A fractional minimal cost flow problem under linear type belief degree based uncertainty is studied for the first time. This type of uncertainty is useful when no historical information of an uncertain event is available. The problem is crisped using an uncertain chance-constrained programming approach and its non-linear objective function is linearized by a variable changing approach. An illus...

In this article, an effective method to control a power system during emergency conditions is presented. Based on Fuzzy Linear Programming (FLP), a new technique is developed to solve the Load Shedding and Generation Reallocation (LSGR) optimization Problem. The objective function consists of terms of load curtailments and deviations in generation schedules. The constraints are power system var...

In this paper, an optimization problem with a linear objective function subject to a consistent finite system of fuzzy relation inequalities using the max-product composition is studied. Since its feasible domain is non-convex, traditional linear programming methods cannot be applied to solve it. We study this problem and capture some special characteristics of its feasible domain and optimal s...

The selection of sugarcane varieties is an important problem facing companies in Brazil that exploit sugarcane harvest for energy production. In the light of current concerns regarding the reduction of environmental damages and the efficiency of the production system, research into this problem is called for. In this context the authors begin by outlining the sugarcane variety selection in acco...

In this paper we solve a wide rang of Semidefinite Programming (SDP) Problem by using Recurrent Neural Networks (RNNs). SDP is an important numerical tool for analysis and synthesis in systems and control theory. First we reformulate the problem to a linear programming problem, second we reformulate it to a first order system of ordinary differential equations. Then a recurrent neural network...