Reliability Redundancy Allocation Problem Research Articles

Reliability optimization of reliability-redundancy allocation problems based on K-mixed strategy

In a reliability-redundancy allocation problem (RRAP), system reliability is maximized by selecting component reliabilities and finding the most suitable redundancy of subsystems. The advantages of a K-mixed strategy were introduced by modeling, so as to better improve system reliability. Compared with other existing redundancy strategies, the performance of a K-mixed strategy was verified using redundancy allocation problems (RAP). In this study, for the first time, a reliability calculation model under the new structure ( [Formula: see text] = 2 and [Formula: see text] = 1) is proposed, and the K-mixed strategy under the new structure is used in the reliability-RAP (RRAP), which is more complex than the RAP and further saves the production cost. In practical optimization, there was a complex decision-making problem to ensure optimal system reliability while minimizing system volume, weight, and cost. Then, this K-mixed strategy was adopted for modeling three benchmark problems in RRAP to seek a better and more flexible system structure. A powerful evolutionary algorithm (NSGA-II) was used to solve the new RRAP model to obtain the best system structure and reliability. The advantages of this model were confirmed by comparison with results from previous reliability optimization studies. The results show that the cost-saving advantages of the new structure in ensuring maximum reliability are significant. All the optimized remaining costs are noticeably higher than those of other methods, with the cost savings of the series-parallel system being the greatest. The difference in remaining costs compared to previous optimizations remains in the tens. Moreover, in more complex systems (Complex bridge system), the advantage in remaining volume is very significant, with the improvement being three times that of the optimization results of other methods.

Sustainable hybrid energy system’s reliability optimization by solving RRAP-CM with integration of metaheuristic approaches

PurposeThis research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.Design/methodology/approachTo obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.FindingsThe study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.Research limitations/implicationsThe study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.Practical implicationsThe research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.Originality/valueThis work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

A novel evolutionary strategy optimization algorithm for reliability redundancy allocation problem with heterogeneous components

The reliability-redundancy allocation problem (RRAP) is an optimization problem that maximizes system reliability under some constraints. In most studies on the RRAP, either active redundant components or cold standby components are used in a subsystem. This paper presents a new model for the RRAP of a system with a mixed redundancy strategy, in which all components can be heterogeneous. This formulation leads to a more precise solution for the problem; however, RRAP is an np-hard problem, and the new mixed heterogeneous model will be more complicated to solve. After formulating the issue, a novel design of an evolutionary strategy optimization algorithm is proposed to solve that. The problem consists of discrete and continuous variables, and different mutation strategies are designed for each. The new formulation of the problem and the new method for solving it lead to better results than those reported in other recent papers. We implement the new suggested heterogeneous model with the PSO and SPSO algorithms to better compare the proposed algorithm. Results show improvement in both system reliability and fitness evaluation count.

A Dual Population Collaborative Harmony Search Algorithm with Adaptive Population Size for the System Reliability-Redundancy Allocation Problems

Abstract Aiming at the problem that the diversity of the current double population algorithm with dynamic population size reduction cannot be guaranteed in real time in iteration and is easy to fall into local optimum, this study presents a dual population collaborative harmony search algorithm with adaptive population size (DPCHS). Firstly, we propose a dual population algorithm framework for improving the algorithm global search capability. Within this framework, the guidance selection strategy and information interaction mechanism are integrated to strengthen the competition and cooperation among populations, and achieving a good balance between exploration and exploitation. A population state assessment method is designed to monitor population changes in real-time for enhancing population real-time self-regulation. Additionally, population size adjustment approach is designed to adopted to effectively streamline population resources and improve population quality. Comprehensive experiment results demonstrate that DPCHS effectively addresses system reliability-redundancy allocation problems with superior performance and robust convergence compared to other HS variants and algorithms from different categories.

