IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i23p16342-d1288853.html
   My bibliography  Save this article

Truck-Drone Pickup and Delivery Problem with Drone Weight-Related Cost

Author

Listed:
  • Yang Xia

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Tingying Wu

    (Anhui Province Key Laboratory of Contemporary Logistics and Supply Chain (AKL-CLaS), International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230026, China)

  • Beixin Xia

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Junkang Zhang

    (School of Management, Shanghai University, Shanghai 200444, China)

Abstract

Truck-drone delivery is widely used in logistics distribution for achieving sustainable development, in which drone weight greatly affects transportation cost. Thus, we consider a new combined truck-drone pickup and delivery problem with drone weight-related cost in the context of last-mile logistics. A system of integer programming is formulated with the objective of minimizing the total cost of the drone weight-related cost, fixed vehicle cost and travel distance cost. An improved adaptive large neighborhood search algorithm (IALNS) is designed based on the characteristics of the problem, several effective destroy and repair operators are designed to explore the solution space, and a simulated annealing strategy is introduced to avoid falling into the local optimal solution. To evaluate the performance of the IALNS algorithm, 72 instances are randomly generated and tested. The computational results on small instances show that the proposed IALNS algorithm performs better than CPLEX both in efficiency and effectiveness. When comparing the truck-drone pickup and delivery problem with drone weight-related cost to the problem without drone weight-related cost, it is found that ignoring the drone weight constraints leads to an underestimate of the total travel cost by 12.61% based on the test of large instances.

