Forecasting stock returns and their risk represents one of the most important concerns of market ... more Forecasting stock returns and their risk represents one of the most important concerns of market decision makers. Although many studies have examined single classifiers of stock returns and risk methods, fusion methods, which have only recently emerged, require further study in this area. The main aim of this paper is to propose a fusion model based on the use of multiple diverse base classifiers that operate on a common input and a Meta classifier that learns from base classifiers' outputs to obtain more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting and Ad-aBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes is determined using a methodology based on dataset clustering and candidate classifiers' accuracy. The results demonstrate that Bagging exhibited superior performance within the fusion scheme and could achieve a maximum of 83.6% accuracy with Decision Tree, LAD Tree and Rep Tree for return prediction and 88.2% accuracy with BF Tree, DTNB and LAD Tree in risk prediction. For feature selection part, a wrapper-GA algorithm is developed and compared with the fusion model. This paper seeks to help researcher select the best individual classifiers and fuse the proper scheme in stock market prediction. To illustrate the approach, we apply it to Tehran Stock Exchange (TSE) data for the period from 2002 to 2012.
Keywords 1 ABSTRACT The main goal of FMS facility layout decision is to arrange the layout of the... more Keywords 1 ABSTRACT The main goal of FMS facility layout decision is to arrange the layout of the work stations to minimize the whole system cost. To meet this goal, in this paper a novel two phase method is presented For FMS problem with the consideration of unequal stations. In the first phase, a computer program is developed based on a Fuzzy AHP approach, for considering the quantitative and qualitative factors among workstations, stored on a knowledge base, to obtain the amount of the material flow between each two stations. Thereafter in the second phase, we optimize the FMS facility layout by utilizing the amount of material flow between stations (obtained in the first phase) by GA and ICA algorithms. Since the transportation system has the most effect on the FMS design, an AGV algorithm is used in order to increase the FMS efficiency and flexibility. Comparing the results of the ICA (first used in facility layout) to the GA algorithm, shows that the two algorithms are similar regarding power, however the ICA algorithm improves much faster.
The main goal of FMS facility layout decision is to arrange the layout of the work stations to mi... more The main goal of FMS facility layout decision is to arrange the layout of the work stations to minimize the whole system cost. To meet this goal, in this paper a novel two phase method is presented For FMS problem with the consideration of unequal stations. In the first phase, a computer program is developed based on a Fuzzy AHP approach, for considering the quantitative and qualitative factors among workstations, stored on a knowledge base, to obtain the amount of the material flow between each two stations. Thereafter in the second phase, we optimize the FMS facility layout by utilizing the amount of material flow between stations (obtained in the first phase) by GA and ICA algorithms. Since the transportation system has the most effect on the FMS design, an AGV algorithm is used in order to increase the FMS efficiency and flexibility. Comparing the results of the ICA (first used in facility layout) to the GA algorithm, shows that the two algorithms are similar regarding power, ho...
Technological and Economic Development of Economy, 2015
In today's competitive environment, holding companies are usually unable to successfully compete ... more In today's competitive environment, holding companies are usually unable to successfully compete in production of goods and services due to technological sophistication. Therefore, for success of holding companies, selecting appropriate strategic alliance partner is a critical factor. Accordingly, the aim of the paper is to propose a systematic approach for an effective partner selection. Firstly, the underlying motivation and reasons for a strategic alliance building are presented using a SWOT analysis. Criteria of partners' evaluation are attained on the basis of combining strengths, weaknesses, opportunities and threats. Due to uncertainty of criteria, they are weighted using fuzzy quantitative strategic planning matrix (FQSPM). Because of a large number of criteria obtained from the SWOT-FQSPM analysis, criteria are diminished based on their weights using the Gap analysis with fuzzy data ranking. In the next step, it is proposed to apply four ranking algorithms including the Fuzzy Additive Ratio Assessment (ARAS-F), the Fuzzy Complex Proportional Assessment (COPRAS-F), the Fuzzy Multi-Objective Optimization by Ratio Analysis (Fuzzy MOORA), and the Fuzzy Technique for Order Preference by Similarity to Ideal solution (Fuzzy TOPSIS) to evaluate strategic partners. Finally, the results are combined with the help of the Borda method to choose the best alternative. To illustrate the efficiency of the proposed approach, a real partner selection problem at a holding industries factory in Iran is presented.
