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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
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    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. 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.
    3. 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.
    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
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