2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), 2015
To make a policy and decision for an appropriate set of optimizer algorithms is an important and ... more To make a policy and decision for an appropriate set of optimizer algorithms is an important and controversial issue. It is significant especially when we want to consider more than a single objective and have to use multi-objective applications. The aim of this paper is to consider procedural fuzzy approximate reasoning to infer which one of the Multi-Objective Evolutionary Algorithms (MOEAs) could play a role in the suitable set as prevalent tool. The proposed procedure is put into practice for an invented bi-objective programming in the supply chain and three numbers of similar applications from the same family, i.e. NSGA-II, NRGA, and PESA-II are deployed.
In the very few recently published paper by Alavidoost et al. [1], they proposed a novel fuzzy ad... more In the very few recently published paper by Alavidoost et al. [1], they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2], as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption.
Supply chain network designing and programming is a momentous issue that many practitioners have ... more Supply chain network designing and programming is a momentous issue that many practitioners have focused on and contributed numerous novelties for this prompt. This paper puts forward a fuzzy multi-agent system according to which compatible with the decision makers’ interests and environmental survey, identifies the parameters of the mathematical model. An embedded optimization party including evolutionary-based optimizer intelligent agents, obtains non-dominated potential solutions. The output of these optimizer agents during the calibration process is an underpinning for evaluating the performance of the party. The system makes the policy of optimization complying with the results evaluation as well as the decision makers’ elaborated desires. Afterwards, in step with this policy, it sets a pool from obtained Pareto Fronts and aggregates them to extract a set of the best individuals. It interactively represents this set to the decision makers and catches their desired circumstance amongst these optional solutions. Proposing the network graph and program—which its generic morphography is determined—for decision makers is contrived as the system last stage. The main competencies of this system could be contemplated regarding the facts that it interactively fulfills the decision makers’ utilities relying on its robustness in optimization, self-tuning, training loop, ambient intelligence and consciousness toward the changes in environment.
Through the years, the ability to predict the future trend of financial time series has drawn ser... more Through the years, the ability to predict the future trend of financial time series has drawn serious attention from both researchers and practitioners aiming to have better investment decisions. In this paper a fuzzy rule-based expert system is developed for predicting stock price movement. The importance of the proposed expert system is that it would be applicable for stock market's speculators and traders' daily transactions. For the experiment and in order to demonstrate the effectiveness of the model, the stock price of Apple Company is used as a sample data set.
Comparison and ranking of evolutionary applications, especially the multi-objective ones, is a pr... more Comparison and ranking of evolutionary applications, especially the multi-objective ones, is a prevalent method in the literature for benchmarking, validation, and etc. It usually refers to the inquiries on how it should be carried out and what indexes for which one of the applications could be used. The aim of this paper is to propose a Fuzzy Comparison Dashboard (named FCD) for the applications which are based on the evolutionary algorithms to prepare a reliable perception for those who want to deploy them and judge or select one. The proposed FCD is put into practice for an invented bi-objective problem in supply chain planning and is solved with three numbers of similar Multi-Objective Evolutionary Algorithms (MOEAs) from the same family, i.e. NSGA-II, NRGA, and PESAII.
Keeping Information Systems (IS) aligned with the corporations' strategy plan is a controversial ... more Keeping Information Systems (IS) aligned with the corporations' strategy plan is a controversial issue. The aim of this paper is to prepare an expert system based on intelligent softbots and prepare and propose a portfolio of IS that has the maximum alignment with the strategy plan. Attaining such an issue, an expert system, which emerges through the implementation of a credible methodology, is proposed. The fuzziness of such an expert system is embedded in its data supply agent which prepares necessary information through an environmental survey.
To make a policy and decision for an appropriate set of optimizer algorithms is an important and ... more To make a policy and decision for an appropriate set of optimizer algorithms is an important and controversial issue. It is significant especially when we want to consider more than a single objective and have to use multi-objective applications. The aim of this paper is to consider procedural fuzzy approximate reasoning to infer which one of the Multi-Objective Evolutionary Algorithms (MOEAs) could play a role in the suitable set as prevalent tool. The proposed procedure is put into practice for an invented bi-objective programming in the supply chain and three numbers of similar applications from the same family, i.e. NSGA-II, NRGA, and PESA-II are deployed.
