IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v13y2021i12p308-d691138.html
   My bibliography  Save this article

Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path

Author

Listed:
  • Abdul Quadir Md

    (Vellore Institute of Technology (VIT), School of Computer Science and Engineering, Chennai 632002, India)

  • Divyank Agrawal

    (Vellore Institute of Technology (VIT), School of Computer Science and Engineering, Chennai 632002, India)

  • Monark Mehta

    (Vellore Institute of Technology (VIT), School of Computer Science and Engineering, Chennai 632002, India)

  • Arun Kumar Sivaraman

    (Vellore Institute of Technology (VIT), School of Computer Science and Engineering, Chennai 632002, India)

  • Kong Fah Tee

    (School of Engineering, University of Greenwich, Kent ME4 4TB, UK)

Abstract

With the pandemic gripping the entire humanity and with uncertainty hovering like a black cloud over all our future sustainability and growth, it became almost apparent that though the development and advancement are at their peak, we are still not ready for the worst. New and better solutions need to be applied so that we will be capable of fighting these conditions. One such prospect is delivery, where everything has to be changed, and each parcel, which was passed people to people, department to department, has to be made contactless throughout with as little error as possible. Thus, the prospect of drone delivery and its importance came around with optimization of the existing system for making it useful in the prospects of delivery of important items like medicines, vaccines, etc. These modular AI-guided drones are faster, efficient, less expensive, and less power-consuming than the actual delivery.

Suggested Citation

  • Abdul Quadir Md & Divyank Agrawal & Monark Mehta & Arun Kumar Sivaraman & Kong Fah Tee, 2021. "Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path," Future Internet, MDPI, vol. 13(12), pages 1-14, November.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:12:p:308-:d:691138
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/13/12/308/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/13/12/308/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Monika Kosacka-Olejnik & Mariusz Kostrzewski & Magdalena Marczewska & Bogna Mrówczyńska & Paweł Pawlewski, 2021. "How Digital Twin Concept Supports Internal Transport Systems?—Literature Review," Energies, MDPI, vol. 14(16), pages 1-37, August.
    2. 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.
    3. Laporte, Gilbert, 1992. "The vehicle routing problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(3), pages 345-358, June.
    4. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
    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. 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.
    2. Vis, Iris F.A., 2006. "Survey of research in the design and control of automated guided vehicle systems," European Journal of Operational Research, Elsevier, vol. 170(3), pages 677-709, May.
    3. Kritikos, Manolis N. & Ioannou, George, 2010. "The balanced cargo vehicle routing problem with time windows," International Journal of Production Economics, Elsevier, vol. 123(1), pages 42-51, January.
    4. Taş, D. & Gendreau, M. & Dellaert, N. & van Woensel, T. & de Kok, A.G., 2014. "Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach," European Journal of Operational Research, Elsevier, vol. 236(3), pages 789-799.
    5. Bhusiri, Narath & Qureshi, Ali Gul & Taniguchi, Eiichi, 2014. "The trade-off between fixed vehicle costs and time-dependent arrival penalties in a routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 1-22.
    6. 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.
    7. Alix Vargas & Carmen Fuster & David Corne, 2020. "Towards Sustainable Collaborative Logistics Using Specialist Planning Algorithms and a Gain-Sharing Business Model: A UK Case Study," Sustainability, MDPI, vol. 12(16), pages 1-29, August.
    8. 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).
    9. Dillmann, Roland & Becker, Burkhard & Beckefeld, Volker, 1996. "Practical aspects of route planning for magazine and newspaper wholesalers," European Journal of Operational Research, Elsevier, vol. 90(1), pages 1-12, April.
    10. S. Irnich, 2008. "A Unified Modeling and Solution Framework for Vehicle Routing and Local Search-Based Metaheuristics," INFORMS Journal on Computing, INFORMS, vol. 20(2), pages 270-287, May.
    11. Said Dabia & Stefan Ropke & Tom van Woensel, 2019. "Cover Inequalities for a Vehicle Routing Problem with Time Windows and Shifts," Transportation Science, INFORMS, vol. 53(5), pages 1354-1371, September.
    12. 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.
    13. Kai Gutenschwager & Christian Niklaus & Stefan Voß, 2004. "Dispatching of an Electric Monorail System: Applying Metaheuristics to an Online Pickup and Delivery Problem," Transportation Science, INFORMS, vol. 38(4), pages 434-446, November.
    14. Hongsheng Zhong & Randolph W. Hall & Maged Dessouky, 2007. "Territory Planning and Vehicle Dispatching with Driver Learning," Transportation Science, INFORMS, vol. 41(1), pages 74-89, February.
    15. Sepehr Nemati & Oleg V. Shylo & Oleg A. Prokopyev & Andrew J. Schaefer, 2016. "The Surgical Patient Routing Problem: A Central Planner Approach," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 657-673, November.
    16. Chardy, Matthieu & Klopfenstein, Olivier, 2012. "Handling uncertainties in vehicle routing problems through data preprocessing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 667-683.
    17. Peng, Fan & Ouyang, Yanfeng, 2012. "Track maintenance production team scheduling in railroad networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1474-1488.
    18. Phan Nguyen Ky Phuc & Nguyen Le Phuong Thao, 2021. "Ant Colony Optimization for Multiple Pickup and Multiple Delivery Vehicle Routing Problem with Time Window and Heterogeneous Fleets," Logistics, MDPI, vol. 5(2), pages 1-13, May.
    19. Nguyen, Minh Anh & Dang, Giang Thi-Huong & Hà, Minh Hoàng & Pham, Minh-Trien, 2022. "The min-cost parallel drone scheduling vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 299(3), pages 910-930.
    20. Said Dabia & David Lai & Daniele Vigo, 2019. "An Exact Algorithm for a Rich Vehicle Routing Problem with Private Fleet and Common Carrier," Transportation Science, INFORMS, vol. 53(4), pages 986-1000, July.

    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:jftint:v:13:y:2021:i:12:p:308-:d:691138. 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.