Application of RRAP reliability optimization as a test of nature-inspired algorithms

This paper presents a discussion on the application of two swarm intelligence algorithms, Cuckoo Search (CS) and Firey Algorithm (FA), to maximize the reliability of two complex systems with resource constraints, which have been well-known in the literature. The reliability of the systems is also evaluated using several classical methods. The results indicate that although the CS algorithm, which utilizes Lévy flight, is eective, the FA rey algorithm outperformed it in the presented optimization tasks, within the given parameter range. These ndings contribute to the ongoing discussion on using nature-inspired algorithms for solving Reliability Redundancy Allocation Problem (RRAP) problems, and the two test scenarios used in the study can be useful for validating other algorithms in RRAP problems. The paper introduces metrics and methods for analyzing and comparing the performance of algorithms in RRAP optimization, including the comparison of criterion function values and other parameters introduced in the paper. Additionally, the paper discusses statistical analyses of variance (ANOVA) with post-hoc RIR Tuckey tests.

A novel evolutionary solution approach for many-objective reliability-redundancy allocation problem based on objective prioritization and constraint optimization

The reliability redundancy allocation problem (RRAP) has been mostly solved either as a single or as a multi-objective optimization problem. However, this problem also has numerous important constraints which play prominent roles in meeting the objectives. This paper proposes a novel formulation named ‘prioritized many reliability redundancy allocation problems (PrMaORRAP)’ that optimizes all the problem objectives concurrently, and also preserves the priority among them. Then, we propose a hybrid method which utilizes the features of many-objective optimization as well as priority relations between different objectives. Here, we divide the procedure into two modules: one is the main priority or the leader which will stay at the top level; underneath the first lie the second part in which rest of the objectives are optimized. The solution approach embeds the optimization structure within the evolutionary process making a prioritized many-objective evolutionary algorithms. We formulate various structures such as series, series-parallel, complex bridge and overspeed gas turbine system of RRAP as many-objective optimization problems, and provide detailed experimental demonstration on how our proposed model works for all these structures. We compare the results given by the proposed approach with the results of other approaches available in the literature and establish the superiority of our proposed solution approach.

A new model for reliability redundancy allocation problem with component mixing

Reliability Redundancy Allocation Problem (RRAP) is a well-known problem in reliability optimization. In most of the previous research in RRAP, it is assumed that the components allocated to each subsystem are of the same type. This assumption limits the possible choices for the subsystem. In this paper, a new RRAP is proposed where the components allocated to each subsystem can be non-identical, and the redundancy strategy is a decision variable that can be active, standby, mixed, or none. In the proposed RRAP, both the active and the standby components can be independently of different types, which has not been considered in any of the previous studies. Since RRAP is an NP-hard problem, an adapted genetic algorithm is presented to search for the optimal solution of the proposed problem. Finally, some well-known problems are investigated to compare the proposed model with the previous ones. The obtained results show that the proposed RRAP can improve the reliability of the system in different cases by 10 % to 63 % in terms of the maximum possible improvement index.

Multi-task optimization in reliability redundancy allocation problem: A multifactorial evolutionary-based approach

Evolutionary multi-task optimization attempts to solve multiple optimization problems simultaneously by modeling the solution structures of two or more problems within a single encoding. In this paper, we report a novel way for evolutionary multi-task optimization in the reliability redundancy allocation problem exploiting the concepts of the popular multifactorial evolutionary algorithm (MFEA). We demonstrate the working of the proposed method considering two test sets and show how they can be concurrently solved using the MFEA. In the first test set, we consider two optimization tasks (case studies): the complex (bridge) system and the series-parallel system. In the second test set, there are two optimization tasks: the over-speed protection system for the gas turbine and the life support system in a space capsule. The common attributes between the two systems, within a set, complement each other to enhance the evolution process through implicit knowledge transfer. We present the comparative results considering existing evolutionary methods such as particle swarm optimization, genetic algorithm, simulated annealing, differential evolution, and ant colony optimization. Results are analyzed and compared using the average reliability, best reliability, computation time, performance ranking, and the popular statistical significance test of analysis of variance. The outcome shows that our proposed approach can solve the multiple case studies of RRAP simultaneously without compromising the solution quality. Moreover, our MFEA based solution method tops the rank among all approaches and provides significant improvement in computation time where it gains 28.02% and 14.43% of improvement in computation time for first and second test set, respectively, when compared with genetic algorithm. The percentage improvements in the computational time of the MFEA significantly increases when it is compared with other approaches.