The competitive market and declined economy have increased the relevant importance of making supp... more The competitive market and declined economy have increased the relevant importance of making supply chain network efficient. Up to now, this has resulted in great motivations to reduce the cost of services, and simultaneously, to improve their quality. A mere network model, as a tri-echelon, consists of Suppliers, Warehouses or Distribution Centers (DCs), and Retailer nodes. To bring it closer to reality, the majority of parameters in this network involve retailer demands, lead-time, warehouses holding and shipment costs, and also suppliers procuring and stocking costs which are all assumed to be stochastic. The aim is to determine the optimum service level so that total cost is minimized. Obtaining such conditions requires determining which supplier nodes, and which DC nodes in network should be active to satisfy the retailers’ needs, an issue which is a network optimization problem per se. The proposed supply chain network for this paper is formulated as a mixed-integer nonlinear programming, and to solve this complicated problem, since the literature for the related benchmark is poor, three numbers of genetic algorithm called Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Ranking Genetic Algorithm (NRGA), and Pareto Envelope-based Selection Algorithm (PESA-II) are applied and compared to validate the obtained results. The Taguchi method is also utilized for calibrating and controlling the parameters of the applied triple algorithms.
The competitive market and declined economy have increased the relevant importance of making su... more The competitive market and declined economy have increased the relevant importance of making supply chain network efficient. Up to now, this has resulted in great motivations to reduce the cost of services, and simultaneously, to improve their quality. A mere network model, as a tri-echelon, consists of Suppliers, Warehouses or Distribution Centers (DCs), and Retailer nodes. To bring it closer to reality, the majority of parameters in this network involve retailer demands, lead-time, warehouses holding and shipment costs, and also suppliers procuring and stocking costs which are all assumed to be stochastic. The aim is to determine the optimum service level so that total cost is minimized. Obtaining such conditions requires determining which supplier nodes, and which DC nodes in network should be active to satisfy the retailers’ needs, an issue which is a network optimization problem per se. The proposed supply chain network for this paper is formulated as a mixed-integer nonlinear programming, and to solve this complicated problem, since the literature for the related benchmark is poor, three numbers of genetic algorithm called Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Ranking Genetic Algorithm (NRGA), and Pareto Envelope-based Selection Algorithm (PESA-II) are applied and compared to validate the obtained results. The Taguchi method is also utilized for calibrating and controlling the parameters of the applied triple algorithms.
Nowadays attaining the general and comprehensive information about customers by means of traditio... more Nowadays attaining the general and comprehensive information about customers by means of traditional methods is difficult for CEO's because of the agility and complexity of organizations. So they spend a considerable time to gather and analyze the market data and consider it according to the organization's strategy. Presenting a useful architecture that capable to diagnose the organization's advantages and disadvantages, and identify the attainable competitive advantages are the main goals of this paper. The output of such architecture can be a general exhibition of company that prepares a clear and on time comprehensive view for CEO's.
This paper aims at the straight and U-shaped assembly line balancing. Due to the uncertainty, var... more This paper aims at the straight and U-shaped assembly line balancing. Due to the uncertainty, variability and imprecision in actual production systems, the processing time of tasks are presented in triangular fuzzy numbers. In this case, it is intended to optimize the efficiency and idleness percentage of the assembly line as well as and concurrently with minimizing the number of workstations. To solve the problem, a modified genetic algorithm is proposed. One-fifth success rule in selection operator to improve the genetic algorithm performance. This leads genetic algorithm being controlled in convergence and diversity simultaneously by the means of controlling the selective pressure. Also a fuzzy controller in selective pressure employed for one-fifth success rule better implementation in genetic algorithm. In addition, Taguchi design of experiments used for parameter control and calibration. Finally, numerical examples are presented to compare the performance of proposed method with existing ones. Results show the high performance of the proposed algorithm.
This paper aims at multi-objective straight and U-shaped assembly line balancing problems with th... more This paper aims at multi-objective straight and U-shaped assembly line balancing problems with the fuzzy task processing times. The problems are referred to herein as f-SALBP and f-SULBP and the objectives that are considered to be satisfied are: (a) minimizing the numbers of stations, (b) maximizing the fuzzy line efficiency, (c) minimizing the fuzzy idleness percentage, and (d) minimizing the fuzzy smoothness index. In fact, the f-SALBP and f-SULBP are SALBP and SULBP generalization in fuzzy circumstance, respectively. Initially, the two problems are formulated and due to the uncertainty, variability and imprecision that often occurred in real-world production systems, the processing time of tasks are supposed as triangular fuzzy numbers. Then, to solve the problem, a hybrid multi-objective genetic algorithm is proposed. A One-Fifth Success Rule (OFSR) is deployed for the selection and mutation operators to improve the genetic algorithm's performance. The results in the genetic algorithm are being controlled in convergence and diversity simultaneously by means of controlling the selective pressure (SP) and mutation rate. Likewise, a fuzzy controller to SP is employed for the OFSR toward a better implementation of the genetic algorithm. In addition, the Taguchi design of experiments is used for parameter control and calibration. Finally, the numerical examples are presented to compare the performance of the proposed method with the existing ones. The results show significantly better performance for the proposed algorithm.
