To effectively address this problem, we propose a preference-based optimization model and a genetic algorithm is applied as a numerical searching technique.
Previously, we proposed a preference set-based design (PSD) by Inoue et al. (2010) [16] method, which enables a flexible and robust design under various sources of uncertainty while capturing the designer's preference based on his/her knowledge or experience.
To facilitate product design or improvement, we propose a novel econometric preference measurement model, the modified ordered choice model (MOCM), to extract aggregate consumer preferences from online product reviews.
In this paper, we propose an innovative preference-based multi-objective evolutionary methodology to perform the assignment and mapping stages.
In addition to a few interesting and rather important side results (like a revealed preference characterisation of Gorman Polar Form preferences for an individual consumer), we proposed a revealed preference test for exact linear aggregation.
This paper proposes a novel preference degree algorithm to rank intuitionistic fuzzy values (IFVs) and applies it to multi-attribute group decision making (MAGDM) with IFVs.
Rung Yu and Shing (2013) propose a two-stage fuzzy logarithmic preference programming with multi-criteria decision making in order to derive the priorities of comparison matrices in the AHP and the ANP.
Rung and Shing (2013) propose a two-stage fuzzy logarithmic preference programming with multi-criteria decision making, to derive the priorities of comparison matrices in the analytic hierarchy process (AHP) and the analytic network process (ANP).
In Section 4, we summarise our proposed cloud service recommendation method, while in Section 5, we propose a formal way to express preferences using a Linked Unified Service Description Language (Linked – USDL) extension.
Here, we propose a model based on fuzzy preferences.
Toomim et al. propose a method to characterize workers preferences for some interfaces and tasks [84].
