Technologies Ranking by Super-Efficiency Analysis

Ranking of technologies is an important phase for technology transfer. Data Envelopment Analysis (DEA) techniques generally do not rank the efficient technologies. This paper proposes an innovative approach, which is based on the super-efficiency. The implication here is that the use of DEA in two-phase model of Khouja for robot selection may be unnecessary and the application of super-efficiency model could suffice to rank the technologies for the purposes of identifying the best performing technologies. A numerical example demonstrates the application of the proposed method.

[1]  Saati,et al.  A MODEL FOR RANKING DECISION MAKING UNITS IN DATA ENVELOPMENT ANALYSIS , 1999 .

[2]  Zilla Sinuany-Stern,et al.  Scaling units via the canonical correlation analysis in the DEA context , 1997, Eur. J. Oper. Res..

[3]  Ram Rachamadugu,et al.  A closer look at the use of data envelopment analysis for technology selection , 1997 .

[4]  Shabnam Razavyan,et al.  Ranking using l1-norm in data envelopment analysis , 2004, Appl. Math. Comput..

[5]  Gholam Reza Jahanshahloo,et al.  A ranking method based on a full-inefficient frontier , 2006 .

[6]  Srinivas Talluri,et al.  A nonparametric stochastic procedure for FMS evaluation , 2000, Eur. J. Oper. Res..

[7]  P. Andersen,et al.  A procedure for ranking efficient units in data envelopment analysis , 1993 .

[8]  Gholam Reza Jahanshahloo,et al.  A Complete Efficiency Ranking of Decision Making Units in Data Envelopment Analysis , 1999, Comput. Optim. Appl..

[9]  Alireza Amirteimoori,et al.  Ranking of decision making units in data envelopment analysis: A distance-based approach , 2005, Appl. Math. Comput..

[10]  Celik Parkan,et al.  Decision-making and performance measurement models with applications to robot selection , 1999 .

[11]  Nestor Thome.  Proceedings of the 8th WSEAS International Conference on Applied Mathematics , 2005 .

[12]  M. Zarepisheh,et al.  Modified MAJ model for ranking decision making units in data envelopment analysis , 2006, Appl. Math. Comput..

[13]  Zilla Sinuany-Stern,et al.  Review of ranking methods in the data envelopment analysis context , 2002, Eur. J. Oper. Res..

[14]  Mustafa Yurdakul,et al.  Selection of computer-integrated manufacturing technologies using a combined analytic hierarchy process and goal programming model , 2004 .

[15]  R M Thrall,et al.  DUALITY CLASSIFICATION AND SLACKS IN DATA ENVELOPMENT ANALYSIS , 1996 .

[16]  F. Førsund,et al.  Slack-adjusted efficiency measures and ranking of efficient units , 1996 .

[17]  F. Hosseinzadeh Lotfi,et al.  Using Monte Carlo method for ranking efficient DMUs , 2005, Appl. Math. Comput..

[18]  T. Sexton,et al.  Data Envelopment Analysis: Critique and Extensions , 1986 .

[19]  Moutaz Khouja,et al.  The use of data envelopment analysis for technology selection , 1995 .

[20]  Shouhong Wang.  Comments on operational competitiveness rating analysis (OCRA) , 2006, Eur. J. Oper. Res..

[21]  F. Liu,et al.  The voting analytic hierarchy process method for selecting supplier , 2005 .

[22]  Reza Farzipoor Saen,et al.  The effect of correlation coefficient among multiple input vectors on the efficiency mean in data envelopment analysis , 2005, Appl. Math. Comput..

[23]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

[24]  F. Hosseinzadeh Lotfi,et al.  A note on some of DEA models and finding efficiency and complete ranking using common set of weights , 2005, Appl. Math. Comput..