Drone routing and optimization for post-disaster inspection

•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 hav...

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Vydané v:Computers & industrial engineering Ročník 159; s. 107495
Hlavní autori: Chowdhury, Sudipta, Shahvari, Omid, Marufuzzaman, Mohammad, Li, Xiaopeng, Bian, Linkan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.09.2021
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ISSN:0360-8352, 1879-0550
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Abstract •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.
AbstractList •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.
ArticleNumber 107495
Author Li, Xiaopeng
Bian, Linkan
Marufuzzaman, Mohammad
Chowdhury, Sudipta
Shahvari, Omid
Author_xml – sequence: 1
  givenname: Sudipta
  surname: Chowdhury
  fullname: Chowdhury, Sudipta
  organization: Department of Industrial and Systems Engineering, Mississippi State University, Starkville, MS 39759-9542, USA
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  givenname: Omid
  surname: Shahvari
  fullname: Shahvari, Omid
  email: osxtn@missouri.edu
  organization: Department of Industrial and Systems Engineering, Mississippi State University, Starkville, MS 39759-9542, USA
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  givenname: Mohammad
  surname: Marufuzzaman
  fullname: Marufuzzaman, Mohammad
  email: maruf@ise.msstate.edu
  organization: Department of Industrial and Systems Engineering, Mississippi State University, Starkville, MS 39759-9542, USA
– sequence: 4
  givenname: Xiaopeng
  surname: Li
  fullname: Li, Xiaopeng
  email: xiaopengli@usf.edu
  organization: Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620-5350, USA
– sequence: 5
  givenname: Linkan
  surname: Bian
  fullname: Bian, Linkan
  email: bian@ise.msstate.edu
  organization: Department of Industrial and Systems Engineering, Mississippi State University, Starkville, MS 39759-9542, USA
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Keywords Drone routing
Battery recharging stations
Heuristics
Disaster management
Language English
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Snippet •A post-disaster inspection drone routing problem is introduced.•A number of drone trajectory-specific factors are integrated into the model.•Developing and...
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StartPage 107495
SubjectTerms Battery recharging stations
Disaster management
Drone routing
Heuristics
Title Drone routing and optimization for post-disaster inspection
URI https://dx.doi.org/10.1016/j.cie.2021.107495
Volume 159
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