Industrial Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/45

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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.
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    ArticlePublicationOpen 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.