Optimal Route Planning for Truck–Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
The optimal delivery route problem for truck–drone delivery is defined as a traveling salesman problem with drone (TSP-D), which has been studied in a wide range of previous literature. However, most of the existing studies ignore truck waiting time at rendezvous points. To fill this gap, this paper...
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| Vydáno v: | Applied sciences Ročník 12; číslo 1; s. 529 |
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01.01.2022
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| ISSN: | 2076-3417, 2076-3417 |
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| Abstract | The optimal delivery route problem for truck–drone delivery is defined as a traveling salesman problem with drone (TSP-D), which has been studied in a wide range of previous literature. However, most of the existing studies ignore truck waiting time at rendezvous points. To fill this gap, this paper builds a mixed integer nonlinear programming model subject to time constraints and route constraints, aiming to minimize the total delivery time. Since the TSP-D is non-deterministic polynomial-time hard (NP-hard), the proposed model is solved by the variable neighborhood tabu search algorithm, where the neighborhood structure is changed by point exchange and link exchange to expand the tabu search range. A delivery network with 1 warehouse and 23 customer points are employed as a case study to verify the effectiveness of the model and algorithm. The 23 customer points are visited by three truck–drones. The results indicate that truck–drone delivery can effectively reduce the total delivery time by 20.1% compared with traditional pure-truck delivery. Sensitivity analysis of different parameters shows that increasing the number of truck–drones can effectively save the total delivery time, but gradually reduce the marginal benefits. Only increasing either the truck speed or drone speed can reduce the total delivery time, but not to the greatest extent. Bilateral increase of truck speed and drone speed can minimize the delivery time. It can clearly be seen that the proposed method can effectively optimize the truck–drone delivery route and improve the delivery efficiency. |
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| AbstractList | The optimal delivery route problem for truck–drone delivery is defined as a traveling salesman problem with drone (TSP-D), which has been studied in a wide range of previous literature. However, most of the existing studies ignore truck waiting time at rendezvous points. To fill this gap, this paper builds a mixed integer nonlinear programming model subject to time constraints and route constraints, aiming to minimize the total delivery time. Since the TSP-D is non-deterministic polynomial-time hard (NP-hard), the proposed model is solved by the variable neighborhood tabu search algorithm, where the neighborhood structure is changed by point exchange and link exchange to expand the tabu search range. A delivery network with 1 warehouse and 23 customer points are employed as a case study to verify the effectiveness of the model and algorithm. The 23 customer points are visited by three truck–drones. The results indicate that truck–drone delivery can effectively reduce the total delivery time by 20.1% compared with traditional pure-truck delivery. Sensitivity analysis of different parameters shows that increasing the number of truck–drones can effectively save the total delivery time, but gradually reduce the marginal benefits. Only increasing either the truck speed or drone speed can reduce the total delivery time, but not to the greatest extent. Bilateral increase of truck speed and drone speed can minimize the delivery time. It can clearly be seen that the proposed method can effectively optimize the truck–drone delivery route and improve the delivery efficiency. |
| Author | Wang, Xue Tong, Bao Zheng, Wenlong Mao, Xinhua Wang, Jianwei Zhou, Feihao |
| Author_xml | – sequence: 1 givenname: Bao surname: Tong fullname: Tong, Bao – sequence: 2 givenname: Jianwei surname: Wang fullname: Wang, Jianwei – sequence: 3 givenname: Xue surname: Wang fullname: Wang, Xue – sequence: 4 givenname: Feihao surname: Zhou fullname: Zhou, Feihao – sequence: 5 givenname: Xinhua surname: Mao fullname: Mao, Xinhua – sequence: 6 givenname: Wenlong surname: Zheng fullname: Zheng, Wenlong |
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| Cites_doi | 10.1016/j.trc.2015.03.005 10.3390/s17051144 10.1016/j.eswa.2018.03.032 10.1109/TPWRS.2005.860946 10.1109/TSMC.2016.2582745 10.3844/jcssp.2005.175.181 10.1007/s10732-019-09431-y 10.1287/ijoc.2018.0826 10.1109/MED51440.2021.9480359 10.1287/mnsc.2017.2824 10.1016/j.trc.2020.02.030 10.2197/ipsjjip.25.655 10.1007/s00170-009-2292-4 10.1287/trsc.2019.0944 10.1016/j.ejor.2020.07.048 10.1002/net.21982 10.1109/TSMCB.2008.915645 10.1016/j.trc.2018.03.025 10.1287/trsc.2017.0791 10.1016/j.trc.2019.06.016 10.1109/TSMC.2018.2867496 10.1109/TASE.2015.2461213 10.1007/s00500-015-1970-4 10.1016/j.trc.2018.12.001 10.1002/net.21864 |
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| SubjectTerms | Algorithms Cost control Customers Drones Efficiency Heuristic Integer programming Logistics Mathematical models mixed-integer nonlinear programming model optimal route problem Traveling salesman problem Trucks truck–drone delivery variable neighborhood tabu search algorithm |
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