Skip to main content
SpringerLink
Log in

Drone Routing for Post-disaster Damage Assessment

  • Chapter
  • First Online:
Dynamics of Disasters

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 169))

Abstract

We consider drones to support post-disaster damage assessment operations when the disaster-affected area is divided into grids and grids are clustered based on their attributes. Specifically, given a set of drones and a limited time for assessments, we address the problem of determining the grids to scan by each drone and the sequence of visits to the selected grids. We aim to maximize the total priority score collected from the assessed grids while ensuring that the pre-specified coverage ratio targets for the clusters are met. We adapt formulations from the literature developed for electric vehicle routing problems with recharging stations and propose two alternative mixed-integer linear programming models for our problem. We use an optimization solver to evaluate the computational difficulty of solving different formulations and show that both formulations perform similarly. We also develop a practical constructive heuristic to solve the proposed drone routing problem, which can find high-quality solutions rapidly. We evaluate the performance of the heuristic with respect to both mathematical models in a variety of instances with the different numbers of drones and grids.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agatz, N., Bouman, P., Schmidt, M. (2018). Optimization approaches for the traveling salesman problem with drone. Transportation Science, 52(4), 965–981.

    Article  Google Scholar 

  2. American Red Cross. (2015). Drones for disaster response and relief operations. Retrieved from https://www.issuelab.org/resources/21683/21683.pdf Accessed 17 May 2020

  3. Andelmin, J., Bartolini, E. (2017). An exact algorithm for the green vehicle routing problem. Transportation Science, 51(4), 1288–1303.

    Article  Google Scholar 

  4. Balcik, B. (2016, June). Selective routing for post-disaster needs assessments. In International Conference on Dynamics of Disasters (pp. 15–36). Springer, Cham.

    Google Scholar 

  5. Balcik, B., Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics, 11(2), 101–121.

    Article  Google Scholar 

  6. Boccardo, P., Chiabrando, F., Dutto, F., Tonolo, F. G., Lingua, A. (2015). UAV deployment exercise for mapping purposes: Evaluation of emergency response applications. Sensors, 15(7), 15717–15737.

    Article  Google Scholar 

  7. Chauhan, D., Unnikrishnan, A., Figliozzi, M. (2019). Maximum coverage capacitated facility location problem with range constrained drones. Transportation Research Part C: Emerging Technologies, 99, 1–18.

    Article  Google Scholar 

  8. Chmaj, G., and Selvaraj, H. (2015). Distributed processing applications for UAV/drones: a survey. In Progress in Systems Engineering (pp. 449–454). Springer, Cham.

    Book  Google Scholar 

  9. Chowdhury, S. (2018). Drone routing and optimization for post-disaster inspection. Mississippi State University.

    Google Scholar 

  10. Chowdhury, S., Emelogu, A., Marufuzzaman, M., Nurre, S. G., Bian, L. (2017). Drones for disaster response and relief operations: A continuous approximation model. International Journal of Production Economics, 188, 167–184.

    Article  Google Scholar 

  11. Davidson, R. A., Shah, H. C. (1997). An urban earthquake disaster risk index. Standford University: John A. Blume Earthquake Engineering Center.

    Google Scholar 

  12. Dhein, G., Zanetti, M. S., de Araújo, O. C. B., and Cardoso Jr, G. (2019). Minimizing dispersion in multiple drone routing. Computers and Operations Research, 109, 28–42.

    Article  MathSciNet  Google Scholar 

  13. Dorling, K., Heinrichs, J., Messier, G. G., and Magierowski, S. (2016). Vehicle routing problems for drone delivery. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(1), 70–85.

    Article  Google Scholar 

  14. Duarte, D., Nex, F., Kerle, N., Vosselman, G. (2017). Towards a more efficient detection of earthquake induced facade damages using oblique UAV imagery. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 93.

    Article  Google Scholar 

  15. Erdelj, M., Natalizio, E. (2016, February). UAV-assisted disaster management: Applications and open issues. In 2016 international conference on computing, networking and communications (ICNC) (pp. 1–5). IEEE.

    Google Scholar 

  16. Erdoğan, S., and Miller-Hooks, E. (2012). A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 100–114.

    Article  Google Scholar 

  17. Estrada, M. A. R., Ndoma, A. (2019). The uses of unmanned aerial vehicles–UAV’s-(or drones) in social logistic: Natural disasters response and humanitarian relief aid. Procedia Computer Science, 149, 375–383.

