Browsing by Author "Aytekin, Ahmet"

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    Warehouse Site Selection for Humanitarian Relief Organizations Using an Interval-Valued Fermatean Fuzzy Lopcow-Rafsi Model
    (Pergamon-Elsevier Science Ltd, 2024) Korucuk, Selcuk; Aytekin, Ahmet; Gorcun, Ozhan; Simic, Vladimir; Gorcun, Omer Faruk
    The selection of warehouse locations for humanitarian organizations represents a critical and strategic decisiomaking process. It is inherently complex, influenced by uncertainties and conflicting criteria. This study presents an integrated decision-making framework tailored to address this complexity for humanitarian organizations. The framework combined two key methodologies: logarithmic percentage change-driven objective weighting (LOPCOW) and ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (RAFSI). Besides, this model has strengthened by extending with the help of the interval-valued Fermatean fuzzy sets to capture and process highly complex ambiguities. A significant advantage of this proposed model is its high resilience against the rank reversal problem, which is a critical shortcoming and challenge in decisionmaking methodologies. Additionally, the model boasts a robust, powerful, and practical structure coupled with a straightforward and easily understandable algorithm. Based on the results obtained through the implementation of the model, population density (0.1140) emerges as the most critical factor influencing decisions regarding site selection. Humanitarian organizations aim to rescue and assist as many individuals as possible during and after a disaster, prioritizing their humanitarian needs. Therefore, enhancing logistical efficiency in disaster zones and promptly reaching affected populations becomes feasible when humanitarian aid warehouses are as close as possible to areas with the highest concentration of potential disaster victims. Moreover, according to the study's findings, the most suitable option identified for the case study was Bagcilar. The conclusions drawn from sensitivity and comparative analyses affirm the model's reliability, consistency, and resilience.