Due to the importance and complexity of the portfolio management issue, over 100 different techniques have already been presented. In general, the final result of these tools is to create a prioritized list of the projects that must be selected for allocating resources. The use of financial strategies may be misleading in some cases, and it is necessary to combine these methods with other methods such as strategic approaches in order to guarantee a balanced portfolio toward the organizational strategies. On the other, categorizing projects into different baskets allows the organizations to select, evaluate and prioritize the projects in a subset using a set of similar criteria and techniques. In this article, by choosing agriculture sector as a case study, an attempt has been made to study the evaluation, ranking and management of projects with investment classifying strategy of the projects using Delphi, TOPSIS and AHP methods. The results reveal that in similar cases we can use the presented model by determining the type of activity and investment and localization of the indexes.

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.

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.

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.