Browsing by Subject "Disaster Management"

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Open Access
Post-disaster assessment routing problem
(2018-06) Oruç Ağlar, Buse Eylül
Post-disaster assessment operations constitute the basis for the operations conducted in the response phase of the disaster management. Through the assessment of the road segments, the extent of damage and the amount of debris will be determined, and debris removal operations will benefit from this assessment. Via assessing the damage at the population centers, the needs of the affected area will be determined and the distribution of relief supplies will be made accordingly. Hence, the damage assessment allows disaster management operation coordinators to determine immediate actions necessary to respond to the effects of the disaster with the effective use of resources for alleviating human suffering. In this study, we propose a post-disaster assessment strategy as part of response operations in which effective and fast relief routing are of utmost importance. In particular, the road segments and the population points to perform assessment activities on are selected based on the value they add to the consecutive response operations. To this end, we develop a bi-objective mathematical model that utilizes a heterogeneous vehicle set. The proposed model for disaster assessment considers motorcycles, which can be utilized under off-road conditions, and/or unmanned-aerial-vehicles, drones. The first objective aims to maximize the total value added by the assessment of the road segments (arcs) whereas the second maximizes the total profit generated by assessing points of interests (nodes). Bi-objectivity of the problem is studied with the -constraint method. Since obtaining solutions as fast as possible is crucial in the post-disaster condition, heuristic methods are also proposed. To test the mathematical model and the heuristic methods, a data set belonging to Kartal district of Istanbul is used
Open Access
Shelter site location under demand uncertainty : a chance-constrained multi-objective modeling framework
(2017-06) Kınay, Omer Burak
Shelters have a very critical role in disaster relief since they provide accommodation 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 fairnessbased plans. Since this selection process is done before the occurrence of disasters, 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 indicate 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 solutions 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 scalarization 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 information by reporting many non-dominated solutions and allowing them to evaluate the trade-offs based on their preferences.