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Optimization of Supply Chain Network using Genetic Algorithms based on Bill of materials

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  • Kallina, Dennis
  • Siegfried, Patrick

Abstract

The integration of genetic algorithms to optimize the networks of value chains could enormously improve the performance of supply chains. For this reason, this paper describes in more detail the application of genetic algorithms in the value chains of the automotive industry. For this purpose, a theoretical model is built up to evaluate whether the application of the model can optimize the value chain. This option is described, analyzed and its restrictions are shown. Instead of looking at the entire network, individual finished goods and their bill of material are used as a basis for optimization, which greatly reduces the complexity of the original problem. The original complexity of the supply chain networks can thus be reduced and considered based on the bill of material.

Suggested Citation

  • Kallina, Dennis & Siegfried, Patrick, 2021. "Optimization of Supply Chain Network using Genetic Algorithms based on Bill of materials," MPRA Paper 111397, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:111397
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    File URL: https://mpra.ub.uni-muenchen.de/111397/1/MPRA_paper_111397.pdf
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    References listed on IDEAS

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    1. Sourirajan, Karthik & Ozsen, Leyla & Uzsoy, Reha, 2009. "A genetic algorithm for a single product network design model with lead time and safety stock considerations," European Journal of Operational Research, Elsevier, vol. 197(2), pages 599-608, September.
    2. Hokey Min, 2015. "Genetic algorithm for supply chain modelling: basic concepts and applications," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 22(2), pages 143-164.
    3. Meixell, Mary J. & Gargeya, Vidyaranya B., 2005. "Global supply chain design: A literature review and critique," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(6), pages 531-550, November.
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    Cited by:

    1. Weng Hoe Lam & Weng Siew Lam & Pei Fun Lee, 2024. "A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility," Mathematics, MDPI, vol. 12(8), pages 1-22, April.

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    More about this item

    Keywords

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    JEL classification:

    • F63 - International Economics - - Economic Impacts of Globalization - - - Economic Development
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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