In the proposed manuscript, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is gainedusing a projection recurrent neural network (RNN) scheme. Since there is a few research for resolving of FNLOPby RNN's, we establish a new scheme to solve the problem. By reducing theoriginal program to an interval problem and then weighting problem, the Karush--Kuhn--Tucker (KKT)conditions are presented. Moreover, we apply the KKT conditions into a RNN as a efficient tool to solve the problem. Besides, the convergence properties and thestability analysis of the system model are provided. In the final step, several simulation examples are verified to support the obtained results. Reported results are compared with some other previous neural networks.

In this paper, a Multi-criteria De Novo Linear Programming (F-MDNLP) problem has been developed under consideration of the ambiguity of parameters. These fuzzy parameters are characterized by fuzzy numbers. A fuzzy goal programming approach is applied for the corresponding multi-criteria De Novo linear programming (   MDNLP) problem by defining suitable membership functions and aspiration levels. The advantage of the approach is that the decision maker's role is only the specification of the level and hence evaluate the   efficient solution for limitation of his/ her incomplete knowledge about the problem domain. A numerical example is given for illustration.

In this study, we discussed a fuzzy programming approach to bi-level linear programming problems and their application. Bi-level linear programming is characterized as mathematical programming to solve decentralized problems with two decision-makers in the hierarchal organization. They become more important for the contemporary decentralized organization where each unit seeks to optimize its own objective. In addition to this, we have considered Bi-Level Linear Programming (BLPP) and applied the Fuzzy Mathematical Programming (FMP) approach to get the solution of the system. We have suggested the FMP method for the minimization of the objectives in terms of the linear membership functions. FMP is a supervised search procedure (supervised by the upper Decision Maker (DM)). The upper-level decision-maker provides the preferred values of decision variables under his control (to enable the lower level DM to search for his optimum in a wider feasible space) and the bounds of his objective function (to direct the lower level DM to search for his solutions in the right direction).