A novel reliability redundancy allocation problem formulation for complex systems

Reliability Redundancy Allocation Problem (RRAP) aims to optimize system design with respect to certain resource constraints. For the most of RRAP studies, it is commonly assumed that the subsystems form a series-parallel or bridge structure, while the redundant components in the same subsystem are placed in parallel. To generalize system structures, this paper proposes a novel RRAP for complex systems. The complex system structure is modeled with a graph, where the vertices and edges represent the components and connections between them, respectively. To calculate the system reliability from its structure graph, an automated calculation method based on functionality multi-graph is put forward. The components are classified into various clusters based on the system functionalities they provide, then a multi-graph is constructed by fusing the vertices in the same cluster into a hyper-vertex. Spanning trees are derived from functionality multi-graph, and a validity test is performed to identify valid system configurations. On this basis, a factoring theorem-based algorithm is devised to calculate system reliability. Comparative experiments are carried out on six benchmark problems, the results of which compare favorably to previous RRAP studies. A case study of security system design is also conducted to demonstrate the practicality of our proposed method.

Optimization of time based fuzzy multi-objective reliability redundancy allocation problem for [formula omitted] system using tuning and neighborhood based fuzzy MOPSO algorithm

In this paper, a time based fuzzy multi objective reliability redundancy allocation problem (FMORRAP) is proposed for the xj−out−of−m series–parallel system. The main objective is to maximize the system reliability with minimization of system cost and system repairing cost by optimizing the number of redundant components at each stage. The objectives are achieved by satisfying entropy constraints with limited redundant components at each stage and same for the whole system. Here the uncertainty of reliability, cost and repairing cost of each component is maintained by using triangular fuzzy number (TFN). The decreasing factor of component reliability and cost follows the change in the length of radius for the inverse logarithmic spiral with respect to time. Similarly the increasing factor of component repairing cost follows the same for the logarithmic spiral. Tuning and Neighborhood based Fuzzy Multi-Objective Particle Swarm Optimization (TNF-MOPSO) algorithm is proposed to solve fuzzy multi-objective optimization problems (MOOP). The proposed problem and proposed algorithm are illustrated by using a bench mark problem. The proposed algorithm produced better membership of objective functions for most of the time values and high satisfaction level of reliability for all values of the time parameter. It also outperforms the standard approaches MOPSO and NF-MOPSO.

Optimization of time-dependent fuzzy multi-objective reliability redundancy allocation problem for n-stage series–parallel system

Optimization of time-dependent fuzzy multi-objective reliability redundancy allocation problem for n-stage series–parallel system

A novel binary-addition simplified swarm optimization for generalized reliability redundancy allocation problem

Abstract Network systems are commonly used in various fields, such as power grids, Internet of Things, and gas networks. The reliability redundancy allocation problem is a well-known reliability design tool that needs to be developed when the system is extended from a series-parallel structure to a more general network structure. Therefore, this study proposes a novel reliability redundancy allocation problem, referred to as the general reliability redundancy allocation problem, to be applied in network systems. Because the general reliability redundancy allocation problem is NP-hard, a new algorithm referred to as binary-addition simplified swarm optimization is proposed in this study. Binary-addition simplified swarm optimization combines the accuracy of the binary addition tree algorithm with the efficiency of simplified swarm optimization, which can effectively reduce the solution space and speed up the time required to find high-quality solutions. The experimental results show that binary-addition simplified swarm optimization outperforms three well-known algorithms: the genetic algorithm, particle swarm optimization, and simplified swarm optimization in high-quality solutions and high stability on six network benchmarks.

