VERİ ZARFLAMA ANALİZİ TEMELLİ ÇOK ÖLÇÜTLÜ KARAR VERME YAKLAŞIMINDA YENİ MODEL ÖNERİLERİ VE UYGULAMALARI

This thesis presents common weight data envelopment analysis (DEA)-based decision making frameworks to provide common assessment with enhanced discriminating power and improved weight dispersion. The developed methodologies consider multiple inputs as well as outputs, and proposed mathematical programming models are applicable when crisp and/or imprecise data are present. The first proposed common weight DEA-based decision framework that includes multiple inputs and multiple outputs is a novel mathematical programming approach that improves the common weight model developed by Karsak and Ahiska (2007). The robustness of the developed model is illustrated by two case studies that aim to provide economic and financial performance assessment. The second proposed common weight DEA-based decision framework aims to incorporate imprecise data into the initially developed decision approach. Three illustrations from the relevant literature are provided to demonstrate the robustness of the proposed methodology, and ranking results are compared with those of other approaches developed in earlier research papers. A case study, which focuses on identifying the most desirable country to work for an expatriate, is presented as well. The third proposed quality function deployment integrated DEA-based decision framework presents an integrated group decision making approach that ranks countries by considering ten attributes that are included in sustainable development goals (SDGs) set by United Nations Development Program (UNDP). The robustness of the proposed approach is shown via a case study that aims to rank Latin American countries considering ten criteria that are included in SDGs presented by UNDP. Comparative advantages of the developed approaches are thoroughly analyzed.