This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a...

In this paper we study fuzzy multiobjective optimization problems defined for $n$ variables.  Based on a new $p$-dimensional fuzzy stationary-point definition,  necessary  efficiency conditions are obtained.  And we prove that these conditions are also sufficient under new fuzzy generalized convexity notions. Furthermore, the results are obtained under general differentiability hypothesis.

The identification of nonlinear systems operating in a stochastic environment is an important problem in various discipline science and engineering. Fuzzy modeling and especially the T-S fuzzy model draw the attention of several researchers in recent decades this is due to their potential to approximate highly nonlinear behavior. An algorithm allowing the identification of the premise and conse...

Fuzzy rules optimization is a crucial step in the development of a fuzzy model. A simple two inputs fuzzy model will have more than ten thousand possible combinations of fuzzy rules. A fuzzy designer normally uses intuition and trial and error method for the rules assignment. This paper is devoted to the development and implementation of genetic optimization library (GOL) to obtain the optimum ...

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative...

We present a unified logical framework for representing and reasoning about both quantitative and qualitative preferences in fuzzy answer set programming [Saad, 2010; Saad, 2009; Subrahmanian, 1994], called fuzzy answer set optimization programs. The proposed framework is vital to allow defining quantitative preferences over the possible outcomes of qualitative preferences. We show the applicat...

This paper describes the optimization of the fuzzy integrators in Ensembles of ANFIS models for time series prediction, this with emphasis on its application to the prediction of Mackey-Glass time series, so this benchmark time series is used to the test of performance of the proposed ensemble architecture. We used fuzzy systems to integrate the outputs (forecasts) of each of the ANFIS models i...

We present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similari...

Web Information Retrieval based on Fuzzy set derives user information need from vague and imprecise user queries and therefore uses fuzzy queries to retrieve documents relevant to user’s information need. Optimization techniques have been applied successfully in various domains and results proved effective therefore a novel approach is recommended using hybrid of Ant Colony Optimization(ACO)Gen...

Concave properties play a dominate role in solving both classic and fuzzy optimization problems. However, since fuzzy problems are generally represented by sets, not crisp numbers, various aggregation schemes are needed to manipulate and to combine the different elements in a fuzzy optimization problem. Based on these different aggregations, various concavity properties can be formulated and ex...