DECISIONS AND OPERATIONS RESEARCH
Year:2018 | Volume:3 | Issue:1 |Papers Count:7

In this paper, A innovative method designed to solving nonlinear optimization problems with convex object function and constrained. In this method, we define an cost function and we find variables to minimization of cost function. For create properly cost function we use K. K. T. optimal conditions. We used Nelder-Mead without derivative optimization method to minimization of cost function. When, dimensions of problem is about 10, application shows that efficiency of Nelder-Mead method is more than the other methods. Using new mathod is easier than the similar methods. By several examples efficiency of new method are verified.

Among various applications of time series, applying this concept in production industries with the intention of pre-detecting failure times of machines and implementing maintenance tasks, is considered as one of the most valuable activities in the automotive industries. With this regard, this article embarks on applying time series analysis as well as quality assurance concepts to detect failure times and implement proper actions. With this regard, we will analyze the data derived from maintenance department of the company. Then we determine influential factors on parts failure by means of quality assurance concepts.

One of the problems in portfolio selection, is choosing a stock with conflicting and incomparable objectives such as return and risk. DEA cross efficiency is one of the most useful tools in assessing performance and prioritize a number of firms that makes it possible to determine efficient units in portfolio selection from different industries. Although cross efficiency is an approach for evaluating performance, it application is improved in portfolio selection. The method used in this research, calculates the (average) cross efficiency scores and considers its changes and then incorporates two statistics of cross efficiency into the mean-variance (MV) formulation of portfolio selection. This method has two advantages: One is selection of portfolios well-diversified in terms of their performance on multiple evaluation criteria, and the other is alleviation of the so-called ‘ ‘ ganging together’ ’ phenomenon of DEA cross-efficiency evaluation in portfolio selection. This procedure is applied on stock portfolio selection in the Iranian stock market consist of 20 reputable companies and efficiency changes with causes over this period is examined. It is demonstrated in this paper that the selected portfolio yields higher risk-adjusted returns than two stock market index for a 9-year sample period.

The extent to which the goals are achieved through the measurement of performance as well as the efficiency and effectiveness of each organization are the principles of organization's dynamism. Therefore, current research has focused on the prioritization of public indicators of performance evaluation of Mazandaran province's employee through the fuzzy hierarchy analysis process. Since the evaluation of criteria is essentially subjective and qualitative, it is difficult for experts and decision-makers to express their judgments and preferences in terms of exact numerical values. Hence, the fuzzy hierarchical process analysis process, based on the concept of fuzzy theory, is used in this research. In this method, paired comparisons are performed based on linguistic words and fuzzy numbers. Accordingly, , the fuzzy pair comparisons were designed and sub-indices and were distributed to completion between experts. Then, using the Fuzzy Analytical Hierarchy Process and Expert Choice software, the fuzzy weights of each of the indices and sub-indices were determined and compared using one of the effective methods for ranking fuzzy quantities. The results show that among the general indicators of employee performance evaluation, the index of initiative and creativity was the first priority

Facility location problems are among the important operation research and management problems. Locating storehouses, hospitals, rescue-relief stations, military bases, bank branches, etc are some of its famous applications. The aim of solving such problems is to determine the best location for the facilities to ensure their maximum efficiency to provide services for customers. Location problems have recently been studied in the light of inverse approach, various classic algorithms for being introduced for their solution. 1-median problem is one of the most famous functions of target location. However, given that real world parameters are not exact, we decided to investigate fuzzy 1-median inverse problem. Based on alfa-cut concept for fuzzy triangular numbers, first we obtain a fully fuzzy linear programming model which proposes a range for different levels of certainty. Then we propose a solution method based on range account. Thus the solution of 1-median inverse problem with fuzzy parameters corresponds to its classic solution. To help better understand the proposed method, we show a numerical example.

Value at risk and expected shortfall are the two most popular measures for calculating financial risk. To calculate these measures (Value at risk and expected shortfall) there are many approaches, which can be divided into two main categories; parametric and non-parametric. In parametric approach it is supposed that the distribution of asset return belongs to a specific class of distributions. For some distributions we can claculate easily the mentioned measures. In this paper the the relation of epected shortfall has been proved for four symetric distribution.

This paper presents a methodology for detecting and classifying the errors occurring on smart power transmission lines. In the proposed method, the voltage and current phases are estimated by the phasor measurement unit (PMU) installed in the generator bus, and then the equivalent voltage and current angles are obtained. These angles are analyzed by fast fourier transform (FFT) and used to detect of transmission line errors. Detection of the transmission line error is performed using the nerve-fuzzy inference system methodology, and the diagnostic error classification is performed using support vector machine (SVM). Validation of the proposed method for the IEEE 14 system is also tested in the MATLAB software environment.