Energy consumption is on the rise in developing economies. In order to improve present and future... more Energy consumption is on the rise in developing economies. In order to improve present and future energy supplies, forecasting energy demands is essential. However, lack of accurate and comprehensive data set to predict the future demand is one of big problems in these countries. Therefore, using ensemble hybrid forecasting models that can deal with shortage of data set could be a suitable solution. In this paper, the annual energy consumption in Iran is forecasted using 3 patterns of ARIMA–ANFIS model. In the first pattern, ARIMA (Auto Regressive Integrated Moving Average) model is implemented on 4 input features, where its nonlinear residuals are forecasted by 6 different ANFIS (Adaptive Neuro Fuzzy Inference System) structures including grid partitioning, sub clustering, and fuzzy c means clustering (each with 2 training algorithms). In the second pattern, the forecasting of ARIMA in addition to 4 input features is assumed as input variables for ANFIS prediction. Therefore, four mentioned inputs beside ARIMA's output are used in energy prediction with 6 different ANFIS structures. In the third pattern, due to dealing with data insufficiency, the second pattern is applied with AdaBoost (Adaptive Boosting) data diversification model and a novel ensemble methodology is presented. The results indicate that proposed hybrid patterns improve the accuracy of single ARIMA and ANFIS models in forecasting energy consumption, though third pattern, used diversification model, acts better than others and model's MSE criterion was decreased to 0.026% from 0.058% of second hybrid pattern. Finally, a comprehensive comparison between other hybrid prediction models is done.
Predicting stock prices is an important objective in the financial world. This paper presents a n... more Predicting stock prices is an important objective in the financial world. This paper presents a novel forecasting model for stock markets on the basis of the wrapper ANFIS (Adaptive Neural Fuzzy Inference System)-ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick. Two approaches of Raw-based and Signal-based are devised to extract the model's input variables with 15 and 24 features, respectively. The correct predictions percentages for periods of 1-6 days with the total number of buy and sell signals are considered as output variables. In proposed model, the ANFIS prediction results are used as a cost function of wrapper model and ICA is used to select the most appropriate features. This novel combination of feature selection not only takes advantage of ICA optimization swiftness, but also the ANFIS prediction accuracy. The emitted buy and sell signals of the model revealed that Signal databases approach gets better results with 87% prediction accuracy and the wrapper features selection obtains 12% improvement in predictive performance regarding to the base study. In addition, since the wrapper-based feature selection models are considerably more time-consuming, our presented wrapper ANFIS-ICA algorithm's results have superiority in time decreasing as well as increasing prediction accuracy as compared to other algorithms such as wrapper Genetic algorithm (GA).
In the recent centuries, one of the most important ongoing challenges is energy consumption and i... more In the recent centuries, one of the most important ongoing challenges is energy consumption and its environmental impacts. As far as agriculture is concerned, it has a key role in the world economics and a great amount of energy from different sources is used in this sector. Since researchers have reported a high degree of inefficiency in developing countries, it is necessary for the modern management of cropping systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization (MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results. Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study.
Energy use management
Farm management
Environmental impacts
Artificial intelligent
Imperialist co... more Energy use management Farm management Environmental impacts Artificial intelligent Imperialist competitive algorithm
In this research, a novel approach is developed to predict stocks return and risks. In this three... more In this research, a novel approach is developed to predict stocks return and risks. In this three stage
method, through a comprehensive investigation all possible features which can be effective on stocks risk
and return are identified. Then, in the next stage risk and return are predicted by applying data mining
techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function-
based clustering; the important features in risk and return prediction are selected then risk and
return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature
selection and these features are good indicators for the prediction of risk and return. To illustrate the
approach as well as to train data and test, we apply it to Tehran Stock Exchange (TSE) data from 2002
to 2011.
