Heuristics Based Optimization for Multidepot Drone Location and Routing Problem to Detect Post-Earthquake Damages

AYDIN N., Yilmaz O., Deveci M., Lv Z.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, cilt.25, sa.1, ss.850-858, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1109/tits.2022.3190698
  • Dergi Adı: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.850-858
  • Anahtar Kelimeler: Drones, Earthquakes, Buildings, Routing, Mathematical models, Path planning, Image resolution, Post-earthquake damage detection, location and routing, ant colony optimization, mixed integer linear programming, drones, UAV
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

The aim of this research is to detect the post-disaster damage by drones as soon as possible so that decision makers can assign search and rescue teams effectively and efficiently. The main differences of this research from the others, which use drones in literature, are as: First, the regions are divided into grids and different importance values are assigned according to the number of buildings that are likely to be damaged and are vital for the response stage, such as hospitals, schools, and fire stations. Second, these importance levels are updated based on the day and time, which helps ordering the grids in a more realistic manner. Third, the depots are selected among the pre-determined candidate locations in accordance with the purpose of objective function. Fourth, detection times at grids are considered as uncertain. Fifth, two versions of Ant Colony Optimization (ACO) are developed as alternatives to exact solution tools. Last, sensitivity analyzes are performed by reducing the number of sorties, reducing the number of drones, and comparing day and night importance values for each instance. According to the results, only for very small-scale instances, exact solution tool was able to reach the optimal while both versions of ACO reached to similar results within a very less CPU times. Additionally, these ACO algorithms also found good results for the larger scaled problems. Then the performance of these ACO algorithms and the exact solution method are compared based on the CPU time and solution quality.