2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), 2015
To make a policy and decision for an appropriate set of optimizer algorithms is an important and ... more To make a policy and decision for an appropriate set of optimizer algorithms is an important and controversial issue. It is significant especially when we want to consider more than a single objective and have to use multi-objective applications. The aim of this paper is to consider procedural fuzzy approximate reasoning to infer which one of the Multi-Objective Evolutionary Algorithms (MOEAs) could play a role in the suitable set as prevalent tool. The proposed procedure is put into practice for an invented bi-objective programming in the supply chain and three numbers of similar applications from the same family, i.e. NSGA-II, NRGA, and PESA-II are deployed.
In the very few recently published paper by Alavidoost et al. [1], they proposed a novel fuzzy ad... more In the very few recently published paper by Alavidoost et al. [1], they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2], as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption.
Supply chain network designing and programming is a momentous issue that many practitioners have ... more Supply chain network designing and programming is a momentous issue that many practitioners have focused on and contributed numerous novelties for this prompt. This paper puts forward a fuzzy multi-agent system according to which compatible with the decision makers’ interests and environmental survey, identifies the parameters of the mathematical model. An embedded optimization party including evolutionary-based optimizer intelligent agents, obtains non-dominated potential solutions. The output of these optimizer agents during the calibration process is an underpinning for evaluating the performance of the party. The system makes the policy of optimization complying with the results evaluation as well as the decision makers’ elaborated desires. Afterwards, in step with this policy, it sets a pool from obtained Pareto Fronts and aggregates them to extract a set of the best individuals. It interactively represents this set to the decision makers and catches their desired circumstance amongst these optional solutions. Proposing the network graph and program—which its generic morphography is determined—for decision makers is contrived as the system last stage. The main competencies of this system could be contemplated regarding the facts that it interactively fulfills the decision makers’ utilities relying on its robustness in optimization, self-tuning, training loop, ambient intelligence and consciousness toward the changes in environment.
Through the years, the ability to predict the future trend of financial time series has drawn ser... more Through the years, the ability to predict the future trend of financial time series has drawn serious attention from both researchers and practitioners aiming to have better investment decisions. In this paper a fuzzy rule-based expert system is developed for predicting stock price movement. The importance of the proposed expert system is that it would be applicable for stock market's speculators and traders' daily transactions. For the experiment and in order to demonstrate the effectiveness of the model, the stock price of Apple Company is used as a sample data set.
Comparison and ranking of evolutionary applications, especially the multi-objective ones, is a pr... more Comparison and ranking of evolutionary applications, especially the multi-objective ones, is a prevalent method in the literature for benchmarking, validation, and etc. It usually refers to the inquiries on how it should be carried out and what indexes for which one of the applications could be used. The aim of this paper is to propose a Fuzzy Comparison Dashboard (named FCD) for the applications which are based on the evolutionary algorithms to prepare a reliable perception for those who want to deploy them and judge or select one. The proposed FCD is put into practice for an invented bi-objective problem in supply chain planning and is solved with three numbers of similar Multi-Objective Evolutionary Algorithms (MOEAs) from the same family, i.e. NSGA-II, NRGA, and PESAII.
Keeping Information Systems (IS) aligned with the corporations' strategy plan is a controversial ... more Keeping Information Systems (IS) aligned with the corporations' strategy plan is a controversial issue. The aim of this paper is to prepare an expert system based on intelligent softbots and prepare and propose a portfolio of IS that has the maximum alignment with the strategy plan. Attaining such an issue, an expert system, which emerges through the implementation of a credible methodology, is proposed. The fuzziness of such an expert system is embedded in its data supply agent which prepares necessary information through an environmental survey.
To make a policy and decision for an appropriate set of optimizer algorithms is an important and ... more To make a policy and decision for an appropriate set of optimizer algorithms is an important and controversial issue. It is significant especially when we want to consider more than a single objective and have to use multi-objective applications. The aim of this paper is to consider procedural fuzzy approximate reasoning to infer which one of the Multi-Objective Evolutionary Algorithms (MOEAs) could play a role in the suitable set as prevalent tool. The proposed procedure is put into practice for an invented bi-objective programming in the supply chain and three numbers of similar applications from the same family, i.e. NSGA-II, NRGA, and PESA-II are deployed.
