We discuss the open issues in preference modelling and preference based optimization.
In this paper, we provide a concise review of research on preference modelling and preference-based optimization methods.
In this paper, we extend prior research by elaborating already-refined artifacts (requirements engineering modeling, icon-based modeling and user preference modeling), and by carrying out summative (confirmatory) evaluation.
Based on the quadratic programming model and the preference theory, the optimal portfolio decision is made.
A dimension-reduction method is used to improve the discrimination power of the DEA model and a preference ratio (PR) method is used to rank the interval efficiency scores in the resulting fuzzy environment.
Participants wanted to know the cost-effectiveness of various models and parental preference for delivery format and information content.
The behavioural model is assumed to be a logit model, and the preference distribution is a discrete finite mixture of logit models assumed to comprise latent classes of respondents with the same preference part-worth utilities and/or scale.
Future research should focus on the economic efficiency of both models and patient preference with respect to the organisation of out-of-hours primary care, because these are important features to take into account when developing out-of-hours care.
There is strong economic resistance to this model, and the preference of the "bean counters" is either to assign only one professor to teach an interdisciplinary course or to give only partial teaching credit for teaching a full course.
To incorporate preferences into multi-objective optimization algorithms, the modeling and articulation of preferences must be considered.
