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.

Location Allocation is one of the most important decision making problems, which attracted many operational researchers during recent decades and many solution procedures are developed so far to cope with this problem. This paper proposes a new graph theory based method to cope with small size capacitated location allocation problems. Additionally, a genetic algorithm is utilized to solve medium and large scale problems. Finally, through some computational experiments, the quality and capability of these algorithms are shown.

This study investigated the relationships between Big-Five personality traits and mate preferences in Isfahan, Iran. The sample consisted of 225 single girls and boys that they were volunteer to complete the research measures. They completed of Neo- FFI-3 to evaluate personality traits and Iranian cultural preference questionnaire. To response to research question, data was examined in two steps. The first step was to examine the intercorrelate relations matrix among dependent and independent variables. The results showed extraversion is related to attractive appearance, educational level, no having sexy relations with opposite sex in single period, and no having addiction. Also attractive appearance and no having too close relations with each other family is correlated with neuroticism and conscientiousness. Also extraversion can predict attractive appearance and no having addiction preferences. Neuroticism can predict no having too close relations with mate families preference. Other variables, although contributing to the overall variance, were not significant predictors in our focused and cultural preferences.

Genetic Algorithm is an algorithm based on population and many optimization problems are solved with this method, successfully. With increasing demand for computer attacks, security, efficient and reliable Internet has increased. Cryptographic systems have studied the science of communication is hidden, and includes two case categories including encryption, password and analysis. In this paper, several code analyses based on genetic algorithms, tabu search and simulated annealing for a permutation of encrypted text are investigated. The study also attempts to provide and to compare the performance in terms of the amount of check and control algorithms and the results are compared.

In this paper, a new robust approach is presented to handle demand uncertainty in cell formation and layout design process. Unlike the scenario based approaches, which use predefined scenarios to represent data uncertainty, in this paper, an interval approach is implemented to address data uncertainty for the part demands, which is more realistic and practical. The objective is to minimize the total inter- and intra-cell material handling cost. The proposed model gives machine cells and determines inter-and intra-cell layouts in such a way that the decision maker can control the robustness of the layout against the level of conservatism. An illustrative example is solved by CPLEX 10 to demonstrate the performance of the proposed method. The results reveal that when the level of conservatism is changed the optimal layout can vary, significantly.

This paper presents an empirical investigation to study the effect of market management using Markowitz theorem. The study uses the information of 50 best performers on Tehran Stock Exchange over the period 2006-2009 and, using Markowitz theorem, the efficient asset allocation are determined and the result are analyzed. The proposed model of this paper has been solved using genetic algorithm. The results indicate that Tehran Stock Exchange has managed to perform much better than average world market in most years of studies especially on year 2009. The results of our investigation have also indicated that one could reach outstanding results using GA and forming efficient portfolio.

Root cause failure analysis (RCFA) is a structured process to acknowledge cause and effect relationship of failure or unfavorable events in the organization in order to prevent repetition or reduction of failures. There are various tools for RCFA like interviewing, fault tree analysis (FTA), 5 whys, failure mode and effects analysis (FMEA), Pareto analysis and storytelling method with different strengths and weaknesses. In this paper, an integrated process model is developed using Reality Charting, FMEA and DEMATEL to understand and implement RCFA effectively. The proposed process model has eight main steps. The presented model in a case study for the UTD-20 engine is implemented and thus, in addition to the use of supporting research in model development, real data as well as the approval of the UTD-20 analysis team members are assisted to validate the proposed model.

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 study, to achieve a comprehensive view of pressure conditions in the Black Sea region, sea level pressure data in Reanalysis II database in National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) were used. The data temporal resolution is daily and the spatial resolution is 2.5×2.5 degrees of arc. The investigated framework covers the areas between -30-70 degrees east longitude and 20-70 degrees north latitude and has dimensions equal 41×21 pixels consisting of 861 pixels. The studied period of time is 33-year (1979 to 2012) and includes 12419 days and 861 spatial pixels. Therefore, sea level pressure data matrix is 12419×861, which has 12419 temporal pixels and 861 spatial ones. In other words, the arrangement of the data is S-shaped. The rows of the matrix represent time while columns represent space. At the end, using the cluster analysis, six sea level pressure circulation patterns based on the spatial - temporal features were obtained: Red Sea trough is fall pattern; Iraq trough is spring pattern; Persian Gulf trough is transitional pattern; Persian Gulf strong trough is summer pattern; Caucasian high pressure is fall - winter pattern and the Caucasian Strong high pressure is winter pattern. In most of these patterns, intensive allobaric conditions can be observed in the troughs and ridges.

In this paper, we develop a novel framework for defining radial measures of centrality in complex networks. This framework is based on the combination of two approaches: social network analysis and traditional social science approach by considering both structure of relations and individual characteristics. It is always an important issue to detect communities in complex networks as efficiently as possible to understand both the structure and function of the networks and to interpret radial centrality measures. Therefore, we propose spectral clustering by determining the best number of communities as a prerequisite stage before finding radial measures. Based on the proposed framework, an algorithm to compute the closeness centrality in complex networks is developed. We test the proposed algorithm on Zachary’s karate club network, which is considerably used as a benchmark for community detection in a network. The preliminary results indicate that the new method is efficient at detecting both good inter-cluster closeness centrality and the appropriate number of clusters.