The competitive market and declined economy have increased the relevant importance of making supp... more The competitive market and declined economy have increased the relevant importance of making supply chain network efficient. Up to now, this has resulted in great motivations to reduce the cost of services, and simultaneously, to improve their quality. A mere network model, as a tri-echelon, consists of Suppliers, Warehouses or Distribution Centers (DCs), and Retailer nodes. To bring it closer to reality, the majority of parameters in this network involve retailer demands, lead-time, warehouses holding and shipment costs, and also suppliers procuring and stocking costs which are all assumed to be stochastic. The aim is to determine the optimum service level so that total cost is minimized. Obtaining such conditions requires determining which supplier nodes, and which DC nodes in network should be active to satisfy the retailers’ needs, an issue which is a network optimization problem per se. The proposed supply chain network for this paper is formulated as a mixed-integer nonlinear programming, and to solve this complicated problem, since the literature for the related benchmark is poor, three numbers of genetic algorithm called Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Ranking Genetic Algorithm (NRGA), and Pareto Envelope-based Selection Algorithm (PESA-II) are applied and compared to validate the obtained results. The Taguchi method is also utilized for calibrating and controlling the parameters of the applied triple algorithms.
The competitive market and declined economy have increased the relevant importance of making su... more The competitive market and declined economy have increased the relevant importance of making supply chain network efficient. Up to now, this has resulted in great motivations to reduce the cost of services, and simultaneously, to improve their quality. A mere network model, as a tri-echelon, consists of Suppliers, Warehouses or Distribution Centers (DCs), and Retailer nodes. To bring it closer to reality, the majority of parameters in this network involve retailer demands, lead-time, warehouses holding and shipment costs, and also suppliers procuring and stocking costs which are all assumed to be stochastic. The aim is to determine the optimum service level so that total cost is minimized. Obtaining such conditions requires determining which supplier nodes, and which DC nodes in network should be active to satisfy the retailers’ needs, an issue which is a network optimization problem per se. The proposed supply chain network for this paper is formulated as a mixed-integer nonlinear programming, and to solve this complicated problem, since the literature for the related benchmark is poor, three numbers of genetic algorithm called Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Ranking Genetic Algorithm (NRGA), and Pareto Envelope-based Selection Algorithm (PESA-II) are applied and compared to validate the obtained results. The Taguchi method is also utilized for calibrating and controlling the parameters of the applied triple algorithms.
Nowadays attaining the general and comprehensive information about customers by means of traditio... more Nowadays attaining the general and comprehensive information about customers by means of traditional methods is difficult for CEO's because of the agility and complexity of organizations. So they spend a considerable time to gather and analyze the market data and consider it according to the organization's strategy. Presenting a useful architecture that capable to diagnose the organization's advantages and disadvantages, and identify the attainable competitive advantages are the main goals of this paper. The output of such architecture can be a general exhibition of company that prepares a clear and on time comprehensive view for CEO's.
This paper aims at the straight and U-shaped assembly line balancing. Due to the uncertainty, var... more This paper aims at the straight and U-shaped assembly line balancing. Due to the uncertainty, variability and imprecision in actual production systems, the processing time of tasks are presented in triangular fuzzy numbers. In this case, it is intended to optimize the efficiency and idleness percentage of the assembly line as well as and concurrently with minimizing the number of workstations. To solve the problem, a modified genetic algorithm is proposed. One-fifth success rule in selection operator to improve the genetic algorithm performance. This leads genetic algorithm being controlled in convergence and diversity simultaneously by the means of controlling the selective pressure. Also a fuzzy controller in selective pressure employed for one-fifth success rule better implementation in genetic algorithm. In addition, Taguchi design of experiments used for parameter control and calibration. Finally, numerical examples are presented to compare the performance of proposed method with existing ones. Results show the high performance of the proposed algorithm.
This paper aims at multi-objective straight and U-shaped assembly line balancing problems with th... more This paper aims at multi-objective straight and U-shaped assembly line balancing problems with the fuzzy task processing times. The problems are referred to herein as f-SALBP and f-SULBP and the objectives that are considered to be satisfied are: (a) minimizing the numbers of stations, (b) maximizing the fuzzy line efficiency, (c) minimizing the fuzzy idleness percentage, and (d) minimizing the fuzzy smoothness index. In fact, the f-SALBP and f-SULBP are SALBP and SULBP generalization in fuzzy circumstance, respectively. Initially, the two problems are formulated and due to the uncertainty, variability and imprecision that often occurred in real-world production systems, the processing time of tasks are supposed as triangular fuzzy numbers. Then, to solve the problem, a hybrid multi-objective genetic algorithm is proposed. A One-Fifth Success Rule (OFSR) is deployed for the selection and mutation operators to improve the genetic algorithm's performance. The results in the genetic algorithm are being controlled in convergence and diversity simultaneously by means of controlling the selective pressure (SP) and mutation rate. Likewise, a fuzzy controller to SP is employed for the OFSR toward a better implementation of the genetic algorithm. In addition, the Taguchi design of experiments is used for parameter control and calibration. Finally, the numerical examples are presented to compare the performance of the proposed method with the existing ones. The results show significantly better performance for the proposed algorithm.
Uploads
Papers by Mosahar Tarimoradi