Debris occurs from the ruin and wreckage of structures during a disaster. Proper removal of debris is of great importance because it blocks roads and prohibits emergency aid teams from accessing disasteraffected regions. Poor disaster management, lack of efficiency and delays in debris removal cause disruptions in providing shelter, nutrition, healthcare and communication services to disaster victims, and more importantly, result in loss of lives. Due to the importance of systematic and efficient debris removal from the perspectives of improving disaster victims quality of life and allowing the transportation of emergency relief materials, the focus of this study is on providing emergency relief supplies to disasteraffected regions as soon as possible by unblocking roads through removing the accumulated debris. We develop a mathematical model for the problem that requires long CPU times for large instances. Since it is crucial to act quickly in an emergency case, we also propose a heuristic methodology that solves instances with an average gap of 1% and optimum ratio of 80.83%.
Aging and some lifestyle habits cause plaque accumulation in the blood vessels of the heart and this causes narrowing of the arteries. Stents are tiny wire mesh tubes which are used in balloon angioplasty to keep the vessels open. However, the stented vessel has a risk of re-narrowing due to the recovery response of the stented vessel segment and this is called in-stent-restenosis. The objective of this study is classifying patients according to their risks of restenosis. For this purpose, first a utilites additives discriminates model called parametrized classification model is developed, then to improve the classification performance of this model, a non-dominated sorting based multi-objective evolutionary algorithm (NSGA-II) is implemented. Finally, computational experiments are conducted with real life data to demonstrate the efficiency of proposed methods.
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