We present a branch-and-cut algorithm for solving discrete bilevel linear programs where the upper-level variables are binary and the lower-level variables are either pure integer or pure binary. This algorithm performs local search to find improved bilevelfeasible solutions. We strengthen the relaxed node subproblems in the branch-and-cut search tree by generating cuts to eliminate all of the ...

In this paper, we integrate goal programming (GP), Taylor Series, Kuhn-Tucker conditions and Penalty Function approaches to solve linear fractional bi-level programming (LFBLP)problems. As we know, the Taylor Series is having the property of transforming fractional functions to a polynomial. In the present article by Taylor Series we obtain polynomial objective functions which are equivalent...

Market clearing in restructured power systems is mostly implemented through an offer-based setting with the goal of maximizing social welfare. This approach leads to sound results from an economic viewpoint when generation offers reflect true production costs. However, offers may significantly differ from actual costs in practice, thus yielding undesired distortion. Under a marginal pricing sch...

In this paper, a new method is proposed to find the fuzzy optimal solution of fully fuzzy linear Bilevel programming (FFLBLP) problems by representing all the parameters as triangular fuzzy numbers. In the proposed method, the given FFLBLP problem is decomposed into three crisp linear programming (CLP) problems with bounded variables constraints, the three CLP problems are solved separately...

Train Set Organization (TSO) is to arrange the train set in railway freight transportation. Bilevel programming techniques were proposed to solve the Stackelberg game in which play is sequential and cooperation is not permitted. In this paper, an optimizing model for TSO is developed by the bilevel techniques. First, we analyzed the multiple level nature of management on TSO and simplified it i...

A neural network model is presented for solving nonlinear bilevel programming problem, which is a NP-hard problem. The proposed neural network is proved to be Lyapunov stable and capable of generating approximal optimal solution to the nonlinear bilevel programming problem. The asymptotic properties of the neural network are analyzed and the condition for asymptotic stability, solution feasibil...

This paper deals with bilevel programming programs with convex lower level problems. New necessary and sufficient optimality conditions that involve a single-level mathematical program satisfying the linear independence constraint qualification are introduced. These conditions are solved by an interior point technique for nonlinear programming. Neither the optimality conditions nor the algorith...

We present a new algorithm for solving bilevel programming problems without reformulating them as single-level nonlinear programming problems. This strategy allows one to take profit of the structure of the lower level optimization problems without using non-differentiable methods. The algorithm is based on the inexact-restoration technique. Under some assumptions on the problem we prove global...