In this paper, we consider a parallel machine scheduling problem with non-renewable resources. Each job consumes several components and must be processed in one stage composed of identical parallel machines. Resources availability operations, jobs assignment and sequencing are considered and optimized simultaneously. In order to find an optimal solution, an exact method is applied to optimize the total completion time. Due to the problem complexity and prohibitive computational time to obtain an exact solution, a metaheuristic approach based genetic algorithm is proposed and several heuristics are adapted to solve it. Moreover, the impact of non-renewable resources procurement methods on production scheduling is analyzed. The system performances are evaluated in terms of measures such as the solution quality and the execution time. The simulation results show that the proposed genetic algorithm gives the same results as the exact method for small instances and performs the best compared to heuristics for medium and large instances.

In this paper, we consider a parallel machine scheduling problem with non-renewable resources. Each job consumes several components and must be processed in one stage composed of identical parallel machines. Resources availability operations, jobs assignment and sequencing are considered and optimized simultaneously. In order to find an optimal solution, an exact method is applied to optimize the total completion time. Due to the problem complexity and prohibitive computational time to obtain an exact solution, a metaheuristic approach based genetic algorithm is proposed and several heuristics are adapted to solve it. Moreover, the impact of non-renewable resources procurement methods on production scheduling is analyzed. The system performances are evaluated in terms of measures such as the solution quality and the execution time. The simulation results show that the proposed genetic algorithm gives the same results as the exact method for small instances and performs the best compared to heuristics for medium and large instances.

In this paper, we consider a parallel machine scheduling problem with non-renewable resources. Each job consumes several components and must be processed in one stage composed of identical parallel machines. Resources availability operations, jobs assignment and sequencing are considered and optimized simultaneously. In order to find an optimal solution, an exact method is applied to optimize the total completion time. Due to the problem complexity and prohibitive computational time to obtain an exact solution, a metaheuristic approach based genetic algorithm is proposed and several heuristics are adapted to solve it. Moreover, the impact of non-renewable resources procurement methods on production scheduling is analyzed. The system performances are evaluated in terms of measures such as the solution quality and the execution time. The simulation results show that the proposed genetic algorithm gives the same results as the exact method for small instances and performs the best compared to heuristics for medium and large instances.

The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

Purpose This paper aims to investigate an integrated approach that aims at enhancing the application process of value stream mapping (VSM) method. It also proposes an extended VSM called Economic and Environmental VSM(E-EVSM). The proposed approach highlights the improvement of economic and environmental performances. Design/methodology/approach The proposed approach has studied the integration of VSM, fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy quality function deployment (QFD) to improve the economic and environmental performances of manufacturing processes. The VSM method is used for data collection and manufacturing process assessment, whereas fuzzy DEMATEL is used to analyse the current state map. Finally, fuzzy QFD is used to organize the improvement phase of VSM method. Findings The clear findings of this research prove the effectiveness of VSM method on the environmental and economic performances of manufacturing processes. In addition, the proposed approach will show the advantages of fuzzy DEMATEL and fuzzy QFD approaches in improving the application of the VSM method. Research limitations/implications The limitation of this study includes the lack of consideration of other dimensions such as social, technological and managerial. In addition, the proposed approach studied an average set of environmental and economic indicators. Originality/value The novelty of the proposed approach is proved by the development of an extended VSM method (E-EVSM). Also, the proposed approach contributes by a new methodology for analysing and improving the current state map of manufacturing processes.

Purpose This paper aims to investigate an integrated approach that aims at enhancing the application process of value stream mapping (VSM) method. It also proposes an extended VSM called Economic and Environmental VSM(E-EVSM). The proposed approach highlights the improvement of economic and environmental performances. Design/methodology/approach The proposed approach has studied the integration of VSM, fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy quality function deployment (QFD) to improve the economic and environmental performances of manufacturing processes. The VSM method is used for data collection and manufacturing process assessment, whereas fuzzy DEMATEL is used to analyse the current state map. Finally, fuzzy QFD is used to organize the improvement phase of VSM method. Findings The clear findings of this research prove the effectiveness of VSM method on the environmental and economic performances of manufacturing processes. In addition, the proposed approach will show the advantages of fuzzy DEMATEL and fuzzy QFD approaches in improving the application of the VSM method. Research limitations/implications The limitation of this study includes the lack of consideration of other dimensions such as social, technological and managerial. In addition, the proposed approach studied an average set of environmental and economic indicators. Originality/value The novelty of the proposed approach is proved by the development of an extended VSM method (E-EVSM). Also, the proposed approach contributes by a new methodology for analysing and improving the current state map of manufacturing processes.

