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Iterated tabu search for the car sequencing problem

Author

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  • Cordeau, Jean-François
  • Laporte, Gilbert
  • Pasin, Federico

Abstract

This paper introduces an iterated tabu search heuristic for the daily car sequencing problem in which a set of cars must be sequenced so as to satisfy requirements from the paint shop and the assembly line. The iterated tabu search heuristic combines a classical tabu search with perturbation operators that help escape from local optima. The resulting heuristic is flexible, easy to implement, and fast. It has produced very good results on a set of test instances provided by the French car manufacturer Renault.

Suggested Citation

  • Cordeau, Jean-François & Laporte, Gilbert & Pasin, Federico, 2008. "Iterated tabu search for the car sequencing problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 945-956, December.
    • Handle: RePEc:eee:ejores:v:191:y:2008:i:3:p:945-956
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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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    Cited by:

    1. A.C. Mahasinghe & L.A. Sarathchandra, 2020. "An Optimization Model for Production Planning in the Synthetic Fertilizer Industry," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(3), pages 28-62, September.
    2. Florian Jaehn & Sergey Kovalev & Mikhail Y. Kovalyov & Erwin Pesch, 2014. "Multiproduct batching and scheduling with buffered rework: The case of a car paint shop," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(6), pages 458-471, September.
    3. Parames Chutima & Sathaporn Olarnviwatchai, 2018. "A multi-objective car sequencing problem on two-sided assembly lines," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1617-1636, October.
    4. Boysen, Nils & Scholl, Armin & Wopperer, Nico, 2012. "Resequencing of mixed-model assembly lines: Survey and research agenda," European Journal of Operational Research, Elsevier, vol. 216(3), pages 594-604.
    5. Iwona Paprocka & Damian Krenczyk, 2023. "On Energy Consumption and Productivity in a Mixed-Model Assembly Line Sequencing Problem," Energies, MDPI, vol. 16(20), pages 1-19, October.
    6. A.C. Mahasinghe & L.A. Sarathchandra, 2020. "An Optimization Model for Production Planning in the Synthetic Fertilizer Industry," International Association of Decision Sciences, Asia University, Taiwan, vol. 24(3), pages 28-62, September.
    7. 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.

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