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Combining two pheromone structures for solving the car sequencing problem with Ant Colony Optimization

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  • Solnon, Christine

Abstract

The car sequencing problem involves scheduling cars along an assembly line while satisfying capacity constraints. In this paper, we describe an Ant Colony Optimization (ACO) algorithm for solving this problem, and we introduce two different pheromone structures for this algorithm: the first pheromone structure aims at learning for "good" sequences of cars, whereas the second pheromone structure aims at learning for "critical" cars. We experimentally compare these two pheromone structures, that have complementary performances, and show that their combination allows ants to solve very quickly most instances.

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  • Solnon, Christine, 2008. "Combining two pheromone structures for solving the car sequencing problem with Ant Colony Optimization," European Journal of Operational Research, Elsevier, vol. 191(3), pages 1043-1055, December.
  • Handle: RePEc:eee:ejores:v:191:y:2008:i:3:p:1043-1055
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    1. Terry Jones & Stephanie Forrest, 1995. "Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms," Working Papers 95-02-022, Santa Fe Institute.
    2. 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.
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    Cited by:

    1. Elahi, Mirza M. Lutfe & Rajpurohit, Karthik & Rosenberger, Jay M. & Zaruba, Gergely & Priest, John, 2015. "Optimizing real-time vehicle sequencing of a paint shop conveyor system," Omega, Elsevier, vol. 55(C), pages 61-72.
    2. Parames Chutima & Sathaporn Olarnviwatchai, 0. "A multi-objective car sequencing problem on two-sided assembly lines," Journal of Intelligent Manufacturing, Springer, vol. 0, pages 1-20.

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