Purpose This paper aims to investigate an integrated approach that aims at enhancing the application process of value stream mapping (VSM) method. It also proposes an extended VSM called Economic and Environmental VSM(E-EVSM). The proposed approach highlights the improvement of economic and environmental performances. Design/methodology/approach The proposed approach has studied the integration of VSM, fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy quality function deployment (QFD) to improve the economic and environmental performances of manufacturing processes. The VSM method is used for data collection and manufacturing process assessment, whereas fuzzy DEMATEL is used to analyse the current state map. Finally, fuzzy QFD is used to organize the improvement phase of VSM method. Findings The clear findings of this research prove the effectiveness of VSM method on the environmental and economic performances of manufacturing processes. In addition, the proposed approach will show the advantages of fuzzy DEMATEL and fuzzy QFD approaches in improving the application of the VSM method. Research limitations/implications The limitation of this study includes the lack of consideration of other dimensions such as social, technological and managerial. In addition, the proposed approach studied an average set of environmental and economic indicators. Originality/value The novelty of the proposed approach is proved by the development of an extended VSM method (E-EVSM). Also, the proposed approach contributes by a new methodology for analysing and improving the current state map of manufacturing processes.

Nowadays, the integration of solar power into the electrical grids is vital to increase energy efficiency and profitability. Effective usage of the instable solar production of photovoltaic (PV) systems necessitates trustworthy forecasting information. Actually, this addition can gives an ameliorated service quality if the solar radiation variation can be forecasted accurately. In this paper, we propose a new forecasting approach that integrates Autoregressive Moving Average (ARMA) and Genetic algorithms (GA) to make benefit of both of them in order to forecast Global Horizontal Irradiance (GHI) component. The proposed approach is compared with the standard ARMA model. The experimental results show that, the proposed approach outperforms the classical ARMA models in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) coefficient of determination (R)2 and the normalized mean squared error (NMSE).

Nowadays, the integration of solar power into the electrical grids is vital to increase energy efficiency and profitability. Effective usage of the instable solar production of photovoltaic (PV) systems necessitates trustworthy forecasting information. Actually, this addition can gives an ameliorated service quality if the solar radiation variation can be forecasted accurately. In this paper, we propose a new forecasting approach that integrates Autoregressive Moving Average (ARMA) and Genetic algorithms (GA) to make benefit of both of them in order to forecast Global Horizontal Irradiance (GHI) component. The proposed approach is compared with the standard ARMA model. The experimental results show that, the proposed approach outperforms the classical ARMA models in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) coefficient of determination (R)2 and the normalized mean squared error (NMSE).

Nowadays, the integration of solar power into the electrical grids is vital to increase energy efficiency and profitability. Effective usage of the instable solar production of photovoltaic (PV) systems necessitates trustworthy forecasting information. Actually, this addition can gives an ameliorated service quality if the solar radiation variation can be forecasted accurately. In this paper, we propose a new forecasting approach that integrates Autoregressive Moving Average (ARMA) and Genetic algorithms (GA) to make benefit of both of them in order to forecast Global Horizontal Irradiance (GHI) component. The proposed approach is compared with the standard ARMA model. The experimental results show that, the proposed approach outperforms the classical ARMA models in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) coefficient of determination (R)2 and the normalized mean squared error (NMSE).

This paper presents a new hardware implementation of a supervision system used in robot manipulators with two degrees of freedom. In addition to the simulation results, the new System Generator tool of Xilinx r is used to ensure self-generation of HDL codes. This code is used to configure field programmable gate arrays (FPGA) devices in the loop, and the supervision system is used mainly to ensure real-time reconfiguration of robots. In the proposed system, we used a new fault detection (FD) method for a viscous friction fault in the supervised robot combined with a fault-tolerance control method. The first module, based on residual analysis, is used to FD and to properly estimate the necessary corrections of the second module. For data transmission between the supervisor and the supervised robots, we used an approach based on the transmission control protocol. The simulation results show that the proposed method adjusts the fault effect using information transferred from the remote supervisor robot. The hardware implementation generated using Xilinx r System Generator is used to validate the proposed contribution and to ensure real-time processing in the case of industrial robots. The simulation results and the response times of both proposed systems are compared and discussed.

