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A Genetic Algorithm to Optimize Dynamics of Supply Chains

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Optimization in Artificial Intelligence and Data Sciences

Part of the book series: AIRO Springer Series ((AIROSS,volume 8))

Abstract

This paper focuses on a model for supply chains, based on partial and ordinary differential equations, that model, respectively, densities of parts on suppliers and queues between consecutive arcs. An optimization approach is discussed via a cost functional that, in consideration of a wished outflow, weights queues of materials by variations of processing velocities for suppliers. The minimization of the cost functional is achieved via a genetic algorithm that, as for the processing velocities, considers mechanisms of selection, crossover and mutation. A simulation example is discussed for the optimization procedure.

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Author information

Authors and Affiliations

  1. Department of Management and Innovation Systems, University of Salerno, Fisciano, Italy

    Luigi Rarità

  2. Department of Science and Technology, University of Sannio, Benevento, Italy

    Luigi Rarità

Authors
  1. Luigi Rarità
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Correspondence to Luigi Rarità .

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Editors and Affiliations

  1. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Lavinia Amorosi

  2. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Paolo Dell’Olmo

  3. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Isabella Lari

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Rarità, L. (2022). A Genetic Algorithm to Optimize Dynamics of Supply Chains. In: Amorosi, L., Dell’Olmo, P., Lari, I. (eds) Optimization in Artificial Intelligence and Data Sciences. AIRO Springer Series, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-030-95380-5_10

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