Optimizing Humanitarian Flood Relief: A Multi-Modal Last-Mile Delivery Framework with Truck, Drone, and Boat

 


Natural disasters such as floods pose significant challenges to logistics and relief operations, particularly in the last mile of delivery where accessibility is often compromised. In flood-stricken regions, conventional ground transport may be rendered ineffective due to submerged roads, while aerial solutions alone may fall short due to limited payload capacity.

To address these challenges, a recent study proposes a novel optimization framework for humanitarian logistics, combining three complementary transportation modes: truck, drone, and inflatable boat.

This integrated delivery framework, referred to as the Traveling Salesman Problem with Drone and Boat (TSP-DB), models the distribution of essential relief items from a central warehouse to multiple emergency shelters located in both dry and flooded areas. The truck serves as a mothership, carrying both the drone and the boat, and serves as the central launching and recovery point for the other two modes of transport.

Problem Structure and Constraints

In the proposed model:

  • Dry-area shelters can be accessed via truck or drone.

  • Flooded-area shelters require access via boat or drone.

  • The drone, due to its limited capacity and battery constraints, is restricted to one delivery per trip before it must return to the truck.

  • The inflatable boat, in contrast, is capable of serving multiple shelters in a single trip.

The primary objective of the framework is to minimize the overall completion time—a critical performance indicator in humanitarian logistics where time-sensitive deliveries can determine outcomes in life-threatening situations.

Mathematical Formulation and Solution Approach

The problem is initially formulated as a Mixed Integer Linear Program (MILP), enabling a rigorous representation of logistical constraints, routing decisions, and transportation dynamics. However, due to the combinatorial nature of the problem and its exponential complexity—especially when applied to real-world instances—exact methods alone become computationally infeasible for large scenarios.

To overcome this limitation, the authors propose a matheuristic approach, which blends exact optimization methods with the metaheuristic “record-to-record travel” algorithm. This hybrid method balances computational efficiency with solution quality, allowing for high-performance solving of complex and large-scale instances.

Performance Assessment and Case Application

The proposed approach was tested on both synthetic benchmark datasets and a real case study set in Jakarta, Indonesia—a region that frequently experiences urban flooding. The results demonstrate that the method is both robust and competitive, outperforming or matching state-of-the-art algorithms developed for similar variants of the Traveling Salesman Problem with drones (TSP-D).

Notably, the study yielded valuable managerial insights for humanitarian planners:

  • The strategic deployment of the drone and boat in tandem significantly reduces delivery delays.

  • The adaptive routing capability of the boat—serving multiple destinations in flood zones—complements the drone’s speed and precision.

  • The truck-as-mothership model enables flexible hub-based operations, which are particularly useful in evolving disaster environments.

Conclusion

This work presents an innovative and practically viable logistics framework tailored to flood relief scenarios, combining technological diversity with mathematical rigor. The TSP-DB model represents a next-generation approach to humanitarian delivery, leveraging the unique strengths of air, water, and ground transport to overcome infrastructural challenges. The research not only advances the field of disaster logistics but also offers a foundation for scalable and adaptable relief operations in a world where climate-induced emergencies are increasingly common.

 References

 

  • Faiz, T. I., Vogiatzis, C., & Noor-E-Alam, M. (2022). A robust optimization framework for two-echelon vehicle and UAV routing for post-disaster humanitarian logistics operations. arXiv preprint arXiv:2207.11879.

  • van Steenbergen, R., van Heeswijk, W., & Mes, M. (2023). The stochastic dynamic post-disaster inventory allocation problem with trucks and UAVs. arXiv preprint arXiv:2312.00140.

  • Merkle, N., Bahmanyar, R., Henry, C., et al. (2023). Drones4Good: Supporting disaster relief through remote sensing and AI. arXiv preprint arXiv:2308.05074.

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