International Journal of Applied Operational Research, Jul 2012
Regarding the fact that getting a suitable combination of the human resources and service
statio... more Regarding the fact that getting a suitable combination of the human resources and service
stations is one of the important issues in the most service and manufacturing environments, In this
paper, we have studied the two models of planning queuing systems and its effect on the cost of the
each system by using two fuzzy queuing models of M/M/1 and M/E2/1. In the first section, we have
compared two different fuzzy queuing models based on the costs of each model and fuzzy ranking
methods are used to select optimal model due to the resulted complexity. This paper results in a new
approach for comparing different queuing models in the fuzzy environment (regarding the obtained
data from the real conditions) that it can be more effective than deterministic queuing models. Also a
sensitivity analysis is carried out to help the decision maker in selecting the optimal model.
International Journal of Production Research, 2012
The problem of part supplier selection is a major concern for all manufacturers when seeking to e... more The problem of part supplier selection is a major concern for all manufacturers when seeking to enhance the
products’ quality and productivity. The objective of this paper is to propose an integrated genetic algorithm
based grey goal programming (G3) approach to solve the part supplier selection problem. The main factor in
part supplier selection is the assembly relation of the parts so as to find the suitable suppliers combination for
the parts of a product. We first identify the main factors affected on supplier selection. We then present a grey based
goal programming model to work as the fitness function to evaluate the suppliers with respect to the
total deviation the factors have from the ideal values. Since the objective is to find the best solution, a genetic
algorithm is used to solve this problem for faster and better evaluation. The novelty of this integrated
approach is to apply both qualitative and quantitative factors at once in one model and to use the grey theory
to cover the lack of information of qualitative factors in order to find a solution in a near real situation.
One concern of many investors is to own the assets which can be liquidated easily. Thus, in this ... more One concern of many investors is to own the assets which can be liquidated easily. Thus, in this paper, we incorporate portfolio liquidity in our proposed model. Liquidity is measured by an index called turnover rate. Since the return of an asset is uncertain, we present it as a trapezoidal fuzzy number and its turnover rate is measured by fuzzy credibility theory. The desired portfolio turnover rate is controlled through a fuzzy chance constraint. Furthermore, to manage the portfolios with asymmetric investment return, other than mean and variance, we also utilize the third central moment, the skewness of portfolio return. In fact, we propose a fuzzy portfolio mean–variance–skewness model with cardinality constraint which combines assets limitations with liquidity requirement. To solve the model, we also develop a hybrid algorithm which is the combination of cardinality constraint, genetic algorithm, and fuzzy simulation, called FCTPM.
Forecasting stock returns and their risk represents one of the most important concerns of market ... more Forecasting stock returns and their risk represents one of the most important concerns of market decision makers. Although many studies have examined single classifiers of stock returns and risk methods, fusion methods, which have only recently emerged, require further study in this area. The main aim of this paper is to propose a fusion model based on the use of multiple diverse base classifiers that operate on a common input and a Meta classifier that learns from base classifiers' outputs to obtain more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting and Ad-aBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes is determined using a methodology based on dataset clustering and candidate classifiers' accuracy. The results demonstrate that Bagging exhibited superior performance within the fusion scheme and could achieve a maximum of 83.6% accuracy with Decision Tree, LAD Tree and Rep Tree for return prediction and 88.2% accuracy with BF Tree, DTNB and LAD Tree in risk prediction. For feature selection part, a wrapper-GA algorithm is developed and compared with the fusion model. This paper seeks to help researcher select the best individual classifiers and fuse the proper scheme in stock market prediction. To illustrate the approach, we apply it to Tehran Stock Exchange (TSE) data for the period from 2002 to 2012.
Keywords 1 ABSTRACT The main goal of FMS facility layout decision is to arrange the layout of the... more Keywords 1 ABSTRACT The main goal of FMS facility layout decision is to arrange the layout of the work stations to minimize the whole system cost. To meet this goal, in this paper a novel two phase method is presented For FMS problem with the consideration of unequal stations. In the first phase, a computer program is developed based on a Fuzzy AHP approach, for considering the quantitative and qualitative factors among workstations, stored on a knowledge base, to obtain the amount of the material flow between each two stations. Thereafter in the second phase, we optimize the FMS facility layout by utilizing the amount of material flow between stations (obtained in the first phase) by GA and ICA algorithms. Since the transportation system has the most effect on the FMS design, an AGV algorithm is used in order to increase the FMS efficiency and flexibility. Comparing the results of the ICA (first used in facility layout) to the GA algorithm, shows that the two algorithms are similar regarding power, however the ICA algorithm improves much faster.