Suggested Citation

  • Yang Xia & Tingying Wu & Beixin Xia & Junkang Zhang, 2023. "Truck-Drone Pickup and Delivery Problem with Drone Weight-Related Cost," Sustainability, MDPI, vol. 15(23), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16342-:d:1288853
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/23/16342/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/23/16342/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Goeke, Dominik, 2019. "Granular tabu search for the pickup and delivery problem with time windows and electric vehicles," European Journal of Operational Research, Elsevier, vol. 278(3), pages 821-836.
    2. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    3. Yuxin Liu & Zihang Qin & Jin Liu, 2023. "An Improved Genetic Algorithm for the Granularity-Based Split Vehicle Routing Problem with Simultaneous Delivery and Pickup," Mathematics, MDPI, vol. 11(15), pages 1-15, July.
    4. Lurkin, Virginie & Schyns, Michaël, 2015. "The Airline Container Loading Problem with pickup and delivery," European Journal of Operational Research, Elsevier, vol. 244(3), pages 955-965.
    5. Ali Mehsin Alyasiry & Michael Forbes & Michael Bulmer, 2019. "An Exact Algorithm for the Pickup and Delivery Problem with Time Windows and Last-in-First-out Loading," Transportation Science, INFORMS, vol. 53(6), pages 1695-1705, November.
    6. Jun Zhang & Jiafu Tang & Richard Y. K. Fung, 2011. "A Scatter Search For Multi-Depot Vehicle Routing Problem With Weight-Related Cost," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 28(03), pages 323-348.
    7. Naccache, Salma & Côté, Jean-François & Coelho, Leandro C., 2018. "The multi-pickup and delivery problem with time windows," European Journal of Operational Research, Elsevier, vol. 269(1), pages 353-362.
    8. 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.
    9. Luo, Zhixing & Qin, Hu & Zhang, Dezhi & Lim, Andrew, 2016. "Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 85(C), pages 69-89.
    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. Guo, Jiaqi & Long, Jiancheng & Xu, Xiaoming & Yu, Miao & Yuan, Kai, 2022. "The vehicle routing problem of intercity ride-sharing between two cities," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 113-139.
    2. Si, Jinhua & He, Fang & Lin, Xi & Tang, Xindi, 2024. "Vehicle dispatching and routing of on-demand intercity ride-pooling services: A multi-agent hierarchical reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    3. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).
    4. Li, Hongqi & Wang, Haotian & Chen, Jun & Bai, Ming, 2020. "Two-echelon vehicle routing problem with time windows and mobile satellites," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 179-201.
    5. Goeke, Dominik & Schneider, Michael, 2015. "Routing a mixed fleet of electric and conventional vehicles," European Journal of Operational Research, Elsevier, vol. 245(1), pages 81-99.
    6. Schulz, Arne & Pfeiffer, Christian, 2024. "Using fixed paths to improve branch-and-cut algorithms for precedence-constrained routing problems," European Journal of Operational Research, Elsevier, vol. 312(2), pages 456-472.
    7. Hammami, Farouk & Rekik, Monia & Coelho, Leandro C., 2019. "Exact and heuristic solution approaches for the bid construction problem in transportation procurement auctions with a heterogeneous fleet," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 150-177.
    8. Yan, Rui & Zhu, Xiaoping & Zhu, Xiaoning & Peng, Rui, 2022. "Optimal routes and aborting strategies of trucks and drones under random attacks," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Liu, Yiming & Roberto, Baldacci & Zhou, Jianwen & Yu, Yang & Zhang, Yu & Sun, Wei, 2023. "Efficient feasibility checks and an adaptive large neighborhood search algorithm for the time-dependent green vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 310(1), pages 133-155.
    10. Jie Xiong & Biao Chen & Xiangnan Li & Zhengbing He & Yanyan Chen, 2020. "Demand Responsive Service-based Optimization on Flexible Routes and Departure Time of Community Shuttles," Sustainability, MDPI, vol. 12(3), pages 1-20, January.
    11. Bombelli, Alessandro & Fazi, Stefano, 2022. "The ground handler dock capacitated pickup and delivery problem with time windows: A collaborative framework for air cargo operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    12. Wang, Yuan & Lei, Linfei & Zhang, Dongxiang & Lee, Loo Hay, 2020. "Towards delivery-as-a-service: Effective neighborhood search strategies for integrated delivery optimization of E-commerce and static O2O parcels," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 38-63.
    13. Ali, Ousmane & Côté, Jean-François & Coelho, Leandro C., 2021. "Models and algorithms for the delivery and installation routing problem," European Journal of Operational Research, Elsevier, vol. 291(1), pages 162-177.
    14. Su, Yue & Dupin, Nicolas & Puchinger, Jakob, 2023. "A deterministic annealing local search for the electric autonomous dial-a-ride problem," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1091-1111.
    15. Côté, Jean-François & Alves de Queiroz, Thiago & Gallesi, Francesco & Iori, Manuel, 2023. "A branch-and-regret algorithm for the same-day delivery problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    16. Zhu, Lin & Sheu, Jiuh-Biing, 2018. "Failure-specific cooperative recourse strategy for simultaneous pickup and delivery problem with stochastic demands," European Journal of Operational Research, Elsevier, vol. 271(3), pages 896-912.
    17. Goeke, D. & Schneider, M., 2015. "Routing a Mixed Fleet of Electric and Conventional Vehicles," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65939, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    18. Wang, Yiran & Chen, Jingxu & Tang, Tianli & Liu, Zhiyuan, 2024. "A holistic approach to multi-depot electric bus scheduling for energy saving considering limitations in charging facilities," Energy, Elsevier, vol. 303(C).
    19. Turkeš, Renata & Sörensen, Kenneth & Hvattum, Lars Magnus, 2021. "Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search," European Journal of Operational Research, Elsevier, vol. 292(2), pages 423-442.
    20. Jiang, Jie & Dai, Ying & Yang, Fei & Ma, Zujun, 2024. "A multi-visit flexible-docking vehicle routing problem with drones for simultaneous pickup and delivery services," European Journal of Operational Research, Elsevier, vol. 312(1), pages 125-137.

    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:jsusta:v:15:y:2023:i:23:p:16342-:d:1288853. 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.