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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agatz, N., Bouman, P., Schmidt, M. (2018). Optimization approaches for the traveling salesman problem with drone. Transportation Science, 52(4), 965–981.
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
Andelmin, J., Bartolini, E. (2017). An exact algorithm for the green vehicle routing problem. Transportation Science, 51(4), 1288–1303.
Balcik, B. (2016, June). Selective routing for post-disaster needs assessments. In International Conference on Dynamics of Disasters (pp. 15–36). Springer, Cham.
Balcik, B., Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics, 11(2), 101–121.
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.
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.
Chmaj, G., and Selvaraj, H. (2015). Distributed processing applications for UAV/drones: a survey. In Progress in Systems Engineering (pp. 449–454). Springer, Cham.
Chowdhury, S. (2018). Drone routing and optimization for post-disaster inspection. Mississippi State University.
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.
Davidson, R. A., Shah, H. C. (1997). An urban earthquake disaster risk index. Standford University: John A. Blume Earthquake Engineering Center.
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.
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.
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.
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.
Erdoğan, S., and Miller-Hooks, E. (2012). A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 100–114.
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.
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.
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.
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.
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.
Ritchie, H. and Roser, M. (2020) - “Natural Disasters”. Published online at OurWorldInData.org. Retrieved from: https://ourworldindata.org/natural-disasters Accessed 17 May 2020
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.
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
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
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.
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.
Koç, Ç., and Karaoglan, I. (2016). The green vehicle routing problem: A heuristic based exact solution approach. Applied Soft Computing, 39, 154–164.
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
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.
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
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.
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.
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.
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.
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.
Nedjati, A., Vizvari, B., Izbirak, G. (2016). Post-earthquake response by small UAV helicopters. Natural Hazards, 80(3), 1669–1688.
Oruc, B. E., Kara, B. Y. (2018). Post-disaster assessment routing problem. Transportation research part B: methodological, 116, 76–102.
Özdamar, L., and Ertem, M. A. (2015). Models, solutions and enabling technologies in humanitarian logistics. European Journal of Operational Research, 244(1), 55–65.
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.
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.
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.
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.
Schneider, M., Stenger, A., Goeke, D. (2014). The electric vehicle-routing problem with time windows and recharging stations. Transportation Science, 48(4), 500–520.
Shavarani, S. M. (2019). Multi-level facility location-allocation problem for post-disaster humanitarian relief distribution. Journal of Humanitarian Logistics and Supply Chain Management.
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.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix 1
Appendix 1
Tables 5 and 6 demonstrate the sets, parameters, and decision variables of the arc-based and path-based formulations, respectively.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
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
DOI: https://doi.org/10.1007/978-3-030-64973-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64972-2
Online ISBN: 978-3-030-64973-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)