linear programming

A method for designing suboptimal control for a class of delayed fractional systems is proposed in this paper. Despite theoretical advances in fractional mathematics and computational techniques for solving fractional optimal control (FOC) problems, as well as a lack of comprehensive analytical methods, numerical methods have been developed. For this purpose, in this study, the necessary optimal conditions for the time-delay fractional optimal control (TDFOC) problem are presented first; Then an algorithm for the numerical solution to this problem is suggested. This algorithm is based on a fractional derivative approximation and linear interpolation for delayed arguments. According to this method, the TDFOC problem is transformed into a system of algebraic equations that can be solved numerically. The proposed method's efficiency is assessed by solving several numerical examples.

This paper presents a new method for regression model prediction in an uncertain environment. In practical engineering problems, in order to develop regression or ANN model for making predictions, the average of set of repeated observed values are introduced to the model as an input variable. Therefore, the estimated response of the process is also the average of a set of output values where the variation around the mean is not determinate. However, to provide unbiased and precise estimations, the predictions are required to be correct on average and the spread of date be specified. To address this issue, we proposed a method based on the fuzzy inference system, and genetic and linear programming algorithms. We consider the crisp inputs and the symmetrical triangular fuzzy output. The proposed algorithm is applied to fit the fuzzy regression model. In addition, we apply a simulation example and a practical example in the field of machining process to assess the performance of the proposed method in dealing with practical problems in which the output variables have the nature of uncertainty and impression. Finally, we compare the performance of the suggested method with other methods. Based on the examples, the proposed method is verified for prediction. The results show that the proposed method reduces the error values to a minimum level and is more accurate than the Linear Programming (LP) and fuzzy weights with linear programming (FWLP) methods.

A stand-alone microgrid usually contains a set of distributed generation resources, energy storage system and loads that can be used to supply electricity of remote areas. These areas are small in terms of population and industry. Connection of these areas to the national distribution network due to the high costs of constructing transmission lines is not economical. Optimal utilization and economic management of production units and storage devices are important issues in isolated microgrids. During optimum utilization, of renewable energy harvesting is maximized and fuel cost of diesel units reduces as much as possible. In this paper, the optimization problem is designed and solved as Linear Programming (LP). The cost of diesel generator unit depends on its production. Also, the fact is considered that the efficiency of diesel generator units is not constant for all amount of production. As a solution for this challenge demand side management plans have been proposed. On the other hand, load uncertainty is considered in this paper. Several scenarios are simulated by GAMS software for different conditions of a typical microgrid. The simulation results show the success of the proposed method in reducing costs and fossil fuel consumption and increasing the consumption of renewable energy.

Future mobile communication networks particularly 5G networks require to be efficient, reliable and agile to fulfill the targeted performance requirements. All layers of the network management need to be more intelligent due to the density and complexity anticipated for 5G networks. In this regard, one of the enabling technologies to manage the future mobile communication networks is Self-Organizing Network (SON). Three common types of SON are self-configuration, Self-Healing (SH) and self-optimization. In this paper, a framework is developed to analyze proactive SH by investigating the effect of recovery actions executed in sub-health states. Our proposed framework considers both detection and compensation processes. Learning method is employed to classify the system into several sub-health (faulty) states in detection process.  The system is modeled by Markov Decision Process (MDP) in compensation process in which the equivalent Linear Programing (LP) approach is utilized to find the action or policy that maximizes a given performance metric. Numerical results obtained in several scenarios with different goals demonstrate that the optimized proposed algorithm in compensation process outperforms the algorithm with randomly selected actions.