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Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing

Author

Listed:
  • Yang Xia

    (Tsinghua University
    Tsinghua University)

  • Wenjia Zeng

    (Tsinghua University
    Tsinghua University)

  • Xinjie Xing

    (Management School University of Liverpool)

  • Yuanzhu Zhan

    (Birmingham Business School, University of Birmingham)

  • Kim Hua Tan

    (University of Nottingham)

  • Ajay Kumar

    (EMLYON Business School)

Abstract

Alongside the rise of ‘last-mile’ delivery in contemporary urban logistics, drones have demonstrate commercial potential, given their outstanding triple-bottom-line performance. However, as a lithium-ion battery-powered device, drones’ social and environmental merits can be overturned by battery recycling and disposal. To maintain economic performance, yet minimise environmental negatives, fleet sharing is widely applied in the transportation field, with the aim of creating synergies within industry and increasing overall fleet use. However, if a sharing platform’s transparency is doubted, the sharing ability of the platform will be discounted. Known for its transparent and secure merits, blockchain technology provides new opportunities to improve existing sharing solutions. In particular, the decentralised structure and data encryption algorithm offered by blockchain allow every participant equal access to shared resources without undermining security issues. Therefore, this study explores the implementation of a blockchain-enabled fleet sharing solution to optimise drone operations, with consideration of battery wear and disposal effects. Unlike classical vehicle routing with fleet sharing problems, this research is more challenging, with multiple objectives (i.e., shortest path and fewest charging times), and considers different levels of sharing abilities. In this study, we propose a mixed-integer programming model to formulate the intended problem and solve the problem with a tailored branch-and-price algorithm. Through extensive experiments, the computational performance of our proposed solution is first articulated, and then the effectiveness of using blockchain to improve overall optimisation is reflected, and a series of critical influential factors with managerial significance are demonstrated.

Suggested Citation

  • Yang Xia & Wenjia Zeng & Xinjie Xing & Yuanzhu Zhan & Kim Hua Tan & Ajay Kumar, 2023. "Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing," Annals of Operations Research, Springer, vol. 327(1), pages 89-127, August.
    • Handle: RePEc:spr:annopr:v:327:y:2023:i:1:d:10.1007_s10479-021-04459-5
    DOI: 10.1007/s10479-021-04459-5
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    References listed on IDEAS

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    1. Zhen Hong & Zehua Wang & Wei Cai & Victor C. M. Leung, 2017. "Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network," Future Internet, MDPI, vol. 9(4), pages 1-21, November.
    2. Alex Pazaitis & Primavera De Filippi & Vasilis Kostakis, 2017. "Blockchain and Value Systems in the Sharing Economy: The Illustrative Case of Backfeed," The Other Canon Foundation and Tallinn University of Technology Working Papers in Technology Governance and Economic Dynamics 73, TUT Ragnar Nurkse Department of Innovation and Governance.
    3. Gabriella M. Hastig & ManMohan S. Sodhi, 2020. "Blockchain for Supply Chain Traceability: Business Requirements and Critical Success Factors," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 935-954, April.
    4. Villegas, Juan G. & Prins, Christian & Prodhon, Caroline & Medaglia, Andrés L. & Velasco, Nubia, 2013. "A matheuristic for the truck and trailer routing problem," European Journal of Operational Research, Elsevier, vol. 230(2), pages 231-244.
    5. Emilie Danna & Claude Pape, 2005. "Branch-and-Price Heuristics: A Case Study on the Vehicle Routing Problem with Time Windows," Springer Books, in: Guy Desaulniers & Jacques Desrosiers & Marius M. Solomon (ed.), Column Generation, chapter 0, pages 99-129, Springer.
    6. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    7. Gilbert Laporte, 2009. "Fifty Years of Vehicle Routing," Transportation Science, INFORMS, vol. 43(4), pages 408-416, November.
    8. Chowdhury, Sudipta & Emelogu, Adindu & Marufuzzaman, Mohammad & Nurre, Sarah G. & Bian, Linkan, 2017. "Drones for disaster response and relief operations: A continuous approximation model," International Journal of Production Economics, Elsevier, vol. 188(C), pages 167-184.
    9. Zeger Degraeve & Raf Jans, 2007. "A New Dantzig-Wolfe Reformulation and Branch-and-Price Algorithm for the Capacitated Lot-Sizing Problem with Setup Times," Operations Research, INFORMS, vol. 55(5), pages 909-920, October.
    10. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert & Veneroni, Marco, 2017. "Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 158-187.
    11. Jeong, Ho Young & Song, Byung Duk & Lee, Seokcheon, 2019. "Truck-drone hybrid delivery routing: Payload-energy dependency and No-Fly zones," International Journal of Production Economics, Elsevier, vol. 214(C), pages 220-233.
    12. Lahyani, Rahma & Khemakhem, Mahdi & Semet, Frédéric, 2015. "Rich vehicle routing problems: From a taxonomy to a definition," European Journal of Operational Research, Elsevier, vol. 241(1), pages 1-14.
    13. Yong Wang & Qin Li & Xiangyang Guan & Jianxin Fan & Yong Liu & Haizhong Wang, 2020. "Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries," Sustainability, MDPI, vol. 12(15), pages 1-33, July.
    14. Ashish Kabra & Elena Belavina & Karan Girotra, 2020. "Bike-Share Systems: Accessibility and Availability," Management Science, INFORMS, vol. 66(9), pages 3803-3824, September.
    15. Niels Agatz & Paul Bouman & Marie Schmidt, 2018. "Optimization Approaches for the Traveling Salesman Problem with Drone," Transportation Science, INFORMS, vol. 52(4), pages 965-981, August.
    16. Cheng, Chun & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2020. "Drone routing with energy function: Formulation and exact algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 364-387.
    17. Wang, Zheng & Sheu, Jiuh-Biing, 2019. "Vehicle routing problem with drones," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 350-364.
    18. Asma Troudi & Sid-Ali Addouche & Sofiene Dellagi & Abderrahman El Mhamedi, 2018. "Sizing of the Drone Delivery Fleet Considering Energy Autonomy," Sustainability, MDPI, vol. 10(9), pages 1-17, September.
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    Cited by:

    1. Ali Emrouznejad & Soumyadeb Chowdhury & Prasanta Kumar Dey, 2023. "Blockchain in operations and supply Chain Management," Annals of Operations Research, Springer, vol. 327(1), pages 1-6, August.
    2. Dukkanci, Okan & Campbell, James F. & Kara, Bahar Y., 2024. "Facility location decisions for drone delivery: A literature review," European Journal of Operational Research, Elsevier, vol. 316(2), pages 397-418.

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