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Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

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Abstract

The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. Genetic algorithms are applied to this purpose and three different popular algorithms capable to deal with multi-objective optimization are compared. The three algorithms, namely the Niched Pareto Genetic Algorithm, the Non-dominated Sorting Genetic Algorithm 2 and the Strength Pareto Genetic Algorithm 2, are described in details and the achieved results are widely discussed; moreover several statistical tests have been applied in order to evaluate the statistical significance of the obtained results.

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Acknowledgments

The authors wish to gratefully acknowledge ILVA Racconigi Works for providing data and relevant information and for the fruitful discussions, which significantly contributed to the present analysis.

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Authors and Affiliations

  1. Scuola Superiore Sant’Anna, Pisa, Italy

    Gianluca Nastasi, Valentina Colla & Silvia Cateni

  2. Roboris s.r.l., Pisa, Italy

    Simone Campigli

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  1. Gianluca Nastasi
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  2. Valentina Colla
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  3. Silvia Cateni
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  4. Simone Campigli
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Correspondence to Gianluca Nastasi.

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Nastasi, G., Colla, V., Cateni, S. et al. Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse. J Intell Manuf 29, 1545–1557 (2018). https://doi.org/10.1007/s10845-016-1198-x

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