Technology selection with both quantitative and qualitative outputs

Technology selection is an important part of the management of technology. To select the best technologies, a practical common-weight Multi-Objective Linear Programming (MOLP) approach with an improved discriminating power is introduced. The proposed MOLP approach enables the evaluation of the relative efficiency of Decision-Making Units (DMUs) with respect to multiple exact and ordinal outputs and a single exact input. Its robustness and discriminating power are illustrated via a previously reported robot evaluation problem by comparing the ranking that is obtained by the proposed MOLP framework with that obtained by the classical Data Envelopment Analysis (DEA) model.

[1]  Shinn Sun,et al.  Assessing computer numerical control machines using data envelopment analysis , 2002 .

[2]  Toshiyuki Sueyoshi,et al.  A unified framework for the selection of a Flexible Manufacturing System , 1995 .

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

[4]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[5]  Marcello Braglia,et al.  Dimensional analysis for investment selection in industrial robots , 2000 .

[6]  Jack R. Mkrkdtth,et al.  Justification techniques for advanced manufacturing technologies , 1986 .

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

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

[9]  C. Kao,et al.  Data envelopment analysis with common weights: the compromise solution approach , 2005, J. Oper. Res. Soc..

[10]  Feraidoon Raafat,et al.  A COMPREHENSIVE BIBLIOGRAPHY ON JUSTIFICATION OF ADVANCED MANUFACTURING SYSTEMS , 2002 .

[11]  Mehdi Toloo,et al.  An improved MCDM DEA model for technology selection , 2006 .

[12]  V. P. Agrawal,et al.  Computer aided robot selection: the ‘multiple attribute decision making’ approach , 1991 .

[13]  John R. Canada,et al.  Past and Present Methods of Manufacturing Investment Evaluation: A Review of the Empirical and Theoretical Literature , 1992 .

[14]  Robert J. Schlesinger,et al.  Decision Models for Robot Selection: A Comparison of Ordinary Least Squares and Linear Goal Programming Methods , 1989 .

[15]  V. P. Agrawal,et al.  Computer-aided evaluation and selection of optimum grippers , 1992 .

[16]  Marcello Braglia,et al.  Evaluating and selecting investments in industrial robots , 1999 .

[17]  Mao-Jiun J. Wang,et al.  A fuzzy multi-criteria decision-making approach for robot selection , 1993 .

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

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

[20]  E. E. Karsak *,et al.  Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection , 2005 .

[21]  E. Ertugrul Karsak,et al.  Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives , 2002 .

[22]  Dong Shang Chang,et al.  A chance-constraints linear programming model on the economic evaluation of flexible manufacturing systems , 1993 .

[23]  E. Ertugrul Karsak.  A TWO-PHASE ROBOT SELECTION PROCEDURE , 1998 .

[24]  Markku Kuula,et al.  Selecting a flexible manufacturing system using multiple criteria analysis , 1991 .

[25]  Fuh-Hwa Franklin Liu,et al.  Ranking of units on the DEA frontier with common weights , 2008, Comput. Oper. Res..

[26]  M. J. Khouja.  AN OPTIONS VIEW OF ROBOT PERFORMANCE PARAMETERS IN A DYNAMIC ENVIRONMENT , 1999 .

[27]  R. Dyson,et al.  Reducing Weight Flexibility in Data Envelopment Analysis , 1988 .

[28]  Emmanuel Thanassoulis,et al.  Weights restrictions and value judgements in Data Envelopment Analysis: Evolution, development and future directions , 1997, Ann. Oper. Res..

[29]  Andrea Rangone,et al.  A reference framework for the application of MADM fuzzy techniques to selecting AMTS , 1998 .

[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]  Chris Tofallis,et al.  Input efficiency profiling: An application to airlines , 1997, Comput. Oper. Res..

[32]  Young K. Son.  A Comprehensive Bibliography on Justification of Advanced Manufacturing Technologies , 1992 .

[33]  S. G. Deshmukh,et al.  A decision support system for selection and justification of advanced manufacturing technologies , 1997 .

[34]  E. Karsak,et al.  Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments , 2001 .