A CONTEMPORARY AND NOVEL APPROACH FOR SOLVING SELECTION PROBLEMS AND MEASURING PERFORMANCE OF ALTERNATIVES: THE MATUSITA SIMILARITY APPROACH (MSA)

Öz

This study proposes the Matusita Similarity Approach (MSA) to achieve more precise evaluations of alternative performances within multi-criteria decision-making (MCDM) processes. Addressing the computational precision and data sensitivity limitations commonly encountered in conventional MCDM methods, MSA integrates the Matusita Distance into reference-based analyses, enabling the assessment of alternatives relative to ideal and anti-ideal reference points. Notably, the square-root intensity inherent in the Matusita Distance allows for a more sensitive measurement of performance differences among alternatives, thereby yielding results that exhibit greater reliability compared to classical approaches. The proposed method has been comparatively examined against sixteen established MCDM techniques, with its robustness validated through sensitivity and simulation analyses. The findings indicate that MSA enhances computational precision while maintaining ranking consistency, offering a dependable alternative for decision support systems. With its applicability spanning engineering, finance, healthcare, and machine learning domains, MSA provides decision-makers with a highly accurate and detailed evaluative framework.

Anahtar Kelimeler

• MCDM
• Matusita distance
• MSA

SEÇİM PROBLEMLERİNİN ÇÖZÜMÜNDE VE ALTERNATİFLERİN PERFORMANSININ ÖLÇÜLMESİNDE GÜNCEL VE YENİLİKÇİ BİR YAKLAŞIM: MATUSİTA BENZERLİK YAKLAŞIMI (MSA)

Öz

Bu çalışma, çok kriterli karar verme (ÇKKV) süreçlerinde alternatiflerin performanslarını daha hassas değerlendirmek amacıyla Matusita Benzerlik Yaklaşımı’nı (MSA) önermektedir. Geleneksel MCDM yöntemlerinde karşılaşılan hesaplama hassasiyeti ve veri duyarlılığı eksikliklerine çözüm sunan MSA, Matusita Mesafesi’ni referans tabanlı analizlere entegre ederek alternatifleri ideal ve anti-ideal referans noktalarına göre değerlendirir. Özellikle Matusita Mesafesi’nin karekök yoğunluğu sayesinde alternatifler arasındaki performans farklarının daha hassas bir şekilde ölçüldüğü değerlendirilmiş olup, bu sayede klasik yöntemlere kıyasla daha güvenirliğe yakın sonuçlar elde edilmektedir. Önerilen yöntem, literatürdeki 16 farklı MCDM tekniğiyle karşılaştırmalı olarak incelenmiş, duyarlılık ve simülasyon analizleri aracılığıyla sağlamlığı doğrulanmıştır. Bulgular, MSA’nın sıralama tutarlılığını koruyarak hesaplama hassasiyetini artırdığını ve karar destek süreçlerinde güvenilir bir alternatif sunduğunu göstermektedir. Mühendislik, finans, sağlık ve makine öğrenimi gibi disiplinlerde uygulanabilirliği olabilecek MSA, karar vericilere yüksek doğrulukta ve detaylı değerlendirme yapma imkânı sunmaktadır.

Anahtar Kelimeler

• ÇKKV
• Matusita uzaklığı
• MSA

Kaynakça

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