Ozdemir, Umit; Mete, Suleyman; Gul, Muhammet

Emergency and Disaster Assembly Areas (EDAA) are designated safe zones where basic needs can be met until temporary shelters are established following natural or man-made disasters like floods, fires, earthquakes, explosions, or chemical incidents. Promptly relocating disaster victims to these areas is crucial for minimizing loss of life and facilitating effective search and rescue operations by maintaining an uninterrupted flow of information. To prepare for disasters like earthquakes, which cause significant material and emotional damage to large populations, sustainable disaster management must be ensured to evaluate site suitability, correct deficiencies, and avoid inappropriate locations. This study will examine the evaluation criteria for EDAAs established by the Tunceli Provincial Disaster and Emergency Management Authority (AFAD) in terms of area, structure, security, and accessibility, taking into account the region’s specific characteristics. Based on a literature review, eleven criteria have been proposed and ranked using the Besson mean ranking method. Areas have been classified into four categories (e.g., adequate, not suitable) using the optimistic, pessimistic, and comprise approaches of the Assignment Rule Driven by Attitudes (ARDA) and the ORESTE-Sort method. The examination of 19 EDAA provides two perspectives: an optimistic view that recommends classifying eleven areas as first class and using all areas as they are, and a pessimistic view that calls for urgent improvements in three areas and states that one area (EDAA 1) is deemed unsuitable due to its assignment to class (Formula presented.). It is also advised that the second area should not be used, despite being rated as class (Formula presented.), due to its proximity to the river and its slope characteristics. The study also performs a sensitivity analysis of the method and provides recommendations for future research.

AtıfScopus2·WoS1

İncele

Ozdemir, Umit; Mete, Suleyman; Gul, Muhammet

Volunteers collaborate with professionals to provide institutional assistance in various areas; their management differs from that of professionals. To maximise volunteer productivity, categorising volunteers according to their knowledge and experience levels and distributing tasks increases volunteer retention and task efficiency. This study categorizes volunteers into four groups, fulfilling nine criteria identified through a literature review of search and rescue activities. TOPSIS-Sort and ORESTE-Sort (Optimist and Pessimist approaches) are employed in classification. Only one alternative is consistently assigned to the same class across all methods. While ORESTE-Sort offers greater flexibility and three distinct classification outcomes, it is more complex and sensitive to threshold values. TOPSIS-Sort, though simpler and producing a single classification, requires criterion weight. The Wilcoxon signed rank test is applied to evaluate the similarity between ORESTE-Sort and TOPSIS-Sort results; there are significant differences between the two methods. The study concludes by comparing the methods' applications, steps, and solution approaches, highlighting their respective advantages and disadvantages, and suggesting directions for future research.

İncele

Ozdemir, Umit; Mete, Suleyman; Gul, Muhammet

Effective volunteer management requires ensuring operational continuity and maximizing benefits from all team members. Prioritizing volunteers with adequate knowledge and experience in task assignments is crucial for maintaining workflow continuity. This study employs a two-stage model for allocating areas and tasks to volunteers in search and rescue operations. The first stage identifies the optimal number of volunteer response centers using the P-median problem. Considering population density as demand, 7 out of 14 accessible and protected disaster collection areas meeting basic needs are selected as optimal response centers based on distance. In the second stage, a mixed-integer programming model is proposed for volunteer assignment, which prioritizes assigning the most knowledgeable and experienced volunteers to critical positions. The model’s flexibility and applicability are demonstrated by incorporating voluntary rest periods, which are allocated based on the urgent needs of response centers in various post-disaster scenarios. Solved using GAMS/CPLEX, the model’s results show that expert volunteers in low-demand scenarios are prioritized, working 5 out of 9 shifts, while other volunteers receive insufficient assignments. In high-demand scenarios, some response centers face volunteer shortages, with a deficit of 62 volunteers across 9 shifts in non-emergency areas. The study evaluates the benefits and drawbacks of the volunteer assignment model and offers recommendations for future research.

AtıfScopus2·WoS2·OpenAlex3

İncele

Ozdemir, Umit; Mete, Suleyman; Gul, Muhammet

Today, it is vital for humanitarian organizations operating in many different fields to select volunteers from the determining criteria according to the needs of the institution. Failure to select volunteers in accordance with the institution's needs may cause the volunteers to be unable to continue their duties and interrupt the institution's work. When choosing volunteers, it is essential for effective volunteer management to classify (expert, experienced, inexperienced, and insufficient) them according to the institution’s needs and assign them starting from the volunteer in the best class volunteer when necessary. The aim of this study is to determine the criteria weights in the classification of volunteers who want to participate in search and rescue activities in humanitarian aid organizations according to the criteria determined before their assignment. In this study, a questionnaire is applied to field experts in Turkey to determine the most important and least important criteria, then criteria weights are calculated by using the Best-Worst Method (BWM). The findings obtained as a result of our study are evaluated and suggestions are made on how to improve it for future studies.

AtıfScopus1·OpenAlex1

İncele