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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

Author

Listed:
  • Gianluca Nastasi

    (Scuola Superiore Sant’Anna)

  • Valentina Colla

    (Scuola Superiore Sant’Anna)

  • Silvia Cateni

    (Scuola Superiore Sant’Anna)

  • Simone Campigli

    (Roboris s.r.l.)

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.

Suggested Citation

  • Gianluca Nastasi & Valentina Colla & Silvia Cateni & Simone Campigli, 2018. "Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1545-1557, October.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:7:d:10.1007_s10845-016-1198-x
    DOI: 10.1007/s10845-016-1198-x
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    References listed on IDEAS

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    1. Gu, Jinxiang & Goetschalckx, Marc & McGinnis, Leon F., 2007. "Research on warehouse operation: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 177(1), pages 1-21, February.
    2. Berg, J. P. van den & Zijm, W. H. M., 1999. "Models for warehouse management: Classification and examples," International Journal of Production Economics, Elsevier, vol. 59(1-3), pages 519-528, March.
    3. Han, Bin & Zhang, Wenjun & Lu, Xiwen & Lin, Yingzi, 2015. "On-line supply chain scheduling for single-machine and parallel-machine configurations with a single customer: Minimizing the makespan and delivery cost," European Journal of Operational Research, Elsevier, vol. 244(3), pages 704-714.
    4. Roodbergen, Kees Jan & Vis, Iris F.A., 2009. "A survey of literature on automated storage and retrieval systems," European Journal of Operational Research, Elsevier, vol. 194(2), pages 343-362, April.
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    Cited by:

    1. Emrah B. Edis & Ozlem Uzun Araz & Ozgur Eski & Rahime Sancar Edis, 2023. "Storage location assignment of steel coils in a manufacturing company: an integer linear programming model and a greedy randomized adaptive search procedure," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 67-109, April.
    2. Liao, Shengli & Liu, Huan & Liu, Benxi & Liu, Tian & Li, Chonghao & Su, Huaying, 2023. "Solution framework for short-term cascade hydropower system optimization operations based on the load decomposition strategy," Energy, Elsevier, vol. 277(C).
    3. Jianglong Yang & Li Zhou & Huwei Liu, 2021. "Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-25, April.
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    5. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2021. "Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms," Energies, MDPI, vol. 14(14), pages 1-25, July.

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