A decision model for technology selection in the existence of both cardinal and ordinal data

Technology selection is an important part of management of technology. To select the best technologies in the existence of both cardinal and ordinal data, this paper proposes an innovative approach, which is based on a mathematical programming. A numerical example demonstrates the application of the proposed method.

[1]  Esmaile Khorram,et al.  The maximum and minimum number of efficient units in DEA with interval data , 2005, Appl. Math. Comput..

[2]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[3]  Sherif Ali Mohtady Mohamed,et al.  Modelling project investment decisions under uncertainty using possibility theory , 2001 .

[4]  Joe Zhu,et al.  Rank order data in DEA: A general framework , 2006, Eur. J. Oper. Res..

[5]  William W. CooperKyung IDEA and AR-IDEA: Models for Dealing with Imprecise Data in DEA , 1999 .

[6]  Jian-Bo Yang,et al.  Interval efficiency assessment using data envelopment analysis , 2005, Fuzzy Sets Syst..

[7]  Soung Hie Kim,et al.  An application of data envelopment analysis in telephone offices evaluation with partial data , 1999, Comput. Oper. Res..

[8]  Volker Stix,et al.  Profile distance method - a multi-attribute decision making approach for information system investments , 2006, Decis. Support Syst..

[9]  Joe Zhu,et al.  Efficiency evaluation with strong ordinal input and output measures , 2003, Eur. J. Oper. Res..

[10]  Shinhong Kim,et al.  An integrated approach for interdependent information system project selection , 2001 .

[11]  Soung Hie Kim,et al.  Identification of inefficiencies in an additive model based IDEA (imprecise data envelopment analysis) , 2002, Comput. Oper. Res..

[12]  Masood A. Badri,et al.  A comprehensive 0-1 goal programming model for project selection , 2001 .

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

[14]  Norman P. Archer,et al.  Project portfolio selection through decision support , 2000, Decis. Support Syst..

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

[16]  Chiang Kao,et al.  Interval efficiency measures in data envelopment analysis with imprecise data , 2006, Eur. J. Oper. Res..

[17]  F. Hosseinzadeh Lotfi,et al.  Sensitivity and stability analysis in DEA with interval data , 2004, Appl. Math. Comput..

[18]  Gang Yu,et al.  An Illustrative Application of Idea (Imprecise Data Envelopment Analysis) to a Korean Mobile Telecommunication Company , 2001, Oper. Res..

[19]  Gyu C. Kim,et al.  An application of zero-one goal programming in project selection and resource planning - a case study from the Woodward Governor Company , 2000, Comput. Oper. Res..

[20]  L. Seiford,et al.  Data Envelopment Analysis in the Presence of Both Quantitative and Qualitative Factors , 1996 .

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

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

[23]  M. Hajeeh,et al.  Application of the analytical hierarchy process in the selection of desalination plants , 2005 .

[24]  Soung Hie Kim,et al.  Using analytic network process and goal programming for interdependent information system project selection , 2000, Comput. Oper. Res..

[25]  Gholam Reza Jahanshahloo,et al.  On FDH efficiency analysis with interval data , 2004, Appl. Math. Comput..

[26]  K. Jo Min,et al.  Decision support models for the selection of internet access technologies in rural communities , 2005, Telematics Informatics.

[27]  Dimitris K. Despotis,et al.  Data envelopment analysis with imprecise data , 2002, Eur. J. Oper. Res..

[28]  Joe Zhu,et al.  Imprecise DEA via Standard Linear DEA Models with a Revisit to a Korean Mobile Telecommunication Company , 2004, Oper. Res..

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

[30]  Joseph Sarkis,et al.  A decision model for evaluation of flexible manufacturing systems in the presence of both cardinal and ordinal factors , 1999 .

[31]  Robert Phaal,et al.  From theory to practice: challenges in operationalising a technology selection framework , 2006 .

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

[33]  Nicole Adler,et al.  Measuring airport quality from the airlines' viewpoint: an application of data envelopment analysis , 2001 .

[34]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[35]  Srinivas Talluri,et al.  A cone-ratio DEA approach for AMT justification , 2000 .

[36]  Reza Farzipoor Saen,et al.  A decision model for selecting slightly non-homogeneous technologies , 2006, Appl. Math. Comput..

[37]  Joe Zhu,et al.  Imprecise data envelopment analysis (IDEA): A review and improvement with an application , 2003, Eur. J. Oper. Res..

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

[39]  Fereidoun Ghasemzadeh,et al.  An integrated framework for project portfolio selection , 1999 .

[40]  Reza Farzipoor Saen,et al.  Technologies ranking in the presence of both cardinal and ordinal data , 2006, Appl. Math. Comput..

[41]  Abdollah Hadi-Vencheh,et al.  On return to scale of fully efficient DMUs in data envelopment analysis under interval data , 2004, Appl. Math. Comput..