IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i16p2899-d886897.html
   My bibliography  Save this article

A Path Planning Model for Stock Inventory Using a Drone

Author

Listed:
  • László Radácsi

    (Faculty of Finance and Accountancy, Budapest Business School, 1149 Budapest, Hungary)

  • Miklós Gubán

    (Faculty of Finance and Accountancy, Budapest Business School, 1149 Budapest, Hungary)

  • László Szabó

    (Faculty of Finance and Accountancy, Budapest Business School, 1149 Budapest, Hungary)

  • József Udvaros

    (Faculty of Finance and Accountancy, Budapest Business School, 1149 Budapest, Hungary)

Abstract

In this study, a model and solution are shown for controlling the inventory of a logistics warehouse in which neither satellite positioning nor IoT solutions can be used. Following a review of the literature on path planning, a model is put forward using a drone that can be moved in all directions and is suitable for imaging and transmission. The proposed model involves three steps. In the first step, a traversal path definition provides an optimal solution, which is pre-processing. This is in line with the structure and capabilities of the warehouse. In the second step, the pre-processed path determines the real-time movement of the drone during processing, including camera movements and image capture. The third step is post-processing, i.e., the processing of images for QR code identification, the interpretation of the QR code, and the examination of matches and discrepancies for inventory control. A key benefit for the users of this model is that the result can be achieved without any external orientation tools, relying solely on its own movement and the organization of a pre-planned route. The proposed model can be effective not only for inventory control, but also for exploring the structure of a warehouse shelving system and determining empty cells.

Suggested Citation

  • László Radácsi & Miklós Gubán & László Szabó & József Udvaros, 2022. "A Path Planning Model for Stock Inventory Using a Drone," Mathematics, MDPI, vol. 10(16), pages 1-19, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2899-:d:886897
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/16/2899/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/16/2899/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David Pisinger & Stefan Ropke, 2019. "Large Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 99-127, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chuanyue Wang & Lei Zhang & Yifan Gao & Xiaoyuan Zheng & Qianling Wang, 2023. "A Cooperative Game Hybrid Optimization Algorithm Applied to UAV Inspection Path Planning in Urban Pipe Corridors," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    2. Zsolt Tibor Kosztyán & Zoltán Kovács, 2023. "Preface to the Special Issue on “Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling”," Mathematics, MDPI, vol. 11(1), pages 1-3, January.

    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. El Mehdi, Er Raqabi & Ilyas, Himmich & Nizar, El Hachemi & Issmaïl, El Hallaoui & François, Soumis, 2023. "Incremental LNS framework for integrated production, inventory, and vessel scheduling: Application to a global supply chain," Omega, Elsevier, vol. 116(C).
    2. Liu, Baoli & Li, Zhi-Chun & Sheng, Dian & Wang, Yadong, 2021. "Integrated planning of berth allocation and vessel sequencing in a seaport with one-way navigation channel," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 23-47.
    3. Baals, Julian & Emde, Simon & Turkensteen, Marcel, 2023. "Minimizing earliness-tardiness costs in supplier networks—A just-in-time truck routing problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 707-741.
    4. Yu, Shaohua & Puchinger, Jakob & Sun, Shudong, 2024. "Electric van-based robot deliveries with en-route charging," European Journal of Operational Research, Elsevier, vol. 317(3), pages 806-826.
    5. Kallestad, Jakob & Hasibi, Ramin & Hemmati, Ahmad & Sörensen, Kenneth, 2023. "A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 309(1), pages 446-468.
    6. Karl-Arne Johannessen & Hans Comtet & Erik Fosse, 2021. "A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory," IJERPH, MDPI, vol. 18(9), pages 1-19, April.
    7. Dumez, Dorian & Lehuédé, Fabien & Péton, Olivier, 2021. "A large neighborhood search approach to the vehicle routing problem with delivery options," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 103-132.
    8. Singh, Nitish & Dang, Quang-Vinh & Akcay, Alp & Adan, Ivo & Martagan, Tugce, 2022. "A matheuristic for AGV scheduling with battery constraints," European Journal of Operational Research, Elsevier, vol. 298(3), pages 855-873.
    9. Zhang, Guowei & Zhu, Ning & Ma, Shoufeng & Xia, Jun, 2021. "Humanitarian relief network assessment using collaborative truck-and-drone system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    10. Wusheng Liu & Wang Li & Qing Zhou & Qian Die & Yan Yang, 2022. "The optimization of the "UAV-vehicle" joint delivery route considering mountainous cities," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-21, March.
    11. Garside, Annisa Kesy & Ahmad, Robiah & Muhtazaruddin, Mohd Nabil Bin, 2024. "A recent review of solution approaches for green vehicle routing problem and its variants," Operations Research Perspectives, Elsevier, vol. 12(C).
    12. LIAN, Ying & LUCAS, Flavien & SÖRENSEN, Kenneth, 2022. "The electric on-demand bus routing problem with partial charging and nonlinear functions," Working Papers 2022005, University of Antwerp, Faculty of Business and Economics.
    13. Frey, Christian M.M. & Jungwirth, Alexander & Frey, Markus & Kolisch, Rainer, 2023. "The vehicle routing problem with time windows and flexible delivery locations," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1142-1159.
    14. Canca, David & De-Los-Santos, Alicia & Laporte, Gilbert & Mesa, Juan A., 2019. "Integrated Railway Rapid Transit Network Design and Line Planning problem with maximum profit," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 1-30.
    15. Moshref-Javadi, Mohammad & Lee, Seokcheon & Winkenbach, Matthias, 2020. "Design and evaluation of a multi-trip delivery model with truck and drones," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    16. Yu, Vincent F. & Anh, Pham Tuan & Baldacci, Roberto, 2023. "A robust optimization approach for the vehicle routing problem with cross-docking under demand uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    17. Perumal, S.S.G. & Dollevoet, T.A.B. & Huisman, D. & Lusby, R.M. & Larsen, J. & Riis, M., 2020. "Solution Approaches for Vehicle and Crew Scheduling with Electric Buses," Econometric Institute Research Papers EI-2020-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Chen, Cheng & Demir, Emrah & Huang, Yuan & Qiu, Rongzu, 2021. "The adoption of self-driving delivery robots in last mile logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    19. Christos Orlis & Nicola Bianchessi & Roberto Roberti & Wout Dullaert, 2020. "The Team Orienteering Problem with Overlaps: An Application in Cash Logistics," Transportation Science, INFORMS, vol. 54(2), pages 470-487, March.
    20. Mohammadmehdi Hakimifar & Vera C. Hemmelmayr & Fabien Tricoire, 2022. "A Bi-Objective Field-Visit Planning Problem for Rapid Needs Assessment under Travel-Time Uncertainty," Sustainability, MDPI, vol. 14(5), pages 1-16, March.

    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:jmathe:v:10:y:2022:i:16:p:2899-:d:886897. 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.