EURO 2024 Copenhagen
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444. UAV Search and Routing Planning in a Disaster Area

Invited abstract in session MC-29: Vehicle routing I, stream Combinatorial Optimization.

Monday, 12:30-14:00
Room: 157 (building: 208)

Authors (first author is the speaker)

1. Rajan Batta
Industrial and Systems Engineering, University at Buffalo
2. Nastaran Oladzad
Industrial and Systems Engineering, University at Buffalo
3. Esther Jose
Industrial and Systems Engineering, University at Buffalo
4. Miguel Lejeune
George Washington University

Abstract

The goal of this work is to identify the best Search and Routing Planning (SRP) for an Unmanned Aerial Vehicle (UAV) to maximize the total number of casualties detected in a disaster-affected area within a limited mission duration. First, a Mixed-Integer Non-linear Programming (MINLP) model is formulated. To linearize the model, continuous search time variables are discretized such that each can only take a finite number of possible values. Therefore, the resulting Mixed Integer Linear Programming (MILP) problem is an approximation of the original problem. This approximation is shown to be highly accurate; however, it can not solve problems with more than 10 search regions in less than three hours. Second, an exact solution approach is introduced, which is capable of solving problems of size 14 or smaller during the given computational time limit. Next, an efficient clustering heuristic is suggested to solve larger instances of the original problem with more than 200 search regions. Finally, a case study based on the 2023 Turkey-Syria earthquakes is presented. The results reveal, despite thousands of casualties remaining missing for weeks in the real-life scenario, the proposed solution methods can help detect 60-77% of casualties within a few hours in provinces under study.

Keywords

Status: accepted

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