A Semi-infinite Programming Approach to Possibilistic Optimization under Necessity Measure Constraints
نویسنده
چکیده
In this paper, possibilistic linear programming problems are investigated. After reviewing relations among conjunction and implication functions, necessity fractile optimization models with various implication functions are applied to the possibilistic linear problems. We show that the necessity fractile optimization models are reduced to semi-infinite linear programming problems. A simple numerical example is given to demonstrate the correctness of the result. The paper is concluded with some remarks for further developments. Keywords— Possibilistic linear programming, semi-infinite linear programming, necessity measure, implication function, conjunction function
منابع مشابه
A New Approach for Approximating Solution of Continuous Semi-Infinite Linear Programming
This paper describes a new optimization method for solving continuous semi-infinite linear problems. With regard to the dual properties, the problem is presented as a measure theoretical optimization problem, in which the existence of the solution is guaranteed. Then, on the basis of the atomic measure properties, a computation method was presented for obtaining the near optimal so...
متن کاملAn Interactive Possibilistic Programming Approach to Designing a 3PL Supply Chain Network Under Uncertainty
The design of closed-loop supply chain networks has attracted increasing attention in recent decades with environmental concerns and commercial factors. Due to the rapid growth of knowledge and technology, the complexity of the supply chain operations is increasing daily and organizations are faced with numerous challenges and risks in their management. Most organizations with limited resources...
متن کاملNon-Lipschitz Semi-Infinite Optimization Problems Involving Local Cone Approximation
In this paper we study the nonsmooth semi-infinite programming problem with inequality constraints. First, we consider the notions of local cone approximation $Lambda$ and $Lambda$-subdifferential. Then, we derive the Karush-Kuhn-Tucker optimality conditions under the Abadie and the Guignard constraint qualifications.
متن کاملSolving Linear Semi-Infinite Programming Problems Using Recurrent Neural Networks
Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem. Then, we use...
متن کاملA numerical approach for optimal control model of the convex semi-infinite programming
In this paper, convex semi-infinite programming is converted to an optimal control model of neural networks and the optimal control model is solved by iterative dynamic programming method. In final, numerical examples are provided for illustration of the purposed method.
متن کامل