This paper presents a new hardware implementation of a supervision system used in robot manipulators with two degrees of freedom. In addition to the simulation results, the new System Generator tool of Xilinx r is used to ensure self-generation of HDL codes. This code is used to configure field programmable gate arrays (FPGA) devices in the loop, and the supervision system is used mainly to ensure real-time reconfiguration of robots. In the proposed system, we used a new fault detection (FD) method for a viscous friction fault in the supervised robot combined with a fault-tolerance control method. The first module, based on residual analysis, is used to FD and to properly estimate the necessary corrections of the second module. For data transmission between the supervisor and the supervised robots, we used an approach based on the transmission control protocol. The simulation results show that the proposed method adjusts the fault effect using information transferred from the remote supervisor robot. The hardware implementation generated using Xilinx r System Generator is used to validate the proposed contribution and to ensure real-time processing in the case of industrial robots. The simulation results and the response times of both proposed systems are compared and discussed.

This paper presents a new hardware implementation of a supervision system used in robot manipulators with two degrees of freedom. In addition to the simulation results, the new System Generator tool of Xilinx r is used to ensure self-generation of HDL codes. This code is used to configure field programmable gate arrays (FPGA) devices in the loop, and the supervision system is used mainly to ensure real-time reconfiguration of robots. In the proposed system, we used a new fault detection (FD) method for a viscous friction fault in the supervised robot combined with a fault-tolerance control method. The first module, based on residual analysis, is used to FD and to properly estimate the necessary corrections of the second module. For data transmission between the supervisor and the supervised robots, we used an approach based on the transmission control protocol. The simulation results show that the proposed method adjusts the fault effect using information transferred from the remote supervisor robot. The hardware implementation generated using Xilinx r System Generator is used to validate the proposed contribution and to ensure real-time processing in the case of industrial robots. The simulation results and the response times of both proposed systems are compared and discussed.

This paper presents a new hardware implementation of a supervision system used in robot manipulators with two degrees of freedom. In addition to the simulation results, the new System Generator tool of Xilinx r is used to ensure self-generation of HDL codes. This code is used to configure field programmable gate arrays (FPGA) devices in the loop, and the supervision system is used mainly to ensure real-time reconfiguration of robots. In the proposed system, we used a new fault detection (FD) method for a viscous friction fault in the supervised robot combined with a fault-tolerance control method. The first module, based on residual analysis, is used to FD and to properly estimate the necessary corrections of the second module. For data transmission between the supervisor and the supervised robots, we used an approach based on the transmission control protocol. The simulation results show that the proposed method adjusts the fault effect using information transferred from the remote supervisor robot. The hardware implementation generated using Xilinx r System Generator is used to validate the proposed contribution and to ensure real-time processing in the case of industrial robots. The simulation results and the response times of both proposed systems are compared and discussed.

This paper presents a new hardware implementation of a supervision system used in robot manipulators with two degrees of freedom. In addition to the simulation results, the new System Generator tool of Xilinx r is used to ensure self-generation of HDL codes. This code is used to configure field programmable gate arrays (FPGA) devices in the loop, and the supervision system is used mainly to ensure real-time reconfiguration of robots. In the proposed system, we used a new fault detection (FD) method for a viscous friction fault in the supervised robot combined with a fault-tolerance control method. The first module, based on residual analysis, is used to FD and to properly estimate the necessary corrections of the second module. For data transmission between the supervisor and the supervised robots, we used an approach based on the transmission control protocol. The simulation results show that the proposed method adjusts the fault effect using information transferred from the remote supervisor robot. The hardware implementation generated using Xilinx r System Generator is used to validate the proposed contribution and to ensure real-time processing in the case of industrial robots. The simulation results and the response times of both proposed systems are compared and discussed.