Afet ve Acil Durum Lojistiği Kapsamında Acil Durum Toplanma Merkezi Seçiminde AHP Yöntemi: Kahramanmaraş On İki Şubat Belediyesinde Bir Uygulama

Çalışmada afet ve acil durum lojistiği kapsamında, Kahramanmaraş ili Onikişubat ilçesindeki en ideal “acil durum toplanma merkezinin” belirlenmesi problemi ele alınmıştır. Çok sayıda alternatifin arasında en uygun acil durum toplanma merkezinin belirlenmesi amacıyla çok kriterli karar verme (ÇKKV) yöntemlerinden faydalanılmıştır. Bir bilimsel karar verme sürecinde, kriterlerin belirlenmesi ve istenilene en uygun olanının seçilmesi hedeflenmektedir. Analitik Hiyerarşi Prosesi (AHP), çok kriterli karar verme (ÇKKV) yöntemleri içinde ana ve alt kriter ağırlıklarının hesaplanması ile ilgili sunduğu avantajlardan dolayı en sık kullanılan tekniklerden biri olmaktadır. Modelin ölçütleri ve hiyerarşik yapısı literatürde yer alan konu ile ilgili çalışmalar ve konusunda uzman kişilerin görüşleri doğrultusunda belirlenmiştir. Hiyerarşi, dört ana kriter, on dört alt kriter ve üç farklı alternatiften oluşmaktadır. İkili karşılaştırma matrisleri oluşturulurken konusunda uzman dört kişiden bilgi alınmıştır. Çalışma sonucunda, en uygun toplanma yeri seçimi ile ilgili öneride bulunmuştur

A Decision support system for shelter site selection with GIS intergration : case for Turkey

Ankara : The Department of Industrial Engineeringand the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical refences.In this study, a methodology for locating shelter sites after a disaster is developed. Currently, in Turkey, Turkish Red Crescent is responsible for selecting the location of shelter areas. First, they identify the candidate shelter site locations. Then, they rank those locations by using a weighted average function composed of eleven criteria and whenever there is an emergency, they utilize the locations with the highest ranking until there is enough space to house the affected population. To improve Turkish Red Crescent’s methodology, a mathematical model that selects the best possible combination of shelter sites from a set of candidate locations, controls the utilization of those sites and assigns every district to its closest shelter site is developed. The mathematical model is implemented with a decision support system. The decision support system, which is developed in C#, is an ArcGIS extension that uses Gurobi optimization software. With the decision support system, the user is able to solve the problem, obtain an initial solution, edit the solution and view the solution on the map that is generated by ArcGIS. To test the model with a greater data set, a sample data based on the Asian side of Istanbul is used.Kılcı, FıratM.S

Location-allocation models for relief distribution and victim evacuation after a sudden-onset natural disaster

Quick response to natural disasters is vital to reduce loss of and negative impact to human life. The response is more crucial in the presence of sudden-onset, difficult-to-predict natural disasters, especially in the early period of those events. On-site actions are part of such response, some of which are determination of temporary shelters and/ or temporary medical facility locations, the evacuation process of victims and relief distribution to victims. These activities of last-mile disaster logistics are important as they are directly associated with sufferers, the main focus of any alleviation of losses caused by any disaster. This research deals with the last-mile site positioning of relief supplies and medical facilities in response to a sudden-onset, difficult-to-predict disaster event, both dynamically and in a more coordinative way during a particular planning time horizon. Four mathematical models which reflect the situation in Padang Pariaman District after the West Sumatera earthquake were built and tested. The models are all concerned with making decisions in a rolling time horizon manner, but differ in coordinating the operations and in utilization of information about future resource availability. Model I is a basic model representing the current practice with relief distribution and victim evacuation performed separately and decisions made only considering the resources available at the time. Model II considers coordination between the two operations and conducts them with the same means of transport. Model III takes into account future information keeping the two operations separate. Model IV combines the features of Models II and III. The four models are approached both directly and by using various heuristics. The research shows that conducting relief distribution and victim evacuation activities by using shared vehicles and/or by taking into account future information on resource availability improves the current practice . This is clearly demonstrated by the experimental results on small problems. For large problems, experiments show that it is not practical to directly solve the models, especially the last three, and that the solution quality is poor when the solution process is limited to a reasonable time. Experiments also show that the heuristics help improve the solution quality and that the performances of the heuristics are different for different models. When each model is solved using its own best heuristic, the conclusions from results of large problems get very close to those from small problems. Finally, deviation of future information on resource availability is considered in the study, but is shown not to affect the performance of model III and model IV in carrying out relief distribution and victim evacuation. This indicates that it is always worthwhile to take into account the future information, even if the information is not perfect, as long as it is reasonably reliable

A multi-organisational approach for disaster preparedness and response:the use of optimisation and GIS for facility location, stock pre-positioning, resource allocation and relief distribution

From 1992 to 2012 4.4 billion people were affected by disasters with almost 2 trillion USD in damages and 1.3 million people killed worldwide. The increasing threat of disasters stresses the need to provide solutions for the challenges faced by disaster managers, such as the logistical deployment of resources required to provide relief to victims. The location of emergency facilities, stock prepositioning, evacuation, inventory management, resource allocation, and relief distribution have been identified to directly impact the relief provided to victims during the disaster. Managing appropriately these factors is critical to reduce suffering. Disaster management commonly attracts several organisations working alongside each other and sharing resources to cope with the emergency. Coordinating these agencies is a complex task but there is little research considering multiple organisations, and none actually optimising the number of actors required to avoid shortages and convergence. The aim of the this research is to develop a system for disaster management based on a combination of optimisation techniques and geographical information systems (GIS) to aid multi-organisational decision-making. An integrated decision system was created comprising a cartographic model implemented in GIS to discard floodable facilities, combined with two models focused on optimising the decisions regarding location of emergency facilities, stock prepositioning, the allocation of resources and relief distribution, along with the number of actors required to perform these activities. Three in-depth case studies in Mexico were studied gathering information from different organisations. The cartographic model proved to reduce the risk to select unsuitable facilities. The preparedness and response models showed the capacity to optimise the decisions and the number of organisations required for logistical activities, pointing towards an excess of actors involved in all cases. The system as a whole demonstrated its capacity to provide integrated support for disaster preparedness and response, along with the existence of room for improvement for Mexican organisations in flood management