The main goal of FMS facility layout decision is to arrange the layout of the work stations to mi... more The main goal of FMS facility layout decision is to arrange the layout of the work stations to minimize the whole system cost. To meet this goal, in this paper a novel two phase method is presented For FMS problem with the consideration of unequal stations. In the first phase, a computer program is developed based on a Fuzzy AHP approach, for considering the quantitative and qualitative factors among workstations, stored on a knowledge base, to obtain the amount of the material flow between each two stations. Thereafter in the second phase, we optimize the FMS facility layout by utilizing the amount of material flow between stations (obtained in the first phase) by GA and ICA algorithms. Since the transportation system has the most effect on the FMS design, an AGV algorithm is used in order to increase the FMS efficiency and flexibility. Comparing the results of the ICA (first used in facility layout) to the GA algorithm, shows that the two algorithms are similar regarding power, ho...
Technological and Economic Development of Economy, 2015
In today's competitive environment, holding companies are usually unable to successfully compete ... more In today's competitive environment, holding companies are usually unable to successfully compete in production of goods and services due to technological sophistication. Therefore, for success of holding companies, selecting appropriate strategic alliance partner is a critical factor. Accordingly, the aim of the paper is to propose a systematic approach for an effective partner selection. Firstly, the underlying motivation and reasons for a strategic alliance building are presented using a SWOT analysis. Criteria of partners' evaluation are attained on the basis of combining strengths, weaknesses, opportunities and threats. Due to uncertainty of criteria, they are weighted using fuzzy quantitative strategic planning matrix (FQSPM). Because of a large number of criteria obtained from the SWOT-FQSPM analysis, criteria are diminished based on their weights using the Gap analysis with fuzzy data ranking. In the next step, it is proposed to apply four ranking algorithms including the Fuzzy Additive Ratio Assessment (ARAS-F), the Fuzzy Complex Proportional Assessment (COPRAS-F), the Fuzzy Multi-Objective Optimization by Ratio Analysis (Fuzzy MOORA), and the Fuzzy Technique for Order Preference by Similarity to Ideal solution (Fuzzy TOPSIS) to evaluate strategic partners. Finally, the results are combined with the help of the Borda method to choose the best alternative. To illustrate the efficiency of the proposed approach, a real partner selection problem at a holding industries factory in Iran is presented.
Energy consumption is on the rise in developing economies. In order to improve present and future... more Energy consumption is on the rise in developing economies. In order to improve present and future energy supplies, forecasting energy demands is essential. However, lack of accurate and comprehensive data set to predict the future demand is one of big problems in these countries. Therefore, using ensemble hybrid forecasting models that can deal with shortage of data set could be a suitable solution. In this paper, the annual energy consumption in Iran is forecasted using 3 patterns of ARIMA–ANFIS model. In the first pattern, ARIMA (Auto Regressive Integrated Moving Average) model is implemented on 4 input features, where its nonlinear residuals are forecasted by 6 different ANFIS (Adaptive Neuro Fuzzy Inference System) structures including grid partitioning, sub clustering, and fuzzy c means clustering (each with 2 training algorithms). In the second pattern, the forecasting of ARIMA in addition to 4 input features is assumed as input variables for ANFIS prediction. Therefore, four mentioned inputs beside ARIMA's output are used in energy prediction with 6 different ANFIS structures. In the third pattern, due to dealing with data insufficiency, the second pattern is applied with AdaBoost (Adaptive Boosting) data diversification model and a novel ensemble methodology is presented. The results indicate that proposed hybrid patterns improve the accuracy of single ARIMA and ANFIS models in forecasting energy consumption, though third pattern, used diversification model, acts better than others and model's MSE criterion was decreased to 0.026% from 0.058% of second hybrid pattern. Finally, a comprehensive comparison between other hybrid prediction models is done.
