A CURRENT APPROACH TO OBJECTIVE CRITERIA WEIGHTING: THE HELLINGER DISTANCE METHOD (HDM)

Furkan Fahri Altıntaş

doi:10.17780/ksujes.1729297

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A CURRENT APPROACH TO OBJECTIVE CRITERIA WEIGHTING: THE HELLINGER DISTANCE METHOD (HDM)

Abstract

This study introduces the Hellinger Distance Method (HDM), a novel objective weighting approach for multi-criteria decision-making (MCDM) problems. HDM employs a dual-layered structure by simultaneously accounting for the internal variation of each criterion (via standard deviation) and the distributional dissimilarities between criteria (via the Hellinger Distance). The method was applied to assess innovation performance across seven countries using the 2024 Global Innovation Index data. Rank Reversal analysis demonstrated that HDM maintains stable alternative rankings following systematic criterion removal, indicating robust sensitivity. Further comparisons with established objective weighting methods ENTROPY, CRITIC, SD, SVP, LOPCOW, and MEREC revealed strong alignment with ENTROPY and SVP, reinforcing HDM’s reliability and methodological soundness. In addition, simulation-based analyses involving ten decision matrix scenarios confirmed the statistical homogeneity and stability of HDM-derived weights, as validated by ANOM and Levene’s tests. These findings highlight the method’s consistent performance across varied data conditions. Overall, HDM emerges as a reliable, theoretically grounded, and practically effective weighting technique, offering a valuable contribution to both the academic literature and real-world MCDM applications.

Keywords

NESNEL KRİTER AĞIRLIKLANDIRMAYA GÜNCEL BİR YAKLAŞIM: HELLİNGER MESAFE YÖNTEMİ (HDM)

Öz

Bu çalışma, çok kriterli karar verme (ÇKKV) problemleri için nesnel bir ağırlıklandırma yöntemi olan Hellinger Mesafe Yöntemi’ni (HDM) literatüre kazandırmaktadır. HDM, her bir kriterin içsel varyasyonunu (standart sapma) ve kriterler arasındaki dağılımsal farklılıkları (Hellinger Mesafesi) eş zamanlı dikkate alan iki katmanlı bir yapı sunmaktadır. Yöntem, 2024 Küresel İnovasyon Endeksi verileriyle yedi ülkenin inovasyon performanslarını değerlendirmek amacıyla uygulanmıştır. Duyarlılığı test etmek amacıyla gerçekleştirilen Sıra Terslenmesi analizinde, sistematik kriter çıkarımı sonrasında sıralamalarda anlamlı bir değişim gözlenmemiştir; bu durum HDM’nin kararlılığını göstermektedir. ENTROPY, CRITIC, SD, SVP, LOPCOW ve MEREC yöntemleriyle yapılan karşılaştırmalar, HDM’nin özellikle ENTROPY ve SVP ile yüksek uyum gösterdiğini ortaya koyarak güvenilirliğini doğrulamaktadır. On senaryolu simülasyon analizleri ise, ANOM ve Levene testleri aracılığıyla elde edilen ağırlıkların değişen veri setlerine rağmen istatistiksel olarak homojen kaldığını göstermektedir. Bulgular, HDM’nin farklı veri koşullarında dahi istikrarlı sonuçlar sunduğunu ve hem kuramsal hem de uygulamalı olarak güçlü bir katkı sağladığını göstermektedir.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Çok Ölçütlü Karar Verme

Bölüm

Araştırma Makalesi

Yazarlar

Furkan Fahri Altıntaş ^*
0000-0002-0161-5862
Türkiye

Yayımlanma Tarihi

3 Aralık 2025

Gönderilme Tarihi

30 Haziran 2025

Kabul Tarihi

19 Eylül 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 4

DOI

https://doi.org/10.17780/ksujes.1729297

IZ

https://izlik.org/JA84UG36EY

Kaynak Göster

RIS / Bibtex

APA

Altıntaş, F. F. (2025). A CURRENT APPROACH TO OBJECTIVE CRITERIA WEIGHTING: THE HELLINGER DISTANCE METHOD (HDM). Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(4), 1861-1885. https://doi.org/10.17780/ksujes.1729297