The main objective of this study is to identify the most important criteria and indicators in selection of business intelligence vendors, and ranking the vendors of such tools using Fuzzy Analytical hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), to compare results of these two methods and to provide appropriate solutions for the sample company, namely National Iranian Oil Company (NIOC).
Spearman & apos; s rank correlation test was used for comparing the methods and determining their correlation. A strong positive correlation was observed between the ranks of business intelligence tools at the significance level of 0.05 in both methods. The results of the ranking by means of FAHP method show that IBM Company was the best one, followed by Oracle, SAS, QlikTech, SAP and Microsoft. However, based on the FTOPSIS method, Oracle was the leading company, followed by IBM, SAS and SAP and finally Microsoft.

As the world is getting overpopulated and over polluted the human being is seeking to utilize new sources of energy that are cleaner, cheaper, and more accessible. Wind is one of these clean energy sources that is accessible everywhere on the planet earth. This source of energy cannot be stored for later use; therefore, environmental circumstances and geographical location of wind plants are crucial matters. This study proposes a model to decide on the optimum location for a wind farm among the demand area. To tackle the uncertainty related to the geographical position of the nominated location such as wind speed; altitude; mean temperature; and humidity; a simulation method is applied on the problem. Other factors such as the time that a plant is out of service and demand fluctuations also have been considered in the simulation phase. Moreover, a probability distribution function is calculated for the turbine power. Then Data Envelopment Analysis (DEA) performs the selection between all the nominated locations for wind farm. The proposed model takes into account several important elements of the problems. Elements such as land cost; average power received from the wind blowing; demand point population etc. are considered at the same time to select the optimum location of wind plants. Finally, the model is applied on a real case in order to demonstrate its reliability and applicability.

Nowadays, industrial robots are being pervasively used in almost every manufacturing organization for improving operational quality, safety and productivity. Depending on the nature of task to be performed, many varieties of robots are now commercially available from different manufacturers. For efficiently carrying out the designed task, a number of functional attributes of an industrial robot are also simultaneously responsible. Therefore, selection of an appropriate and competitive robot alternative becomes a complicated and equally challenging task for the decision makers. A quite strong model of multi-criteria decision-making is needed to deal with this problem of industrial robot evaluation and selection. In this paper, the applicability of fuzzy axiomatic design (FAD) principles is explored for solving a real time robot selection problem. Seven candidate robots which are commercially available for light assembly operations are evaluated with respect to a mix of nine criteria. All these criteria are either qualitative in nature or expressed as a range of numerical values. Suitability rankings of all the feasible alternatives are derived using FAD methodology, thus establishing it as a systematic and dependable tool for solving industrial robot selection problems in fuzzy environment.

This research seeks to examine and introduce an appropriate method for evaluating and selecting suppliers in multiple sourcing and compares two efficient and effective methods, i.e. MADM and ANN for selecting the suppliers. The results of using the methods are compared with each other by using the measurement criteria such as RMSE, NRMSE, and R2 and they indicate that ANN could perform relatively better than MADM methods.

In the area of financial stock market forecasting, many studies have focused on application of Artificial Neural Networks (ANNs). Due to its high rate of uncertainty and volatility, the stock markets returns forecasting by conventional methods became a difficult task. The ANNs is a relatively new and have been reported as good methods to predict financial stock market levels and can model flexible linear or non-linear relationship among variables. The aim of the study is to employ an ANN models to estimate and predict the dynamic volatility of the daily of S & P500 market returns. Results of ANN models will be compared with time series model using GARCH family models. The use of the novel model for conditional stock markets returns volatility can handle the vast amount of nonlinear data, simulate their relationship and give a moderate solution for the hard problem. The forecasts of stock index returns in the paper will be evaluated and compared, considering the MSE, RMSE and MAE forecasts statistic.

With ever-increasing demands for high surface finish and complex shape geometries on various difficult-to-machine materials, conventional metal removal methods are now being replaced by non-traditional machining (NTM) processes. These NTM processes use energy in its direct form to remove material from the workpiece surface. They are also cost effective for a wide range of micro- and nano-level applications. For effective utilization of different NTM processes, it is quite important to study their characteristics and material removal mechanisms in order to identify the most significant control parameters affecting the process responses. In this paper, a data mining approach using classification and regression tree algorithm is employed to identify the most important input parameters of three NTM processes, i.e. micro electro discharge milling process, wire electrical discharge machining process and laser beam machining process. The derived observations are also validated using the analysis of variance results.

Ranking the companies can be a useful guide for investors to select an optimum portfolio. Tehran stock exchange (TSE) uses liquidity criterion to rank the companies; however, this study shows that preferences of investors, the criteria they use to evaluate companies’ performances, and the extent to which ranking of companies based on investors’ criteria are in line with the ranking announced by the stock exchange. Since the criteria used for ranking the companies are various and often conflicting and because each multiple criteria technique has its own specific characteristics, various rankings are offered. Therefore, it is required to utilize multiple criteria decision making models to avoid confusion of investors. For this purpose, some companies were selected from 50 top companies listed in 2011 in TSE, which maintained the reliability of their ranks and finally, 20 companies were selected and were ranked based on investors’ criteria using EECTRE III Technique. The obtained ranking was then compared with the ranking offered by stock exchange. Research results indicate that ELECTRE III technique was a useful and efficient method to select a portfolio. Moreover, value-based criteria as well as accounting criteria are suitable and useful bases for investors to select a portfolio.

In the present paper, a novel intuitionistic fuzzy Multiple Attribute Decision Making (MADM) is proposed for modelling and solving analytical hierarchy process (AHP) problems with small amount of relationship among various criteria. Assigning a membership degree, fuzzy sets can model some uncertainty to the decision space. Intuitionistic fuzzy sets model the uncertainty more accurately associated with both membership and non-membership degree. Based on advantages of Intuitionistic fuzzy sets, this paper first uses IF-AHP to evaluate the weighting for each criterion and then develops an intuitionistic fuzzy DEMATEL method to establish contextual relationships among those criteria. Finally, an integrated IF-DEMATEL-AHP method is proposed and used for a case study for selecting managers in the automobile industry in Iran.

This paper analyzes the choice of a PISA selector for a Hybrid Algorithm integrating it as a Multi-Objective Evolutionary Algorithm (MOEA) with a path-dependent search algorithm. The interaction between these components provides an efficient procedure for solving Multi-Objective Problems (MOPs) in operations scheduling. In order to choose the selector, we consider both NSGA and SPEA as well as their successors (NSGAII and SPEAII). NSGAII and SPEAII are shown to be the most efficient candidates. On the other hand, for the path-dependent search at the end of each evolutionary phase we use the multi-objective version of Simulated Annealing.

Cell formation process is one of the first and the most important steps in designing cellular manufacturing systems. It consists of identifying part families according to the similarities in the design, shape, and presses of parts and dedicating machines to each part family based on the operations required by the parts. In this study, a hybrid method based on a combination of simulated annealing algorithm and dynamic programming was developed to solve a bi-objective cell formation problem with duplicate machines. In the proposed hybrid method, each solution was represented as a permutation of parts, which is created by simulated annealing algorithm, and dynamic programming was used to partition this permutation into part families and determine the number of machines in each cell such that the total dissimilarity between the parts and the total machine investment cost are minimized. The performance of the algorithm was evaluated by performing numerical experiments in different sizes. Our computational experiments indicated that the results were very encouraging in terms of computational time and solution quality.