A linear optimization problem to derive relative weights using an interval judgement matrix

European Journal of Operational Research

Volumn 201, Issue 2, 2010, Pages 537-544

  Conde, Eduardo ^a   de la Paz Rivera Pérez, María ^b  

^a UNIVERSITY OF SEVILLE   (Spain)

^b UNIVERSIDAD PABLO DE OLAVIDE   (Spain)

Author keywords

Decision analysis; Multi objective optimization

Indexed keywords

CONSISTENCY INDEX; DECISION ANALYSIS; DECISION MAKING MODELS; FINITE NUMBER; GEOMETRICAL PROPERTY; INTERVAL MATRIX; LINEAR OPTIMIZATION PROBLEMS; MATRIX; MULTI-OBJECTIVE FORMULATION; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; NEW MODEL; OPTIMIZATION PROBLEMS; PAIR-WISE COMPARISON; PARETO OPTIMAL SOLUTIONS; RELATIVE WEIGHTS;

DECISION MAKING; DECISION THEORY; MATRIX ALGEBRA; MULTIOBJECTIVE OPTIMIZATION; PREDICTIVE CONTROL SYSTEMS; VECTORS;

SET THEORY;

EID: 70349289151     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2009.03.029     Document Type: Article

References (21)

1. Arbel A., and Vargas L.G. Preference simulation and preference programming-robustness issues in priority derivation. European Journal of Operational Research 69 (1993) 200-209
2. Blanquero R., Carrizosa E., and Conde E. Inferring efficient weights from pairwise comparison matrices. Mathematical Methods of Operations Research 62 2 (2006) 271-284
3. Bryson N. A goal programming method for generating priority vectors. Journal of the Operational Research Society 46 (1995) 641-648
4. Carrizosa E., and Messine F. An exact global optimization method for deriving weights from pairwise comparison matrices. Journal of Global Optimization 38 (2007) 237-247
5. Chankong V., and Haimes Y. Multiobjective Decision Making (1983), North-Holland, Amsterdam
6. Choo E.U., and Wedley W.C. A common framework for deriving preference values from pairwise comparison matrices. Computers & Operations Research 31 (2004) 893-908
7. Cook W.D., and Kress M. Deriving weights from pairwise comparison ratio matrices: An axiomatic approach. European Journal of Operational Research 37 3 (1988) 355-362
8. Cox M.A.A. Examining alternatives in the interval analytic hierarchy process using complete enumeration. European Journal of Operational Research 180 2 (2007) 957-962
9. Haines L.M. A statistical approach to the analytic hierarchy process with interval judgements. (I) Distributions on feasible regions. European Journal of Operational Research 110 (1998) 112-125
10. Lootsma F.A. A model for the relative importance of the criteria in the multiplicative ahp and smart. European Journal of Operational Research 94 (1996) 467-476
11. Martos B. Nonlinear Programming. Theory and Methods (1975), North-Holland, Amsterdam
12. Mustajoki J., Hämäläinen R.P., and Lindstedt M.R.K. Using intervals for global sensitivity and worst-case analyses in multiattribute value trees. European Journal of Operational Research 174 (2006) 278-292
13. Ramanathan R. A note on the use of goal programming for the multiplicative ahp. Journal of Multi-criteria Decision Analysis 6 (1997) 296-307
14. Saaty T.L. Multicriteria Decision Making: The Analytic Hierarchy Process (1980), McGraw-Hill, New York
15. Saaty T.L. Eigenvector and logarithmic least squares. European Journal of Operational Research 48 (1990) 156-160
16. Saaty T.L., and Vargas L.G. Uncertainty and rank order in the analytic hierarchy process. European Journal of Operational Research 32 (1987) 107-117
17. Salo A., and Hämäläinen R.P. Preference assessment by imprecise ratio statements. Operations Research 40 6 (1992) 1053-1061
18. Salo A., and Hämäläinen R.P. Preference programming through approximate ratio comparisons. European Journal of Operational Research 82 (1995) 458-475
19. Thompson R.G., Dharmapala P.S., Gatewood E.J., Macy S., and Thrall R.M. DEA/assurance region SBDC efficiency and unique projections. Operations Research 44 (1996) 533-543
20. Triantaphyllou E. Multi-Criteria Decision Making Methods: A Comparative Study (2000), Springer
21. Zhu J. Imprecise data envelopment analysis (IDEA): A review and improvement with an application. European Journal of Operational Research 144 3 (2003) 513-529