    Article  Google Scholar 

  18. Felipe, Á., Ortuño, M. T., Righini, G., and Tirado, G. (2014). A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transportation Research Part E: Logistics and Transportation Review, 71, 111–128.

    Article  Google Scholar 

  19. Froger, A., Mendoza, J. E., Jabali, O., Laporte, G. (2019). Improved formulations and algorithmic components for the electric vehicle routing problem with nonlinear charging functions. Computers and Operations Research, 104, 256–294.

    Article  MathSciNet  Google Scholar 

  20. Golabi, M., Shavarani, S. M., Izbirak, G. (2017). An edge-based stochastic facility location problem in UAV-supported humanitarian relief logistics: a case study of Tehran earthquake. Natural Hazards, 87(3), 1545–1565.

    Article  Google Scholar 

  21. Ham, A. M. (2018). Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming. Transportation Research Part C: Emerging Technologies, 91, 1–14.

    Article  Google Scholar 

  22. Ritchie, H. and Roser, M. (2020) - “Natural Disasters”. Published online at OurWorldInData.org. Retrieved from: https://ourworldindata.org/natural-disasters Accessed 17 May 2020

  23. Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data mining and knowledge discovery, 2(3), 283–304.

    Article  Google Scholar 

  24. IFRC (International Federation of Red Cross and Red Crescent Societies): World disasters report 2018. https://media.ifrc.org/ifrc/wp-content/uploads/sites/5/2018/10/B-WDR-2018-EN-LR.pdf. Accessed 12 March 2020

  25. IFRC (International Federation of Red Cross and Red Crescent Societies): Humanitarian logistics and procurement. (n.d.). Retrieved from https://www.ifrc.org/en/what-we-do/logistics/ Accessed 17 May 2020

  26. Kim, S. J., Lim, G. J., Cho, J., Côté, M. J. (2017). Drone-aided healthcare services for patients with chronic diseases in rural areas. Journal of Intelligent and Robotic Systems, 88(1), 163–180.

    Article  Google Scholar 

  27. Kim, K., Pant, P., Yamashita, E. (2015). Disasters, drones, and crowdsourced damage assessment. In Proceedings of Computers in Urban Planning and Urban Management Conference, Cambridge, Massachusetts.

    Google Scholar 

  28. Koç, Ç., and Karaoglan, I. (2016). The green vehicle routing problem: A heuristic based exact solution approach. Applied Soft Computing, 39, 154–164.

    Article  Google Scholar 

  29. Leetaru, K., (2015). How Drones Are Changing Humanitarian Disaster Response. Retrieved from https://www.forbes.com/sites/kalevleetaru/2015/11/09/how-drones-are-changing-humanitarian-disaster-response/ Accessed 17 May 2020

  30. Leggieri, V., and Haouari, M. (2017). A practical solution approach for the green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 104, 97–112.

    Article  Google Scholar 

  31. Meier, P., (2015). Crisis Mapping Nepal with Aerial Robotics. Retrieved from https://irevolutions.org/2015/11/04/crisis-mapping-nepal-aerial-robotics/ Accessed 17 May 2020

  32. Montoya, A., Guéret, C., Mendoza, J. E., and Villegas, J. G. (2017). The electric vehicle routing problem with nonlinear charging function. Transportation Research Part B: Methodological, 103, 87–110.

    Article  Google Scholar 

  33. Motlagh, N. H., Taleb, T., Arouk, O. (2016). Low-altitude unmanned aerial vehicles-based internet of things services: Comprehensive survey and future perspectives. IEEE Internet of Things Journal, 3(6), 899–922.

    Article  Google Scholar 

  34. Murphy, R. R., Duncan, B. A., Collins, T., Kendrick, J., Lohman, P., Palmer, T., Sanborn, F. (2016). Use of a Small Unmanned Aerial System for the SR-530 Mudslide Incident near Oso, Washington. Journal of field Robotics, 33(4), 476–488.

    Article  Google Scholar 

  35. Murray, C. C., Chu, A. G. (2015). The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transportation Research Part C: Emerging Technologies, 54, 86–109.

    Article  Google Scholar 

  36. Murray, C., Raj, R. (2020). The multiple flying sidekicks traveling salesman problem: Parcel delivery with multiple drones. The multiple flying sidekicks traveling salesman problem: Parcel delivery with multiple drones, Transportation Research Part C: Emerging Technologies, 110, 368–398.

