Preference modelling by estimating local utility functions for multiobjective optimization

Decision‐maker's preferences modelling within the goal‐programming model: a new typology

“…He also learns more about both his preference structure and the decision context parameters and alternatives. Yang and Sen (1996) consider that this kind of methods is based on a learning process and Zeleny (1982) states that the evolution of DM's preferences related to a decisional context represents an important learning process which must be taken into account. We also consider that the interactive approach is a natural implementation of a learning process and allows for a more reliable representation of the DM's preferences.…”

Interactive Nonlinear Multiobjective Optimization Methods

“…So far, we have given several examples of them. Let us next mention references to some more methods based on miscellaneous ideas: [9,29,30,31,39,46,50,54,55,78,89,94,97,100,116,117,119,132,158,162,163,176,187,188,189,190,198,199,204,212,216,217,223,226,227,229,235,248,266,267,269].…”

Satisfying optimization method based on goal programming for fuzzy multiple objective optimization problem

“…Moreover, the solution of multiple objective optimization problem is dependent upon the DM's preference. This can be represented by relative importance and priority (Lin, 2004;Tiwari et al, 1986Tiwari et al, , 1987 besides an explicit utility function or progressive articulation in actual decision making (Sakawa et al, 2004;Sakawa and Yauchi, 2001;Yang and Li, 2002;Yang and Sen, 1996). Transforming all objective functions into a scalar criterion by weights is conventional strategy for importance preference, which is based on p-norm (1 6 p 6 1) formulation (Yang, 2000).…”