In this paper, we present a new Imperialist Competitive Algorithm (ICA) to solve a bi-objective unrelated parallel machine scheduling problem where setup times are sequence dependent. The objectives include mean completion time of jobs and mean squares of deviations from machines workload from their averages. The performance of the proposed ICA (PICA) method is examined using some randomly generated data and they are compared with three alternative methods including particle swarm optimization (PSO), original version of imperialist competitive algorithm (OICA) and genetic algorithm (GA) in terms of the objective function values. The preliminary results indicate that the proposed study outperforms other alternative methods. In addition, while OICA performs the worst as alternative solution strategy, PSO and GA seem to perform better.

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.

This paper presents a scheme of implementation of the protocol for diagnosing stakeholders of educational space, which aims to meet the needs and interests of both contractors and the university, and makes an efficiency assessment in achieving the goals of the university. The ranking of the social status of the stakeholders in the association is obtained, which demonstrates the importance of each stakeholder in the collaboration. The relationships between university and stakeholders, the degree of communication of stakeholders with each other, the attitude of each stakeholder towards the group of stakeholders as a whole and the total activity of group interaction based on the calculation of sociometric indicators are determined. Finally, the effectiveness of the level of involvement of stakeholders in the activities of the University is also proved.

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.

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.

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.

Mobile payment is considered as a technology innovation which is being exploited and fur-ther expanded in both developed and developing or emerging countries. It is considered as an alternative method to cash payment method. This study is employed with the objective of exploring how Cambodian user behavior intention is affected by perceived transaction convenience, perceived transaction speed, social influences, and facilitating condition. Each fac-tor is measured by different items and there are 38 items developed in this study. The data is collected by distributing questionnaires to Cambodian users and 329 questionnaires are collected successfully.

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.