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Dynamics in Logistics pp 47–57Cite as

Optimization of a Factory Line Using Multi-Objective Evolutionary Algorithms

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  • First Online:

  • 2002 Accesses

Part of the book series: Lecture Notes in Logistics ((LNLO))

Abstract

In this paper, we describe a simulation for an automotive manufacturing process using automated guided vehicles (AGVs). The simulation is used to optimize a generalized factory model layout using multi-objective evolutionary algorithms. The Pareto front of the optimization is analyzed, and layouts are compared to the industry standard transfer line in terms of objectives that include capital cost, energy usage, and product throughput. We seek to determine from the results whether genetic algorithms are a feasible tool for the optimization of manufacturing automobiles.

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Author information

Authors and Affiliations

  1. Georgia Tech Research Institute, Electro-Optical Systems Lab, Atlanta, 30332, GA, USA

    Andrew Hardin, Jason Zutty, Gisele Bennett & Gregory Rohling

  2. Research and Development Center, General Motors Corp., Warren, 48090, MI, USA

    Ningjian Huang

Authors
  1. Andrew Hardin
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  2. Jason Zutty
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  3. Gisele Bennett
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  4. Ningjian Huang
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  5. Gregory Rohling
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Corresponding author

Correspondence to Jason Zutty .

Editor information

Editors and Affiliations

  1. Lehrstuhl für ABWL und Logistikmanagemen, University of Bremen, Bremen, Germany

    Herbert Kotzab

  2. University of Bremen, Bremen, Germany

    Jürgen Pannek

  3. Planung und Steuerung produktionstechnischer Systeme, Bremer Institut für Produktion und Logistik (BIBA), Bremen, Bremen, Germany

    Klaus-Dieter Thoben

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© 2016 Springer International Publishing Switzerland

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Hardin, A., Zutty, J., Bennett, G., Huang, N., Rohling, G. (2016). Optimization of a Factory Line Using Multi-Objective Evolutionary Algorithms. In: Kotzab, H., Pannek, J., Thoben, KD. (eds) Dynamics in Logistics. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-319-23512-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-23512-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23511-0

  • Online ISBN: 978-3-319-23512-7

  • eBook Packages: EngineeringEngineering (R0)

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