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Solving the Flying Sidekick Traveling Salesman Problem by a Simulated Annealing Heuristic

Author

Listed:
  • Vincent F. Yu

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
    Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei 106335, Taiwan)

  • Shih-Wei Lin

    (Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan
    Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei 243303, Taiwan
    Department of Emergency Medicine, Keelung Chang Gung Memorial Hospital, Keelung 20401, Taiwan)

  • Panca Jodiawan

    (Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan)

  • Yu-Chi Lai

    (Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan)

Abstract

This study investigates the flying sidekick traveling salesman problem (FSTSP), in which a truck and an unmanned aerial vehicle work together to make deliveries. This study develops a revised mixed-integer linear programming (MILP) model for the FSTSP. The revised MILP model performs better than the existing model. Due to the FSTSP’s high complexity, we propose an effective heuristic based on simulated annealing (SA) to solve the problem. The novelty of the proposed SA heuristic lies in the new solution representation, which not only determines the visiting sequence of customers but also the service type of customers and rendezvous positions. Another feature of the proposed SA is a new operator specifically designed for the FSTSP. To evaluate the performance of the proposed SA heuristic, we conduct a comprehensive computational study where we fine-tune the parameters of the SA heuristic and compare the performance of the SA heuristic with several state-of-the-art algorithms including hybrid genetic algorithm (HGA) and iterated local search (ILS) in solving existing FSTSP benchmark instances. The results indicate that the proposed SA heuristic outperforms ILS and is statistically competitive with HGA. It obtains best-known solutions for all small FSTSP instances and 29 best-known solutions for the 60 large FSTSP instances, including 20 new best-known solutions.

Suggested Citation

  • Vincent F. Yu & Shih-Wei Lin & Panca Jodiawan & Yu-Chi Lai, 2023. "Solving the Flying Sidekick Traveling Salesman Problem by a Simulated Annealing Heuristic," Mathematics, MDPI, vol. 11(20), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4305-:d:1260645
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    References listed on IDEAS

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    1. Oruc, Buse Eylul & Kara, Bahar Yetis, 2018. "Post-disaster assessment routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 116(C), pages 76-102.
    2. Matai, Rajesh, 2015. "Solving multi objective facility layout problem by modified simulated annealing," Applied Mathematics and Computation, Elsevier, vol. 261(C), pages 302-311.
    3. Pérez-Sánchez, Modesto & Sánchez-Romero, Francisco Javier & López-Jiménez, P. Amparo & Ramos, Helena M., 2018. "PATs selection towards sustainability in irrigation networks: Simulated annealing as a water management tool," Renewable Energy, Elsevier, vol. 116(PA), pages 234-249.
    4. Quang Minh Ha & Yves Deville & Quang Dung Pham & Minh Hoàng Hà, 2020. "A hybrid genetic algorithm for the traveling salesman problem with drone," Journal of Heuristics, Springer, vol. 26(2), pages 219-247, April.
    5. 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.
    6. Maria Elena Bruni & Sara Khodaparasti, 2022. "A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    7. Wang, Zheng & Sheu, Jiuh-Biing, 2019. "Vehicle routing problem with drones," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 350-364.
    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.
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