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This paper formulates a two-echelon singleproducer multi-buyer supply chain model, while a single product is produced and transported to the buyers by the producer. The producer and the buyers apply vendormanaged inventory mode of operation. It is assumed that the producer applies economic production quantity policy, which implies a constant production rate at the producer. The operational parameters of each buyer are sales quantity, sales price and production rate. Channel profit of the supply chain and contract price between the producer and each buyer is determined based on the values of the operational parameters. Since the model belongs to nonlinear integer programs, we use a discrete particle swarm optimization algorithm (DPSO) to solve the addressed problem; however, the performance of the DPSO is compared utilizing two well-known heuristics, namely genetic algorithm and simulated annealing. A number of examples are provided to verify the model and assess the performance of the proposed heuristics. Experimental results indicate that DPSO outperforms the rival heuristics, with respect to some comparison metrics.

Abstract. In this article, a new model and a novel solving method are provided to address the non-exponential redundancy allocation problem in series-parallel k-out-of-n systems with repairable components based on Optimization Via Simulation (OVS) technique. Despite the previous studies, in this model, the failure and repair times of each component were considered to have non-negative exponential distributions. This assumption makes the model closer to the reality where the majority of used components have greater chance to face a breakdown in comparison to new ones. The main objective of this research is the optimization of Mean Time to the First Failure (MTTFF) of the system via allocating the best redundant components to each subsystem. Since this objective function of the problem could not be explicitly mentioned, the simulation technique was applied to model the problem, and di erent experimental designs were produced using DOE methods. To solve the problem, some meta-Heuristic Algorithms were integrated with the simulation method. Several experiments were carried out to test the proposed approach; as a result, the proposed approach is much more real than previous models, and the near optimum solutions are also promising.

Redundancy allocation is one of the adopted approaches that is used by system designers to improve the performance of systems. In this article, a new model and a novel‐solving method are provided to address the nonexponential redundancy allocation problem in series‐parallel systems with repairable components based on optimization via simulation approach and artificial neural network technique. Despite the previous researches, in this model the failure and repair times of the each component were considered to have nonnegative exponential distributions. This assumption makes the model closer to the reality where most of used components have greater chance to face a breakdown in comparison to new ones. The main aim of this research is the optimization of mean time to the first failure of the system via allocating the best redundant components for each subsystem. Since this objective function of the problem could not be explicitly mentioned, the simulation technique and artificial neural network were applied to model the problem, and different experimental designs were produced using design of experiment methods. To solve the problem, some metaheuristic algorithms were integrated with the simulation method. Several experiments were performed to test the proposed approach, and as the results show, the proposed approach is much more real than previous models, and also the near optimum solutions are promising.

This article presents an optimal distributed energy resource management system for a smart grid connected to photovoltaics, battery energy storage, and an electric vehicle aggregator. These management systems are one of the key factors for the optimal control of power converters connected to the grid. The proposed management system includes the communication architecture necessary for realizing the information flow between the individual control of the distributed generators and the master supervisory control algorithm. The work carried out on two levels is first to design a control strategy for energy management and validate it with the grid in real-time hardware-in-the-loop simulation integrating the IEC61850 communication layer and physical intelligent electronic devices. The second is to analyze the vulnerabilities of the designed methodology for cybersecurity threats explicitly with the extension of IEC61850 to electric vehicle aggregators for communication with the master energy management. A man-in-the-middle attack conducted in the supervisory communication layer enabled us to investigate the effects of such an attack on the performance and operation of the smart electric grid.