Drone routing and optimization for post-disaster inspection

• Published in: Computers & industrial engineering Vol. 159; p. 107495
• Main Authors: Chowdhury, Sudipta, Shahvari, Omid, Marufuzzaman, Mohammad, Li, Xiaopeng, Bian, Linkan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2021
• Subjects: Battery recharging stations; Disaster management; Drone routing; Heuristics; Drone routing; Battery recharging stations; Heuristics; Disaster management
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2021.107495

•A post-disaster inspection drone routing problem is introduced.•A number of drone trajectory-specific factors are integrated into the model.•Developing and testing different heuristic techniques.•Drawing managerial insights from a real-life disaster-affected monitoring case study. Aerial drones have received increased attention by humanitarian organizations due to their potential to serve/monitor a disaster-affected region. In this study, we propose a mixed-integer linear programming model for a Heterogeneous Fixed Fleet Drone Routing problem (HFFDRP) that minimizes the post-disaster inspection cost of a disaster-affected area by accounting a number of drone trajectory-specific factors into consideration, such as battery recharging costs, servicing costs, drone hovering, turning, accelerating, cruising, and decelerating costs, and many others. Two heuristic algorithms are proposed, namely, Adaptive Large Neighborhood Search (ALNS) algorithm and Modified Backtracking Adaptive Threshold Accepting (MBATA) algorithm, to solve the largest instances of our proposed optimization model in a reasonable amount of time. Computational results indicate that the proposed MBATA algorithm is capable of producing high-quality solutions consistently within a reasonable amount of time. Finally, a real-life case study is used to visualize and validate the model results and to draw managerial insights that reveal how network design decisions vary with key drone design parameters.