Çok kriterli sınıflandırma problemlerine yeni bir etkileşimli yöntem: Enerji sektöründe bir uygulama

Bu çalışmada birden fazla kriterle değerlendirilen alternatiflerin sınıflandırması için karar verici ile etkileşimli bir yaklaşım önerilmektedir. Karar vericinin tercihlerinin literatürde sıklıkla kullanılan toplamsal fayda fonksiyonu ile tutarlı olduğu varsayılmaktadır. Önerilen yaklaşımda, iterasyonlar boyunca karar vericiye alternatifler sunularak bu alternatiflerin kategori bilgisi alınmaktadır. Karar vericiden alınan bilgiler kullanılarak alternatiflerin atanabilecekleri olası en kötü ve en iyi kategorileri belirlemek için matematiksel modeller çözülmektedir. Bu modellerden bilgi toplayarak alternatiflerin kategorilere atanma olasılıkları hesaplanmaktadır. Karar vericiye sunulacak alternatif belirlenirken atanma belirsizliğini ölçmek için bir bilgi kuramı ölçüsü olan göreceli entropiden yararlanılmaktadır. Alternatiflerin atanma belirsizlikleri belirli bir seviyenin altına düşünce atanma olasılıkları belirli bir seviyeden yüksek olan alternatifler karar vericiye sorulmadan olasılıksal olarak kategorilere atanmaktadır. Tüm alternatifler sınıflandırılana kadar karar vericiden aşamalı olarak atama bilgisi alınmaktadır. Yaklaşımımız literatürden başka bir yöntem ile enerji sektöründe bir veri seti üzerinde test edilerek performans kıyaslaması yapılmıştır. Sonuçlar, karar vericinin bilişsel yükünü azaltmada önerilen yaklaşımın başarılı olduğunu göstermektedir.

Anahtar Kelimeler:

çok kriterli sınıflandırma problemleri, göreceli entropi, etkileşimli olasılıksal yöntem, toplamsal fayda fonksiyonu, enerji üçlemi endeksi

A new interactive approach to multiple criteria sorting problems: An application in energy sector

In this study, an interactive approach with the decision maker is proposed for sorting alternatives evaluated by multiple criteria. The preferences of the decision maker are assumed to be consistent with an additive utility function which is commonly used in literature. In the proposed approach, alternatives are assigned to categories by the decision maker through iterations. Based on the information obtained from the decision maker, mathematical models are solved in order to determine the worst and best possible categories of the alternatives. Accordingly, the probabilities of assigning alternatives to categories are calculated. Relative entropy, which is a measure of information theory, is used to measure the assignment ambiguity of the alternatives and select the alternative to be asked the decision maker. Alternatives with a certain level of assignment probability are probabilistically assigned to categories without consulting the decision maker. Assignment information from the decision maker is gathered iteratively until there are no alternative left. Our approach is compared against another approach from literature on a data set in the energy sector. The results show that the proposed approach is successful in reducing the cognitive burden of the decision maker.

Keywords:

multi-criteria sorting problems, relative entropy, interactive probabilistic method, additive utility function, energy trilemma index,

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