Predicting stock prices is an important objective in the financial world. This paper presents a n... more Predicting stock prices is an important objective in the financial world. This paper presents a novel forecasting model for stock markets on the basis of the wrapper ANFIS (Adaptive Neural Fuzzy Inference System)-ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick. Two approaches of Raw-based and Signal-based are devised to extract the model's input variables with 15 and 24 features, respectively. The correct predictions percentages for periods of 1-6 days with the total number of buy and sell signals are considered as output variables. In proposed model, the ANFIS prediction results are used as a cost function of wrapper model and ICA is used to select the most appropriate features. This novel combination of feature selection not only takes advantage of ICA optimization swiftness, but also the ANFIS prediction accuracy. The emitted buy and sell signals of the model revealed that Signal databases approach gets better results with 87% prediction accuracy and the wrapper features selection obtains 12% improvement in predictive performance regarding to the base study. In addition, since the wrapper-based feature selection models are considerably more time-consuming, our presented wrapper ANFIS-ICA algorithm's results have superiority in time decreasing as well as increasing prediction accuracy as compared to other algorithms such as wrapper Genetic algorithm (GA).
In the recent centuries, one of the most important ongoing challenges is energy consumption and i... more In the recent centuries, one of the most important ongoing challenges is energy consumption and its environmental impacts. As far as agriculture is concerned, it has a key role in the world economics and a great amount of energy from different sources is used in this sector. Since researchers have reported a high degree of inefficiency in developing countries, it is necessary for the modern management of cropping systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization (MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results. Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study.
Energy use management
Farm management
Environmental impacts
Artificial intelligent
Imperialist co... more Energy use management Farm management Environmental impacts Artificial intelligent Imperialist competitive algorithm
In this research, a novel approach is developed to predict stocks return and risks. In this three... more In this research, a novel approach is developed to predict stocks return and risks. In this three stage
method, through a comprehensive investigation all possible features which can be effective on stocks risk
and return are identified. Then, in the next stage risk and return are predicted by applying data mining
techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function-
based clustering; the important features in risk and return prediction are selected then risk and
return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature
selection and these features are good indicators for the prediction of risk and return. To illustrate the
approach as well as to train data and test, we apply it to Tehran Stock Exchange (TSE) data from 2002
to 2011.
International Journal of Applied Operational Research, Jul 2012
Regarding the fact that getting a suitable combination of the human resources and service
statio... more Regarding the fact that getting a suitable combination of the human resources and service
stations is one of the important issues in the most service and manufacturing environments, In this
paper, we have studied the two models of planning queuing systems and its effect on the cost of the
each system by using two fuzzy queuing models of M/M/1 and M/E2/1. In the first section, we have
compared two different fuzzy queuing models based on the costs of each model and fuzzy ranking
methods are used to select optimal model due to the resulted complexity. This paper results in a new
approach for comparing different queuing models in the fuzzy environment (regarding the obtained
data from the real conditions) that it can be more effective than deterministic queuing models. Also a
sensitivity analysis is carried out to help the decision maker in selecting the optimal model.
International Journal of Production Research, 2012
The problem of part supplier selection is a major concern for all manufacturers when seeking to e... more The problem of part supplier selection is a major concern for all manufacturers when seeking to enhance the
products’ quality and productivity. The objective of this paper is to propose an integrated genetic algorithm
based grey goal programming (G3) approach to solve the part supplier selection problem. The main factor in
part supplier selection is the assembly relation of the parts so as to find the suitable suppliers combination for
the parts of a product. We first identify the main factors affected on supplier selection. We then present a grey based
goal programming model to work as the fitness function to evaluate the suppliers with respect to the
total deviation the factors have from the ideal values. Since the objective is to find the best solution, a genetic
algorithm is used to solve this problem for faster and better evaluation. The novelty of this integrated
approach is to apply both qualitative and quantitative factors at once in one model and to use the grey theory
to cover the lack of information of qualitative factors in order to find a solution in a near real situation.
One concern of many investors is to own the assets which can be liquidated easily. Thus, in this ... more One concern of many investors is to own the assets which can be liquidated easily. Thus, in this paper, we incorporate portfolio liquidity in our proposed model. Liquidity is measured by an index called turnover rate. Since the return of an asset is uncertain, we present it as a trapezoidal fuzzy number and its turnover rate is measured by fuzzy credibility theory. The desired portfolio turnover rate is controlled through a fuzzy chance constraint. Furthermore, to manage the portfolios with asymmetric investment return, other than mean and variance, we also utilize the third central moment, the skewness of portfolio return. In fact, we propose a fuzzy portfolio mean–variance–skewness model with cardinality constraint which combines assets limitations with liquidity requirement. To solve the model, we also develop a hybrid algorithm which is the combination of cardinality constraint, genetic algorithm, and fuzzy simulation, called FCTPM.
