoutimi)

  • M Gravel

    (Université du Québec à Chicoutimi)

  • S Morin

    (Université du Québec à Chicoutimi)

  • W L Price

    (Université Laval Ste-Foy)

Abstract

This paper compares different ant colony optimization algorithms for solving the NP-hard car-sequencing problem, which is of great practical interest. The five algorithms that are compared are the Ant System (AS), the Elitist AS, the Rank-Based AS, the Max–Min AS and the Ant Colony System. These algorithms, which are well known in the literature, differ in the way in which the pheromone trail is managed. The comparative analysis seeks to identify which algorithm best manages the learning process in solving the car-sequencing problem. Moreover, we propose a new structure for the pheromone trail specifically designed to take advantage of the type of constraints found in the car-sequencing problem. The quality of the results obtained with this new form of learning for three problem sets drawn from the literature is superior to that of the best results published and demonstrates the efficiency of this new trail structure.

Suggested Citation

  • C Gagné & M Gravel & S Morin & W L Price, 2008. "Impact of the pheromone trail on the performance of ACO algorithms for solving the car-sequencing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(8), pages 1077-1090, August.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:8:d:10.1057_palgrave.jors.2602361
    DOI: 10.1057/palgrave.jors.2602361
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    References listed on IDEAS

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    1. B. Bullnheimer & R.F. Hartl & C. Strauss, 1999. "An improved Ant System algorithm for theVehicle Routing Problem," Annals of Operations Research, Springer, vol. 89(0), pages 319-328, January.
    2. L M Gambardella & É D Taillard & M Dorigo, 1999. "Ant colonies for the quadratic assignment problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(2), pages 167-176, February.
    3. Smith, Kate & Palaniswami, M. & Krishnamoorthy, M., 1996. "Traditional heuristic versus Hopfield neural network approaches to a car sequencing problem," European Journal of Operational Research, Elsevier, vol. 93(2), pages 300-316, September.
    4. M Gravel & C Gagné & W L Price, 2005. "Review and comparison of three methods for the solution of the car sequencing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(11), pages 1287-1295, November.
    5. Gagne, Caroline & Gravel, Marc & Price, Wilson L., 2006. "Solving real car sequencing problems with ant colony optimization," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1427-1448, November.
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