جستجوی مقالات مرتبط با کلیدواژه
تکرار جستجوی کلیدواژه redundancy allocation problem در نشریات گروه فنی و مهندسی
redundancy allocation problem
در نشریات گروه برق
تکرار جستجوی کلیدواژه redundancy allocation problem در مقالات مجلات علمی
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Scientia Iranica, Volume:29 Issue: 6, Nov-Dec 2022, PP 3523 -3541In this research, a new hybrid model for the redundancy allocation problem (RAP) in a series-parallel configuration with the k-out-of-n subsystem is presented. In the given model, the redundancy policy is set to an active, warm standby, or no redundancy. In warm standby policy, an imperfect switch detected the component's failure and replaced the fail component with a new standby one. So, the subsystems' redundancy policy is one of the model's decision variables. We presented a new objective function for the RAP to calculate the reliability of a system that consists of active and warm standby subsystems. The presented model aims to determine the subsystems' redundancy policy, the type and number of redundant components to maximize the system's reliability, under the system's cost, volume, and weight constraints. To solve the proposed model, we used two Genetic Algorithm (GA) and hybrid GA (HGA) meta-heuristic algorithm with local search. Since the %RPD of HGA is 2.1% (on average) better than GA in solving ten large-scale instances, the result shows the superiority of HGA in comparison with GA for solving the presented RAP.Keywords: Redundancy allocation problem, warm standby, Reliability, Meta-heuristic methods
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Scientia Iranica, Volume:28 Issue: 6, Nov-Dec 2021, PP 3602 -3616This paper presents a new redundancy allocation problem for a system with the k-out-of-n configuration at the subsystems’ level with two active and cold standby redundancy strategies. The failure rate of components in each subsystem depends on the number of working components. The components are non-reparable, and the failure rate of the component can be decreased with some preventive maintenance actions. The model has two objective functions: maximizing the system’s reliability and minimizing the system’s costs. The system aims to find the type and number of components in each subsystem, redundancy strategy of subsystems, as well as the decreased values of components failure rates in subsystems. Since the redundancy allocation problem belongs to NP-Hard problems, two Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Ranked genetic algorithm (NRGA) metaheuristic algorithms were used to solve the presented model and to tune algorithms parameters we used response surface methodology (RSM). Besides, these algorithms were compared using five different performance metrics. Finally, the hypothesis test was used to analyze the results of the algorithms.Keywords: Reliability, Redundancy allocation problem, NSGA-II, NRGA, Response Surface Methodology
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Scientia Iranica, Volume:26 Issue: 2, Mar-Apr 2019, PP 1023 -1038Reliability improvement for electronics and mechanical systems is vital for engineers in order to design of these systems. For this reason, there are many researches in this scope to help engineers in real world applications. One of the useful methods in reliability optimization is redundancy allocation problem (RAP). In the most previous works, the failure rates of system components are considered to be constant based on negative exponential distribution; whereas, nearly all systems in real world have components with time-dependent failure rates; i.e., the failure rates of system components will be changed time by time. In this paper, we have worked on a RAP for a system under k-out-of-n subsystems with time-dependent components failure rates based on Weibull distribution. Also, the redundancy policy of the proposed system is considered as mixed strategy and the optimization method was based on the simulation technique to obtain reliability function as implicit function. Finally, a branch and bound algorithm has been used to solve the model, exactly.Keywords: Reliability, Redundancy allocation problem, Weibull Distribution, Time-dependent Failure Rates, Optimization via Simulation
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Redundancy allocation problem (RAP) is one way to increase system reliability. In most of the models developed so far for the RAP, system components are considered to have a binary state consisting of «working perfect» or «completely failed.» However, to suit real-world applications, this assumption has been relaxed in this paper such that components can have three states. Moreover, a bi-objective RAP (BORAP) is modeled for a system with serial subsystems, in which non-repairable tri-state components of each subsystem are configured in parallel and the subsystem works under the k-out-of-n policy. Furthermore, to enhance system reliability, technical and organizational activities that can affect failure rates of the components and hence can improve the system performance are also taken into account. The aim is to find the optimum number of redundant components in each subsystem such that the system reliability is maximized while the cost is minimized within some real-world constraints. In order to solve the complicated NP-hard problem at hand, the multi-objective strength Pareto evolutionary algorithm (SPEA-II) is employed. As there is no benchmark available, the non-dominated sorting genetic algorithm (NSGA-II) is used to validate the results obtained. Finally, the performances of the algorithms are analyzed using 20 test problems.Keywords: Reliability, Redundancy allocation problem, Tri, state components, Bi, objective optimization, SPEA, II
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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.Keywords: Redundancy allocation problem, k, out, of, n systems, Meta, heuristic algorithms, Simulation methods, Enterprise Dynamic (ED) software
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