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Comprehensive Review of Robotized Freight Packing

Author

Listed:
  • German Pantoja-Benavides

    (School of Engineering, University of los Andes, Bogota 111711, Colombia)

  • Daniel Giraldo

    (School of Engineering, University of los Andes, Bogota 111711, Colombia)

  • Ana Montes

    (School of Engineering, University of los Andes, Bogota 111711, Colombia)

  • Andrea García

    (Integra S.A., Pereira 660003, Colombia)

  • Carlos Rodríguez

    (School of Engineering, University of los Andes, Bogota 111711, Colombia)

  • César Marín

    (Integra S.A., Pereira 660003, Colombia)

  • David Álvarez-Martínez

    (School of Engineering, University of los Andes, Bogota 111711, Colombia)

Abstract

Background : This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods : To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results : Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions : This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems.

Suggested Citation

  • German Pantoja-Benavides & Daniel Giraldo & Ana Montes & Andrea García & Carlos Rodríguez & César Marín & David Álvarez-Martínez, 2024. "Comprehensive Review of Robotized Freight Packing," Logistics, MDPI, vol. 8(3), pages 1-24, July.
  • Handle: RePEc:gam:jlogis:v:8:y:2024:i:3:p:69-:d:1430868
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    References listed on IDEAS

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    1. Wascher, Gerhard & Hau[ss]ner, Heike & Schumann, Holger, 2007. "An improved typology of cutting and packing problems," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1109-1130, December.
    2. Leao, Aline A.S. & Toledo, Franklina M.B. & Oliveira, José Fernando & Carravilla, Maria Antónia & Alvarez-Valdés, Ramón, 2020. "Irregular packing problems: A review of mathematical models," European Journal of Operational Research, Elsevier, vol. 282(3), pages 803-822.
    3. Bortfeldt, Andreas & Wäscher, Gerhard, 2013. "Constraints in container loading – A state-of-the-art review," European Journal of Operational Research, Elsevier, vol. 229(1), pages 1-20.
    4. Silvano Martello & David Pisinger & Daniele Vigo, 2000. "The Three-Dimensional Bin Packing Problem," Operations Research, INFORMS, vol. 48(2), pages 256-267, April.
    5. Dyckhoff, Harald, 1990. "A typology of cutting and packing problems," European Journal of Operational Research, Elsevier, vol. 44(2), pages 145-159, January.
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