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In this study a simulation-based fuzzy chance-constrained programming (SFCCP) model is developed based on possibility theory.
The proposed approach herein quantifies uncertainty based on possibility theory.
This paper introduces generalized possibilistic logic (GPL), a logic for epistemic reasoning based on possibility theory.
The results show that our approach based on possibility theory has a potential for reducing uncertainty in LCC prediction modeling.
As future work, we propose to put online a tool for uncertainty propagation and sensitivity analysis based on possibility theory.
Then, the uncertainty propagation based on possibility theory method is applied to reduce the parameter and structural uncertainty of the LCC prediction model.
Similar(50)
The probabilistic or classical reliability theory is built on the principles of probability theory which introduces the probabilistic measure for evaluating confidence levels; the theory introduced in this paper is based on the possibility theory which uses a new confidence measure: the measure of possibility.
To tackle this problem, an outranking preference model based on the possibility theory is developed in this paper.
Based on the possibility theory, fuzzy return and liquidity are quantified by possibilistic mean, and market risk and liquidity risk are measured by lower possibilistic semivariance.
A measure of schedule robustness based on qualitative possibility theory is proposed to guide the search process to determine the robust schedule; i.e., the schedule with the best worst-case performance.
Our approach is based on two possible theories.
More suggestions(15)
based on graph theory
based on transition theory
based on optimization theory
based on propagation theory
based on piston theory
based on seepage theory
based on interpolation theory
based on sound theory
based on equilibrium theory
based on game theory
based on possibility distribution
based on decision theory
based on screw theory
based on bifurcation theory
based on homogenization theory
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