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Papers by Sasan Barak
systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization
(MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective
evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results.
Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study.
Farm management
Environmental impacts
Artificial intelligent
Imperialist competitive algorithm
method, through a comprehensive investigation all possible features which can be effective on stocks risk
and return are identified. Then, in the next stage risk and return are predicted by applying data mining
techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function-
based clustering; the important features in risk and return prediction are selected then risk and
return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature
selection and these features are good indicators for the prediction of risk and return. To illustrate the
approach as well as to train data and test, we apply it to Tehran Stock Exchange (TSE) data from 2002
to 2011.
stations is one of the important issues in the most service and manufacturing environments, In this
paper, we have studied the two models of planning queuing systems and its effect on the cost of the
each system by using two fuzzy queuing models of M/M/1 and M/E2/1. In the first section, we have
compared two different fuzzy queuing models based on the costs of each model and fuzzy ranking
methods are used to select optimal model due to the resulted complexity. This paper results in a new
approach for comparing different queuing models in the fuzzy environment (regarding the obtained
data from the real conditions) that it can be more effective than deterministic queuing models. Also a
sensitivity analysis is carried out to help the decision maker in selecting the optimal model.
products’ quality and productivity. The objective of this paper is to propose an integrated genetic algorithm
based grey goal programming (G3) approach to solve the part supplier selection problem. The main factor in
part supplier selection is the assembly relation of the parts so as to find the suitable suppliers combination for
the parts of a product. We first identify the main factors affected on supplier selection. We then present a grey based
goal programming model to work as the fitness function to evaluate the suppliers with respect to the
total deviation the factors have from the ideal values. Since the objective is to find the best solution, a genetic
algorithm is used to solve this problem for faster and better evaluation. The novelty of this integrated
approach is to apply both qualitative and quantitative factors at once in one model and to use the grey theory
to cover the lack of information of qualitative factors in order to find a solution in a near real situation.
systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization
(MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective
evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results.
Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study.
Farm management
Environmental impacts
Artificial intelligent
Imperialist competitive algorithm
method, through a comprehensive investigation all possible features which can be effective on stocks risk
and return are identified. Then, in the next stage risk and return are predicted by applying data mining
techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function-
based clustering; the important features in risk and return prediction are selected then risk and
return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature
selection and these features are good indicators for the prediction of risk and return. To illustrate the
approach as well as to train data and test, we apply it to Tehran Stock Exchange (TSE) data from 2002
to 2011.
stations is one of the important issues in the most service and manufacturing environments, In this
paper, we have studied the two models of planning queuing systems and its effect on the cost of the
each system by using two fuzzy queuing models of M/M/1 and M/E2/1. In the first section, we have
compared two different fuzzy queuing models based on the costs of each model and fuzzy ranking
methods are used to select optimal model due to the resulted complexity. This paper results in a new
approach for comparing different queuing models in the fuzzy environment (regarding the obtained
data from the real conditions) that it can be more effective than deterministic queuing models. Also a
sensitivity analysis is carried out to help the decision maker in selecting the optimal model.
products’ quality and productivity. The objective of this paper is to propose an integrated genetic algorithm
based grey goal programming (G3) approach to solve the part supplier selection problem. The main factor in
part supplier selection is the assembly relation of the parts so as to find the suitable suppliers combination for
the parts of a product. We first identify the main factors affected on supplier selection. We then present a grey based
goal programming model to work as the fitness function to evaluate the suppliers with respect to the
total deviation the factors have from the ideal values. Since the objective is to find the best solution, a genetic
algorithm is used to solve this problem for faster and better evaluation. The novelty of this integrated
approach is to apply both qualitative and quantitative factors at once in one model and to use the grey theory
to cover the lack of information of qualitative factors in order to find a solution in a near real situation.