This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, t...

Covering problem tries to locate the least number of facilities and each demand has at least one facility located within a specific distance.This paper considers a cross entropy algorithm for solving the mixed integer nonlinear programming (MINLP) for covering location model.The model is solved to determine the best covering value.Also, this paper proposes aCross Entropy (CE) algorithm consider...

We present a new algorithm for nonlinear semidefinite programming, based on the iterative solution in the primal and dual variables of Karush-KuhnTucker optimality conditions, which generates a feasible decreasing sequence. At each iteration, two linear systems with the same matrix are solved to compute a feasible descent direction and then an inexact line search is performed in order to determ...

In this paper we present a robust duality theory for generalized convex programming problems in the face of data uncertainty within the framework of robust optimization. We establish robust strong duality for an uncertain nonlinear programming primal problem and its uncertain Lagrangian dual by showing strong duality between the deterministic counterparts: robust counterpart of the primal model...

In this study a hierarchical hub location problem with two layers is considered. The first layer includes small hubs and the second one includes a star-shaped network central hub. The considered case is a cargo delivery network where there is hierarchy between hubs. All the hubs and links are capacitated and there are three kinds of commodities for each of which there is a special kind of vehic...

Accurate modelling of real-world problems often requires nonconvex terms to be introduced in the model, either in the objective function or in the constraints. Nonconvex programming is one of the hardest fields of optimization, presenting many challenges in both practical and theoretical aspects. The presence of multiple local minima calls for the application of global optimization techniques. ...

in traditional portfolio selection model coefficients often are certain and deterministic, but in real world these coefficients are probabilistic. so decision maker cannot estimate them exactly. financial optimization is one of the most attractive areas in decision under uncertainty. in the portfolio selection problem the decision maker considers simultaneously conflicting objectives such as ra...

Optimal repair–replacement problem is an important aspect of economic decisionmaking at the firm and aggregate levels. In this paper, we extend the continuous time optimal replacement model in the firm under technological progress by considering the possibility of repairing/replacing the machines during their lifetime period. In ourmodel, two possible decisions can be recognized by themanagers ...

It is wildly believed that the information sharing and cooperation become important now for improving the performance of the retailer and the manufacturer of the short lifcycle product. This paper suggests a way to enhance the competency of short lifecycle products by integrating the markdown pricing policy into the aggregate production planning.An Aggregate Production Planning -Markdown Pricin...

In group decision making under uncertainty, interval preference orderings as a type of simple uncertain preference structure, can be easily and conveniently used to express the experts’ evaluations over the considered alternatives. In this paper, we investigate group decision making problems with interval preference orderings on alternatives. We start by fusing all individual interval preferenc...