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Optimizing real-time vehicle sequencing of a paint shop conveyor system

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  • Elahi, Mirza M. Lutfe
  • Rajpurohit, Karthik
  • Rosenberger, Jay M.
  • Zaruba, Gergely
  • Priest, John

Abstract

A discrete event simulation model and a decision optimizer that were developed for a General Motors paint shop conveyor system are presented. The simulation model interacts with the decision optimizer at four critical points in the system, trying to regroup batches of different colored vehicles. The decision optimizer employs dynamic programming and integer programming to optimize vehicle routing policies. Simulation results of the current decision making policies are compared with those of the proposed optimized policies showing that the number of paint head changes can be significantly reduced resulting in substantial savings on paint head cleaners and paint.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jomega:v:55:y:2015:i:c:p:61-72
    DOI: 10.1016/j.omega.2015.02.003
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    Cited by:

    1. Taube, F. & Minner, S., 2018. "Resequencing mixed-model assembly lines with restoration to customer orders," Omega, Elsevier, vol. 78(C), pages 99-111.
    2. Troilo, Michael & Bouchet, Adrien & Urban, Timothy L. & Sutton, William A., 2016. "Perception, reality, and the adoption of business analytics: Evidence from North American professional sport organizations," Omega, Elsevier, vol. 59(PA), pages 72-83.

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