Flexible manufacturing systems (FMSs) have widely expanded in industry sectors all over the world. Scheduling is one of the problems that face the life cycle of FMSs. This paper introduces the simultaneous scheduling problem of automated guided vehicles (AGVs) and machines in FMSs. The study is based on discrete event simulation (DES) to solve that problem using benchmark data used by previous studies. The objective of this study is to determine the starting and completion times of each job to minimise the makespan. The results validate the robustness of the simulation model, especially for large-scale problems. Furthermore, they are compared against the other approaches and show consistency.

Flexible manufacturing systems (FMSs) have widely expanded in industry sectors all over the world. Scheduling is one of the problems that face the life cycle of FMSs. This paper introduces the simultaneous scheduling problem of automated guided vehicles (AGVs) and machines in FMSs. The study is based on discrete event simulation (DES) to solve that problem using benchmark data used by previous studies. The objective of this study is to determine the starting and completion times of each job to minimise the makespan. The results validate the robustness of the simulation model, especially for large-scale problems. Furthermore, they are compared against the other approaches and show consistency.

Flexible manufacturing systems (FMSs) have widely expanded in industry sectors all over the world. Scheduling is one of the problems that face the life cycle of FMSs. This paper introduces the simultaneous scheduling problem of automated guided vehicles (AGVs) and machines in FMSs. The study is based on discrete event simulation (DES) to solve that problem using benchmark data used by previous studies. The objective of this study is to determine the starting and completion times of each job to minimise the makespan. The results validate the robustness of the simulation model, especially for large-scale problems. Furthermore, they are compared against the other approaches and show consistency.

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Over the past few years, dynamic spectrum access has been gaining an increasing attention as a solution to the spectrum scarcity problem. In this paper, a primary user detection technique based on Maximum A Posteriori estimation is proposed for dynamic spectrum access networks. In the proposed technique, a set of secondary users acting as sensing nodes send their individual decisions about the existence of the primary user to a central fusion center. The fusion center uses the received data to form a codeword then, applies the maximum a posteriori estimation rule to make a final decision regarding the presence of the primary user. The proposed technique takes into consideration the accuracy of the local decisions provided by the secondary users when making a final decision. In this paper, we analyze the performance of the proposed scheme and derive closed-form expressions for the upper bounds of the false alarm and misdetection probabilities. The results show that the proposed technique outperforms other combining techniques in terms of its ability to detect the primary user and, accordingly, minimizes the harmful interference to the licensed network. Moreover, the proposed technique achieves better performance at a lower number of reporting secondary users which compensates for the complexity of the maximum a posteriori estimation.

Over the past few years, dynamic spectrum access has been gaining an increasing attention as a solution to the spectrum scarcity problem. In this paper, a primary user detection technique based on Maximum A Posteriori estimation is proposed for dynamic spectrum access networks. In the proposed technique, a set of secondary users acting as sensing nodes send their individual decisions about the existence of the primary user to a central fusion center. The fusion center uses the received data to form a codeword then, applies the maximum a posteriori estimation rule to make a final decision regarding the presence of the primary user. The proposed technique takes into consideration the accuracy of the local decisions provided by the secondary users when making a final decision. In this paper, we analyze the performance of the proposed scheme and derive closed-form expressions for the upper bounds of the false alarm and misdetection probabilities. The results show that the proposed technique outperforms other combining techniques in terms of its ability to detect the primary user and, accordingly, minimizes the harmful interference to the licensed network. Moreover, the proposed technique achieves better performance at a lower number of reporting secondary users which compensates for the complexity of the maximum a posteriori estimation.

Over the past few years, dynamic spectrum access has been gaining an increasing attention as a solution to the spectrum scarcity problem. In this paper, a primary user detection technique based on Maximum A Posteriori estimation is proposed for dynamic spectrum access networks. In the proposed technique, a set of secondary users acting as sensing nodes send their individual decisions about the existence of the primary user to a central fusion center. The fusion center uses the received data to form a codeword then, applies the maximum a posteriori estimation rule to make a final decision regarding the presence of the primary user. The proposed technique takes into consideration the accuracy of the local decisions provided by the secondary users when making a final decision. In this paper, we analyze the performance of the proposed scheme and derive closed-form expressions for the upper bounds of the false alarm and misdetection probabilities. The results show that the proposed technique outperforms other combining techniques in terms of its ability to detect the primary user and, accordingly, minimizes the harmful interference to the licensed network. Moreover, the proposed technique achieves better performance at a lower number of reporting secondary users which compensates for the complexity of the maximum a posteriori estimation.