Shelters have a very critical role in disaster relief since they provide accommo- dation and necessary services for the disaster victims who lost their homes. The selection of their locations among many candidate poin...
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Shelters have a very critical role in disaster relief since they provide accommo- dation and necessary services for the disaster victims who lost their homes. The selection of their locations among many candidate points is a task that should be carried out with a proper methodology that generates applicable and fairness- based plans. Since this selection process is done before the occurrence of disas- ters, it is important to take demand variability into account. Motivated by this, the problem of determining shelter site locations under demand uncertainty is addressed. In particular, a chance-constrained mathematical model that takes demand as a stochastic input is developed. By using a linearization approach that utilizes special ordered set of type 2 (SOS2) variables, a mixed-integer linear programming model is formulated. Using the proposed formulation, instances of the problem using data associated with Istanbul are solved. The results in- dicate that capturing uncertainty in the shelter site location problem by means of chance constraints may lead to solutions that are much different from those obtained from a deterministic setting. During these computational analysis, it is observed that the single-objective model is prone to generate many alternative so- lutions with different characteristics of important quality measures. Motivated by this, a multi-objective framework is developed for this problem in order to have a stronger modeling approach that generates only non-dominated solutions for the selected performance measures. The ε-constraint method is used for scalar- ization of the model. Bi-objective and 3-objective algorithms are presented for detecting all the efficient solutions of a given setting. Unlike the single-objective configuration, the decision makers could be supplied with much richer informa- tion by reporting many non-dominated solutions and allowing them to evaluate the trade-offs based on their preferences.
This paper addresses frequent and foreseeable floods in the short-term preparedness of an imminent event using a multicriteria optimization model integrated with a geographical information system to simulate flood lev...
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This paper addresses frequent and foreseeable floods in the short-term preparedness of an imminent event using a multicriteria optimization model integrated with a geographical information system to simulate flood levels, determine the best strategies, and update information. The proposed model takes into account the four main relief operations: location of emergency facilities (i.e., distribution centers, shelters, and meeting points), prepositioning of humanitarian aid, evacuation, and distribution of humanitarian aid. Three criteria are considered in the formulation to minimize: the maximum evacuation flow-time, the maximum distribution flow-time, and total cost of relief operations. The approximation to the efficient frontier is built using multiobjectiveprogramming through the use of commercial software. The usefulness and robustness of the model are verified using data from one of the worst Mexican floods considering various flood levels created from three key elements in humanitarian logistics. The strategies provided by the proposed methodology are compared with those implemented by the Mexican authorities during the studied disaster.
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