We propose an exact penalty approach for solving mixed integer nonlinear programming (MINLP) problems by converting a general MINLP problem to a finite sequence of nonlinear programming (NLP) problems with only continuous variables. We express conditions of exactness for MINLP problems and show how the exact penalty approach can be extended to constrained problems.
This special class of a nonlinear mathematical programming problem which is addressed in this paper has a structure characterized by a subset of variables restricted to assume discrete values, which are linear and separable from the continuous variables. The strategy of releasing
This paper presents a new algorithm for solving mixed integer nonlinear programming problems. The algorithm uses a branch and bound strategy where a nonlinear programming problem is solved in each integer node of the tree. The nonlinear programming problems, at each integer node
The open-pit mine production scheduling problem has received a great deal of attention in recent years, both in the academic literature, and in the mining industry. Optimization approaches to strategic planning for mine exploitation have become the industry standard, as the recen
This paper deals with the development of an efficient hybrid approach based on genetic algorithm and particle swarm optimization for solving mixed integer nonlinear reliability optimization problems in series, series–parallel and bridge systems. This approach maximizes the overal
in this paper, some kkt type sufficient global optimality conditions for general mixed integer nonlinear programming problems with equality and inequality constraints (minpp) are established. we achieve this by employing a lagrange function for minpp. in addition,
In this paper, some KKT type sufficient global optimality conditions for general mixed integer nonlinear programming problems with equality and inequality constraints (MINPP) are established. We achieve this by employing a Lagrange function for MINPP. In addition, verifiable suff