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A Self-adapting Immigrational Genetic Algorithm for Solving a Real-Life Application of Vehicle Routing Problem

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Computational Statistics and Mathematical Modeling Methods in Intelligent Systems (CoMeSySo 2019 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1047))

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Abstract

The present article presents the research for optimizing a real-life instance of heterogeneous vehicle routing problem, used intensively by shipping companies. The experiments have been carried out on the data provided by real companies, with constrains on the number and capacity of the vehicles, minimum and maximum number of stops for each route, along with the margins which can be take into account when optimizing the load of each truck. The optimization is performed using genetic algorithms hybridized with techniques for avoiding local optima, such as self adaptation and immigration. It turns out that more sophisticate approaches perform better, with very little compromise on execution time. It is a new proof of the importance of immigration techniques in bringing diversity in the genetic population.1.

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Authors and Affiliations

  1. Technical University of Cluj-Napoca, North University Center of Baia Mare, Baia Mare, Romania

    Adrian Petrovan, Rudolf Erdei, Petrica Pop-Sitar & Oliviu Matei

Authors
  1. Adrian Petrovan
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  2. Rudolf Erdei
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  3. Petrica Pop-Sitar
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  4. Oliviu Matei
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Corresponding author

Correspondence to Adrian Petrovan .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Zdenka Prokopova

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Petrovan, A., Erdei, R., Pop-Sitar, P., Matei, O. (2019). A Self-adapting Immigrational Genetic Algorithm for Solving a Real-Life Application of Vehicle Routing Problem. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational Statistics and Mathematical Modeling Methods in Intelligent Systems. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-31362-3_15

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