IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v8y2024i4p126-d1536126.html
   My bibliography  Save this article

Indoor Positioning Systems in Logistics: A Review

Author

Listed:
  • Laura Vaccari

    (Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy)

  • Antonio Maria Coruzzolo

    (Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy)

  • Francesco Lolli

    (Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy
    Interdepartmental Centre En&Tech, Piazzale Europa, 1, 42124 Reggio Emilia, Italy)

  • Miguel Afonso Sellitto

    (Production and Systems Engineering Graduate Program, Universidade do Vale do Rio dos Sinos, UNISINOS, Av. Unisinos, 950—Cristo Rei, São Leopoldo 93022-000, Brazil)

Abstract

Background: Indoor Positioning Systems (IPS) have gained increasing relevance in logistics, offering solutions for safety enhancement, intralogistics management, and material flow control across various environments such as industrial facilities, offices, hospitals, and supermarkets. This study aims to evaluate IPS technologies’ performance and applicability to guide practitioners in selecting systems suited to specific contexts. Methods: The study systematically reviews key IPS technologies, positioning methods, data types, filtering methods, and hybrid technologies, alongside real-world examples of IPS applications in various testing environments. Results: Our findings reveal that radio-based technologies, such as Radio Frequency Identification (RFID), Ultra-wideband (UWB), Wi-Fi, and Bluetooth (BLE), are the most commonly used, with UWB offering the highest accuracy in industrial settings. Geometric methods, particularly multilateration, proved to be the most effective for positioning and are supported by advanced filtering techniques like the Extended Kalman Filter and machine learning models such as Convolutional Neural Networks. Overall, hybrid approaches that integrate multiple technologies demonstrated enhanced accuracy and reliability, effectively mitigating environmental interferences and signal attenuation. Conclusions: The study provides valuable insights for logistics practitioners, emphasizing the importance of selecting IPS technologies suited to specific operational contexts, where precision and reliability are critical to operational success.

Suggested Citation

  • Laura Vaccari & Antonio Maria Coruzzolo & Francesco Lolli & Miguel Afonso Sellitto, 2024. "Indoor Positioning Systems in Logistics: A Review," Logistics, MDPI, vol. 8(4), pages 1-31, December.
    • Handle: RePEc:gam:jlogis:v:8:y:2024:i:4:p:126-:d:1536126
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/8/4/126/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2305-6290/8/4/126/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dominik Gnaś & Dariusz Majerek & Michał Styła & Przemysław Adamkiewicz & Stanisław Skowron & Monika Sak-Skowron & Olena Ivashko & Józef Stokłosa & Robert Pietrzyk, 2024. "Enhanced Indoor Positioning System Using Ultra-Wideband Technology and Machine Learning Algorithms for Energy-Efficient Warehouse Management," Energies, MDPI, vol. 17(16), pages 1-15, August.
    2. Jaiteg Singh & Noopur Tyagi & Saravjeet Singh & Ahmad Ali AlZubi & Firas Ibrahim AlZubi & Sukhjit Singh Sehra & Farman Ali, 2023. "Enhancing Indoor Navigation in Intelligent Transportation Systems with 3D RIF and Quantum GIS," Sustainability, MDPI, vol. 15(22), pages 1-17, November.
    3. Jinkai Liu & Yanqing Qiu & Kezhao Yin & Wentong Dong & Jiaqing Luo, 2018. "RILS: RFID indoor localization system using mobile readers," International Journal of Distributed Sensor Networks, , vol. 14(4), pages 15501477187, April.
    4. Antonio Maria Coruzzolo & Francesco Lolli & Elia Balugani & Elisa Magnani & Miguel Afonso Sellitto, 2023. "Order Picking Problem: A Model for the Joint Optimisation of Order Batching, Batch Assignment Sequencing, and Picking Routing," Logistics, MDPI, vol. 7(3), pages 1-18, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter Vestenický & Martin Vestenický, 2019. "Optimization of receiving window width of the correlation receiver for radiofrequency identification marker localization," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    2. Claudio Suppini & Natalya Lysova & Michele Bocelli & Federico Solari & Letizia Tebaldi & Andrea Volpi & Roberto Montanari, 2024. "From Single Orders to Batches: A Sensitivity Analysis of Warehouse Picking Efficiency," Sustainability, MDPI, vol. 16(18), pages 1-17, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlogis:v:8:y:2024:i:4:p:126-:d:1536126. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.