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    A MILP model and a heuristic algorithm for post-disaster connectivity problem with heterogeneous vehicles
    (Springer, 2024) Tukenmez, Ilknur; Sarac, Tugba; Kaya, Onur
    Throughout the response phase of the disaster, the speedy restoration of transportation by reconnecting the nodes where the connection is broken is absolutely critical for evacuating civilians, providing clear access to hospitals, and distributing aid. Following a disaster, some roads in a disaster area might be closed to transportation. In reality, some roads can be blocked due to debris, and some of roads can be blocked by collapsing. In this model, different types of road unblocking methods are included, and each road can only be opened to access by a vehicle suitable for that method. So, different types of vehicles may be needed to repair the roads depending on the type of damage. In addition, fast-built bridges built both on land and over water are also used if necessary following a disaster. In problems of this nature, it is essential to restore the roads to enable the complete connectivity of the network such that all nodes can be reached by one another. In addition, it is also critical for the speedy reach of critical nodes, such as hospitals, and emergency disaster centers. This study aims to reduce the maximum time for connection and minimize the total time in which to reach critical nodes. For this purpose, we developed a bi-objective mathematical model that considers the multiple vehicle types that can repair different types of damages. Since the problem is NP-hard, two heuristic methods were developed, and the numerical results were presented. It has been observed that the local search algorithm gives better results than the hybrid algorithm. Additionally, different scenario data was produced. Numbers of unconnected components from 3 to 10 are solved with heuristic algorithms for test data containing 80 and 250 nodes, and real-life data containing 223 nodes and 391 edges are solved with heuristic algorithms for the number of unconnected components 6, 9, 12, and 15.
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    A MILP model and a heuristic algorithm for post-disaster connectivity problem with heterogeneous vehicles (JUL, 10.1007/s10732-024-09531-4, 2024)
    (Springer, 2024) Tukenmez, Ilknur; Sarac, Tugba; Kaya, Onur
    [Abstract Not Available]
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    Matheuristic approaches for multi-visit drone routing problem to prevent forest fires
    (Elsevier, 2024) Tukenmez, Ilknur; Ozkan, Omer
    Forest fires draw more attention as the impact of elements that threaten nature increases, such as the thinning of the ozone layer, and global warming. The prevention of forest fires is extremely important for the protection of natural life, and the provision of a healthy world to future generations. Some of the methods used for the prevention of forest fires are observation towers, unmanned aerial vehicles, images taken from satellites, and detectors. Noticing the fire as soon as it starts and intervening in the fire prevents the fire from spreading and causing major and negative consequences. The degree of fire sensitivity of forest areas may vary depending on factors such as the climate of the region, topographic structure, humidity ratio, vegetation, tree species, and density. Observing regions with high fire sensitivity more frequently than regions with low fire sensitivity will prevent the spread of fires faster by detecting them. Different from the literature, in this study, the degree of sensitivity of fire-sensitive areas are taken into account. Visit frequency is determined according to the degree of fire sensitivity. Due to the complexity of the constraints, the mathematical model can not reach the optimal solution in a short time. To solve larger problem decomposition based matheuristic approaches are proposed. Matheuristic algorithms are compared using different parameters for samples of different sizes.