    Google Scholar 

  37. Nedjati, A., Vizvari, B., Izbirak, G. (2016). Post-earthquake response by small UAV helicopters. Natural Hazards, 80(3), 1669–1688.

    Article  Google Scholar 

  38. Oruc, B. E., Kara, B. Y. (2018). Post-disaster assessment routing problem. Transportation research part B: methodological, 116, 76–102.

    Article  Google Scholar 

  39. Özdamar, L., and Ertem, M. A. (2015). Models, solutions and enabling technologies in humanitarian logistics. European Journal of Operational Research, 244(1), 55–65.

    Article  MathSciNet  Google Scholar 

  40. Qi, J., Song, D., Shang, H., Wang, N., Hua, C., Wu, C., …and Han, J. (2016). Search and rescue rotary-wing uav and its application to the lushan ms 7.0 earthquake. Journal of Field Robotics, 33(3), 290–321.

    Google Scholar 

  41. Rabta, B., Wankmüller, C., Reiner, G. (2018). A drone fleet model for last-mile distribution in disaster relief operations. International Journal of Disaster Risk Reduction, 28, 107–112.

    Article  Google Scholar 

  42. Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., and Sarazzi, D. (2011). UAV photogrammetry for mapping and 3d modeling–current status and future perspectives. International archives of the photogrammetry, remote sensing and spatial information sciences, 38(1), C22.

    Google Scholar 

  43. Sato, Y., Ozawa, S., Terasaka, Y., Kaburagi, M., Tanifuji, Y., Kawabata, K., …, Torii, T. (2018). Remote radiation imaging system using a compact gamma-ray imager mounted on a multicopter drone. Journal of Nuclear Science and Technology, 55(1), 90–96.

    Google Scholar 

  44. Schneider, M., Stenger, A., Goeke, D. (2014). The electric vehicle-routing problem with time windows and recharging stations. Transportation Science, 48(4), 500–520.

    Article  Google Scholar 

  45. Shavarani, S. M. (2019). Multi-level facility location-allocation problem for post-disaster humanitarian relief distribution. Journal of Humanitarian Logistics and Supply Chain Management.

    Google Scholar 

  46. Shohet, I. M., Levi, L. A. D. T., Levy, R., Salamon, A., Vilnay, O., Ornai, D., …, Levi, S. S. O. (2015). Analytical-Empirical Model for the Assessment of Earthquake Casualties and Injuries in a Major City in Israel–The Case of Tiberias. Contract, 3, 9618.

    Google Scholar 

  47. Sokat, K. Y., Dolinskaya, I. S., Smilowitz, K., Bank, R. (2018). Incomplete information imputation in limited data environments with application to disaster response. European Journal of Operational Research, 269(2), 466–485.

    Article  Google Scholar 

  48. Yamazaki, F., Kubo, K., Tanabe, R., Liu, W. (2017, July). Damage assessment and 3d modeling by UAV flights after the 2016 Kumamoto, Japan earthquake. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3182–3185). IEEE.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial Engineering Department, Ozyegin University, Cekmekoy, Istanbul, Turkey

    Birce Adsanver, Elvin Coban & Burcu Balcik

Authors
  1. Birce Adsanver
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elvin Coban
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Burcu Balcik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Birce Adsanver .

Editor information

Editors and Affiliations

  1. Department of Physics & Computer Science, Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias S. Kotsireas

  2. Isenberg School of Management, University of Massachusetts Amherst , Amherst, MA, USA

    Anna Nagurney

  3. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  4. University of Florida, Gainesville, FL, USA

    Arsenios Tsokas

Appendix 1

Appendix 1

Tables 5 and 6 demonstrate the sets, parameters, and decision variables of the arc-based and path-based formulations, respectively.

Table 5 Sets, parameters, and decision variables for the arc-based formulation
Full size table
Table 6 Sets, parameters, and decision variables for the path-based formulation
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Adsanver, B., Coban, E., Balcik, B. (2021). Drone Routing for Post-disaster Damage Assessment. In: Kotsireas, I.S., Nagurney, A., Pardalos, P.M., Tsokas, A. (eds) Dynamics of Disasters. Springer Optimization and Its Applications, vol 169. Springer, Cham. https://doi.org/10.1007/978-3-030-64973-9_1

Download citation

Publish with us

Policies and ethics

Search

Navigation