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.

The emerging behavior in complex systems is more complicated than the sum of the behaviors of their constituent parts. This behavior involves the propagation of faults between the parts and requires information about how the parts are related. Therefore, the prognostic function at the system-level becomes a very tough task. Conventional approaches focus on identifying faults and their probabilities of occurrence. In complex systems, this can create statistical limitations for prognostic function where component fault relies on the connected components in the system and their state of degradations. In this paper, a new Holonic agent-based approach is proposed for system-level remaining useful life (S-RUL) estimation with different dependencies. As the proposed approach can capture fault/failure mode propagation and interactions that occur in the system all the way up through the component and eventually system level, it can work as an automatic testing-tool in reliability tasks. Through a numerical example, the implementation is done in Java Agent Development Environment with and without consideration of stochastic dependence. Results show that the indirect effect of influencing components has a massive impact on the S-RUL, and the impact of stochastic dependencies should not be ignored, especially in the early stages of the system design.

The emerging behavior in complex systems is more complicated than the sum of the behaviors of their constituent parts. This behavior involves the propagation of faults between the parts and requires information about how the parts are related. Therefore, the prognostic function at the system-level becomes a very tough task. Conventional approaches focus on identifying faults and their probabilities of occurrence. In complex systems, this can create statistical limitations for prognostic function where component fault relies on the connected components in the system and their state of degradations. In this paper, a new Holonic agent-based approach is proposed for system-level remaining useful life (S-RUL) estimation with different dependencies. As the proposed approach can capture fault/failure mode propagation and interactions that occur in the system all the way up through the component and eventually system level, it can work as an automatic testing-tool in reliability tasks. Through a numerical example, the implementation is done in Java Agent Development Environment with and without consideration of stochastic dependence. Results show that the indirect effect of influencing components has a massive impact on the S-RUL, and the impact of stochastic dependencies should not be ignored, especially in the early stages of the system design.

The emerging behavior in complex systems is more complicated than the sum of the behaviors of their constituent parts. This behavior involves the propagation of faults between the parts and requires information about how the parts are related. Therefore, the prognostic function at the system-level becomes a very tough task. Conventional approaches focus on identifying faults and their probabilities of occurrence. In complex systems, this can create statistical limitations for prognostic function where component fault relies on the connected components in the system and their state of degradations. In this paper, a new Holonic agent-based approach is proposed for system-level remaining useful life (S-RUL) estimation with different dependencies. As the proposed approach can capture fault/failure mode propagation and interactions that occur in the system all the way up through the component and eventually system level, it can work as an automatic testing-tool in reliability tasks. Through a numerical example, the implementation is done in Java Agent Development Environment with and without consideration of stochastic dependence. Results show that the indirect effect of influencing components has a massive impact on the S-RUL, and the impact of stochastic dependencies should not be ignored, especially in the early stages of the system design.

The emerging behavior in complex systems is more complicated than the sum of the behaviors of their constituent parts. This behavior involves the propagation of faults between the parts and requires information about how the parts are related. Therefore, the prognostic function at the system-level becomes a very tough task. Conventional approaches focus on identifying faults and their probabilities of occurrence. In complex systems, this can create statistical limitations for prognostic function where component fault relies on the connected components in the system and their state of degradations. In this paper, a new Holonic agent-based approach is proposed for system-level remaining useful life (S-RUL) estimation with different dependencies. As the proposed approach can capture fault/failure mode propagation and interactions that occur in the system all the way up through the component and eventually system level, it can work as an automatic testing-tool in reliability tasks. Through a numerical example, the implementation is done in Java Agent Development Environment with and without consideration of stochastic dependence. Results show that the indirect effect of influencing components has a massive impact on the S-RUL, and the impact of stochastic dependencies should not be ignored, especially in the early stages of the system design.

The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

In this paper we analyze a problem of inventory management in a multi-levels multi-products distribution network with three echelon, the studied system consists of a central warehouse and three distribution centers identified by their location zones where each center is connected to a wholesaler group that serve the retailers of his region, which in turn feeds the customers of the regions located in the Algerian territory. The aim of this study is to apply a collaboration between the different actors of the same level in a form of an exchange of products, the exchange can occurs only when the actual demand is being received, in order to study the impact of product exchanges in the distribution networks and its influence on the total costs of the logistics chain from the central warehouse to the delivery to the final customer.

In this paper we analyze a problem of inventory management in a multi-levels multi-products distribution network with three echelon, the studied system consists of a central warehouse and three distribution centers identified by their location zones where each center is connected to a wholesaler group that serve the retailers of his region, which in turn feeds the customers of the regions located in the Algerian territory. The aim of this study is to apply a collaboration between the different actors of the same level in a form of an exchange of products, the exchange can occurs only when the actual demand is being received, in order to study the impact of product exchanges in the distribution networks and its influence on the total costs of the logistics chain from the central warehouse to the delivery to the final customer.

In this paper we analyze a problem of inventory management in a multi-levels multi-products distribution network with three echelon, the studied system consists of a central warehouse and three distribution centers identified by their location zones where each center is connected to a wholesaler group that serve the retailers of his region, which in turn feeds the customers of the regions located in the Algerian territory. The aim of this study is to apply a collaboration between the different actors of the same level in a form of an exchange of products, the exchange can occurs only when the actual demand is being received, in order to study the impact of product exchanges in the distribution networks and its influence on the total costs of the logistics chain from the central warehouse to the delivery to the final customer.