Browsing by Author "Adsanver, Birce"

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Drone routing for post-disaster damage assessment
(Springer, 2021) Adsanver, Birce; Göktürk, Elvin Çoban; Koyuncu, Burcu Balçık; Industrial Engineering; GÖKTÜRK, Elvin Çoban; KOYUNCU, Burcu Balçık; Adsanver, Birce
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.
Metadata only
An integrated post-disaster assessment routing problem for collecting damage information with drones
Adsanver, Birce; Göktürk, Elvin Çoban; Göktürk, Elvin Çoban; Koyuncu, Burcu Balçık; Albey, Erinç; Yanıkoğlu, İhsan; Yıldırım, U. M.; Department of Industrial Engineering; Adsanver, Birce
In this study, we focus on post-disaster damage assessment operations supported by a set of drones when the disaster-affected area is divided into grids, and grids are clustered based on their attributes. We propose a two-phase methodology to assess the damage status of the built environment in grids. Specifically, given a set of drones and a limited time for an assessment interval, the first phase addresses 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 scanned grids while satisfying the predefined targeted coverage ratio. In the second phase, we aim to predict the damage status of unscanned grids by using the cluster-based information obtained from the scanned grids at the end of the assessment interval. Nevertheless, the damage status of all grids may not be assessed (by scanning or prediction) after one interval; therefore, these two phases iterate until all grids are evaluated. For the problem solved in the first phase, we adapt two formulations from the literature developed for electric vehicle routing problems. We also develop a Variable Neighborhood Descent based heuristic which can find high-quality solutions rapidly. We evaluate the performance of the alternative formulations and the heuristic in a variety of instances. For the second phase, we devise a novel imputation method and different imputation policies to predict the damage status of the unscanned grids. We also define several performance metrics to measure the efficiency and effectiveness of the proposed imputation policies. Our analyses demonstrate that using the proposed imputation policies improve the system performance as they induce a rapid detection of the damaged areas.
Open Access
A predictive multistage postdisaster damage assessment framework for drone routing
(Wiley, 2024-01) Adsanver, Birce; Göktürk, Elvin Çoban; Koyuncu, Burcu Balçık; Industrial Engineering; GÖKTÜRK, Elvin Çoban; Adsanver, Birce
This study focuses on postdisaster damage assessment operations supported by a set of drones. We propose a multistage framework, consisting of two phases applied iteratively to rapidly gather damage information within an assessment period. In the initial phase, the problem involves determining areas to be scanned by each drone and the optimal sequence for visiting these selected areas. We have adapted an electric vehicle routing formulation and devised a variable neighborhood descent heuristic for this phase. In the second phase, information collected from the scanned areas is employed to predict the damage status of the unscanned areas. We have introduced a novel, fast, and easily implementable imputation policy for this purpose. To evaluate the performance of our approach in real-life disasters, we develop a case study for the expected 7.5 magnitude earthquake in Istanbul, Turkey. Our numerical study demonstrates a significant improvement in response time and priority-based metrics.