On lexicographic goal programming method for generating weights from inconsistent interval comparison matrices

DOI10.1016/j.amc.2005.04.029zbMath1137.90607OpenAlexW2082416620MaRDI QIDQ2489454

Ying-Ming Wang

Publication date: 28 April 2006

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2005.04.029

zbMATH Keywords

Goal programmingAnalytic hierarchy processWeight estimationInterval comparison matrix

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Production models (90B30) Case-oriented studies in operations research (90B90)

Related Items

The possibilistic moments of fuzzy numbers and their applications, Post-optimality analysis of priority vectors derived from interval comparison matrices by lexicographic goal programming, Generalized analytic network process, An eigenvector method for generating normalized interval and fuzzy weights, Logarithmic least squares approaches to deriving interval weights, rectifying inconsistency and estimating missing values for interval multiplicative preference relations, Deriving weights of criteria from inconsistent fuzzy comparison matrices by using the nearest weighted interval approximation, A new fuzzy programming method to derive the priority vector from an interval reciprocal comparison matrix, Consistency issues of interval pairwise comparison matrices, A stochastic AHP decision making methodology for imprecise preferences, Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multi-criteria decision making problems, Acceptable consistency analysis of interval reciprocal comparison matrices, The interval cognitive network process for multi-attribute decision-making

Cites Work

• Approximate articulation of preference and priority derivation
• Uncertainty and rank order in the analytic hierarchy process
• Preference programming and inconsistent interval judgments
• Preference simulation and preference programming: Robustness issues in priority derivation