ANOTHER WAY TO DETERMINE WEIGHTS OF BALANCED PERFORMANCE EVALUATIONS
ANOTHER WAY TO DETERMINE WEIGHTS OF BALANCED PERFORMANCE EVALUATIONS
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.Önemli girdi 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.
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- Adler et al., (2002),”Review of ranking in the data envelopment analysis context”, European
Journal of Operational Research, 140 (2) , pp. 249–265.
- Andersen P., Petersen, N.C., (1993). “ A procedure for ranking efficient units in data
envelopment analysis”, Management Science 39, 1261-1264.
- Angulo M. and Estellita L., (2002),”Review of methods for increasing discrimination in
data envelopment analysis”, Annals of Operations Research, 116 (1–4), pp. 225–242.
- Bal, H., Örkcü, H.H. (2011), “A New Mathematical Programming Approach to MultiGroup Classification Problems”. Computers and Operations Reserach, 38(3251-3254).
- Banker, R.D., Charnes, A., Cooper, W.W., (1984), “Some models for estimating technical
and scale inefficiencies in data envelopment analysis”, Management Science 30, 1078–
1092.
- Charnes A, Cooper WW, Rhodes E., (1978), “Measuring the efficiency of decision making
units”, European Journal of Operational Research 2, 429–44.
- Cook W.D., J. Zhu, (2007), “Within-group common weights in DEA: An analysis of power
plant efficiency”, European Journal of Operational Research, 178 (1), pp. 207–216.
- Cooper, W.W., Tone, K., (1997), “Measures of inefficiency in dataenvelopment analysis
and stochastic frontier estimation”. European Journal of Operational Research 99, 72–88.
- Doyle and Green, (1994), “Efficiency and cross-efficiency in DEA: Derivations, meanings
and uses”, Journal of the Operational Research Society, 45 (5) (1994), pp. 567–578.
- Emrouznejad, A., Parker B. R., Tavares G., (2008) “Evaluation of research in efficiency
and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA“,
Socio-Economic Planning Sciences, Volume 42, Issue 3, September, Pages 151-157.
- Ganley J.A., Cubbin J.S., (1992), ”Public sector efficiency measurement: Applications of
data envelopment analysis”, North-Holland, Elsevier Science Publishers, Amsterdam.
- Kao, C., Hung, H.T., (2005) “Data Envelopment Analysis with Common Weights: the
Compromise Solution Approach,” Journal of Operation Research Society,Vol.56, ,pp.
1196-1203.
- Liu F.H.F., H.H. Peng, (2008), “Ranking of units on the DEA frontier with common
weights” , Computers and Operations Research, 35 (5), pp. 1624–1637.
- Makui, A., Alinezhad, A., KianiMavi, R., Zohrebandian, M., (2008), “A Goal Programming
Method for Finding Common Weights in DEA with an Improved Discriminating Power for
Efficiency,” Journal of Industrial and Systems Engineering, Vol. 1, pp.293-30
- Mecit, E.D., Alp, I., (2013), “A new proposed model of restricted Data Envelopment
Analysis by correlation coefficients”, Applied Mathematical Modelling, 37 (5), 3407-3425,
2013 (SCI).
- Podinovski V.V., E. Thanassoulis, (2007) , “Improving discrimination in data envelopment
analysis: Some practical suggestions”, Journal of Productivity Analysis, 28 (1–2), pp. 117–
126.
- Razavi, Hajiagha* S. H.,, Sh.S. Hashemi & H. Amoozad Mahdiraji, (2014), “DEA with
Common Set of Weights Based on a MultiObjective Fractional Programming problem”,
International Journal of Industrial Engineering & Production Research September 2014,
Volume 25, Number 3,pp. 207-214
- Ray S.C., (2004), Data Envelopment Analysis: Theory and Techniques for Economics and
Operations Research, Cambridge University Pres.
- Roll Y., W.D. Cook, B. Golany, (1991), “Controlling factor weights in data envelopment
analysis”, IEEE Transactions, 23 (1), pp. 2–9.
- Roll Y., B. Golany, (1993), “Alternate methods of treating factor weights in DEA”, Omega,
21, pp. 99–109.
- Sinuany-Stern, Z., Mehrez, A., Barboy, A., (1994). Academic departments efficiency via
data envelopment analysis. Computers and Operations Research 21 (5), 543–556.1105
- Thompson R.G., F. Singleton, R. Thrall, B. Smith, (1986), “Comparative site evaluations
for locating a high-energy physics lab in Texas”, Interfaces, 16pp. 35–49.
- Thompson, R.G. , L. Langemeier, C. Lee, E. Lee, R. Thrall, (1990), “The role of multiplier
bounds in efficiency analysis with application to Kansas farming”, J. Econom., 46 pp. 93–
108.
- Torgersen, A.M., Forsund, F.R., Kittelsen, S.A.C., (1996), “Slack-adjusted efficiency
measures and ranking of efficient units”. The Journal of Productivity Analysis 7, 379–398.
- Troutt, M.D., (1995), “A maximum decisional efficiency estimation principle”, Management
Science 41, 76–82.
- Wong, Y.-H.B., Beasley, J.E., (1990). “Restricting weight flexibility in data envelopment
analysis”. Journal of Operational Research Society 41, 829–835