İhsan ALP

3517

DENGELİ PERFORMANS AĞIRLIKLARININ HESAPLANMASINDA DİĞER BİR YOL

Çok girdi ve çıktı olması durumunda, karar verme birimlerinin (KVB) performans hesabı, ağırlıklı çıktılar toplamı bölü ağırlıklı girdiler toplamı olarak tanımlanır. Performans ağılıklarını belirlemede başlıca iki yol vardır: Sübjektif ve objektif yaklaşımlar. Sübjektif yaklaşımlarda girdi ve çıktılara verilen ağırlıklar KVB’nin ya da uzmanların görüşlerine dayalı belirlenir. Objektif yaklaşımlarda ise ağırlıklar kişisel görüşlere dayanmayarak model ve hesaplamalar yardımıyla tespit edilir. Bunlardan en yaygınca kullanılanı Veri Zarflama Analizi (VZA) yöntemidir. VZA yöntemi parametrik olmayan yöneylem araştırması tabanlı bir tekniktir. VZA performans hesaplamalarında çok girdi ve çok çıktıyı her KVB’nin performansını en büyük yapacak ağırlıkları doğrusal programlamayla objektif biçimde hesaplar. Bu yöntemle hesaplanan ağırlıklar için iki dezavantaj vardır: I.Önemligirdi ve çıktılara sıfıra yakın veya sıfır ağırlık vermek. II.  Performans hesaplamalarında her bir girdi ve çıktıya farklı karar vericiler için farklı ağırlıklar ataması KVB’lerinin performansı hesaplanırken yöntemin yukarıda bahsedilen dezavantajlarını elimine etmenin bir yolu ortak ağırlıklar kullanmaktır. Başka bir yöntem girdilerle çıktılar arasında korelasyonları kullanmaktır.
Anahtar Kelimeler:

Veri zarflama analizi, ortak ağırlıklar, performans hesaplamaları, korelasyon

ANOTHER WAY TO DETERMINE WEIGHTS OF BALANCED PERFORMANCE EVALUATIONS

In case of multiple inputs and outputs, performance of Decision Making Units (DMU) is defined as the ratio of weighted sum of outputs to weighted sum of inputs. There are two group ways to determine the weights of performance : objective and subjective approaches mainly. In the subjective approaches, weights which will be given to the inputs and outputs are determined based on the opinion of DMUs or experts. In the objective approaches, weights are found via models  and calculations which are not based on personal judgments. One of them is the most important and widely used Data Envelopment Analysis (DEA) method. Data Envelopment analysis is a nonparametric and operations research-based technique. DEA, in the performance calculations, assigns weights to multiple inputs and outputs in an objective manner by means of a linear programming model to maximize the performance of each DMU.There may be two disadvantages for the weights which calculated by this method:I.  To give very small or zero weights to important inputs and outputs.II.  In aggregate evaluation, computed weights generally to be different for each input and output for different decision- makers; in the performance evaluation, importances or weights of the inputs and outputs not to happen same for every DMU. One way for eliminate the disadvantages mentioned above is to use common weights when calculating the performance of DMUs. Another method is to use the correlation coefficients between inputs and outputs. Mentioned methods in this work will be interpreted by applying to the data of a real-world problem.
Keywords:

Data envelopment analysis, common weigths, performans calculations, correlation,

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Dumlupınar Üniversitesi Sosyal Bilimler Dergisi-Cover
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 1999
  • Yayıncı: Dumlupınar Üniversitesi Rektörlüğü
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DENGELİ PERFORMANS AĞIRLIKLARININ HESAPLANMASINDA DİĞER BİR YOL

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