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This paper presents a bi level large scale multi objective quadratic programming problem (BLLSMOQPP) with fuzzy parameters in the objective functions problem. In the first phase convert the fuzzy parameters of the problem to equivalent crisp problem to make it easy in solving. In the second phase use Taylor series to convert the quadratic problem to linear problem to be easy for solving with the decomposition algorithm that deals with large scale constraint. In addition, a numerical example is provided to demonstrate the correctness of the proposed solution. [ABSTRACT FROM AUTHOR]

This paper solves a bi-level multi objective large scale quadratic programming problem with stochastic parameters in the constraints (SBLMOLSQPP). We solve this problem usingan algorithm that begins with transforming the probabilistic nature to equivalent deterministic of this problem, thenTaylor series is used to overcome the complexity of the quadratic problem. Finally, an illustrative numerical example is given to clarify the developed theory. [ABSTRACT FROM AUTHOR]

Banking risk management as a decision making challenge is basically a complex structure with non crisp input factors. Based on the strength of those attributes it is a reasonable way for fast and human like decision to group the factors, and to use the fuzzy approach in banking risk management modeling. Bi-level programming problem of banking crisis management with linear or non-linear constraints, and in which the objective function at each level are to be maximized. The bi-level programming problem can be thought as a static version of the Stackelberg strategy, which is used leader-follower game in which a Stackelberg strategy is used by the leader, or the higher-level decision-maker (manager ), given the rational reaction of the follower, or the lower-level decision-maker (follower). This paper proposes a two-planner model and a solution method for solving this problem. This method uses the concept of tolerance membership function to develop a fuzzy Max-Min decision model for generating Pareto optimal solution for this problem; an illustrative numerical example is given to demonstrate the obtained results. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level linear programming problem with linear constraints, in which the linear objective functions are to be maximized with different rough goals, the suggested approach in this paper is mainly based on the iterative goal programming method of Dauer and Krueger to develop the optimal solution of the bi-level decision- maker, then we uses the concepts of tolerance membership function technique to generate the optimal solution for this problem. An auxiliary problem is discussed as well as an example is presented. [ABSTRACT FROM AUTHOR]

This paper studies a three-level large scale linear programming problem, in which the objective functions at every level are to be maximized. Three-level programming problem can be thought as a static version of the Stackelberg strategy, which is used leader-follower game in which a Stackelberg strategy is used by the leader. This paper proposes a fuzzy approach for solving a three-planner model and a solution method for solving this problem. Each level attempts to optimize its problem separately as a large scale programming problem using a decomposition technique to handle the optimization through a series of sub problems that can be solved easily. This method uses the concept of tolerance membership function to develop a Tchebycheff problem for generating Pareto optimal solution for this problem. Finally, a numerical example is given to clarify the main results developed in this paper. [ABSTRACT FROM AUTHOR]