Hybridization in nature inspired algorithms as an approach for problems with multiple goals: An application on reliability–redundancy allocation problems

This paper focuses on approaching reliability–redundancy allocation problems, using hybrid schemes that consist of individual nature inspired algorithms. The aim is to investigate if hybridization is an efficient way to approach problems with multiple goals. Therefore, known algorithms that have been successfully applied to reliability and redundancy allocation problems in literature are implemented as part of hybrid schemes in this study. The idea behind that is to study whether an efficient hybrid scheme is the outcome of the hybridization of (individual) efficient algorithms. The performance of the nine proposed schemes and the individual algorithms is tested on ten well-known artificial and real-world case studies, from the field of reliability engineering. The numerical results are compared to others from literature highlighting the efficiency of the proposed hybrid schemes and consequently, support the hypothesis that hybridization can enhance the performance of optimization methods. The experimental results demonstrate that among the nine hybrid schemes, the one consisting of Bat Algorithm and Firefly Algorithm shows the best performance.

Grey wolf optimizer and hybrid PSO‐GWO for reliability optimization and redundancy allocation problem

AbstractAnalysis and optimization of system reliability have very much importance for developing an optimal design for the system while using the available resources. Several studies are centered towards reliability optimization using metaheuristics. In this study, a recently developed metaheuristic optimization algorithm called hybrid PSO‐GWO (HPSGWO) to solve the reliability‐redundancy optimization problem has been proposed. The HPSGWO fuses the Particle Swarm Optimization's (PSO) exploitation ability with the grey wolf optimizer's (GWO) exploration ability. The comparison of results with prior best results of PSO and GWO for the four benchmarks of reliability redundancy allocation problem demonstrates the HPSGWO as a productive enhancement strategy since it got promising answers than other metaheuristic algorithms.

A Generalized Multiobjective Reliability Redundancy Allocation With Uncertainties

Multi-objective reliability-redundancy allocation problem (MORRAP) needs to maximize system reliability and minimize cost, weight, and volume with underlining constraints. In the systems’ design and analysis phase, uncertainties can occur from various sources, such as manufacturing variability, environmental conditions, user behavior, etc. To deal with this, we present a generalization of the traditional MORRAP under multiple empirical and ambiguous circumstances, named interval type-2 fuzzy multiobjective reliability redundancy allocation problem (IT2FMORRAP). The newly formulated IT2FMORRAP considers optimizing goals as reliability, cost, and weight for a series-parallel system with interval type-2 fuzzy number. The mathematical formulation is established under which the proposed IT2FMORRAP model reduces to T1FMORRAP (type-1 fuzzy MORRAP), IVMORRAP (interval-valued MORRAP), and classical MORRAP. An Enhanced Karnik-Mendel and NSGA-II algorithm-based solving strategy is developed for the proposed IT2FMORRAP. The real-world dataset is considered to demonstrate the efficacy of the solution method for the proposed problem. A K-mean clustering technique identifies the best solution sets from the knee region of the generated Pareto fronts. An experimental study on commonly used performance metrics reveals that IT2FMORRAP performs significantly better than T1FMORRAP and crisp MORRAP. Further, the statistical analysis also confirms the hypothesis established in the empirical research. Finally, a comparative performance study has been conducted with notable state-of-the-art papers from the literature to encounter an appropriate establishment for the proposed work in the domain.

A Novel Perspective on Reliable System Design With Erlang Failures and Realistic Constraints for Incomplete Switching Mechanisms

This study focuses on effectively designing reliable systems. Such systems are capable of withstanding failure events by applying multiple realistic workarounds. These workarounds include allocating redundancy, component reliability, and backup strategy that are considered concurrently as decision variables. In this novel view, the strategy and reliability of components are determined freely and independently to achieve optimality. The research problem is implemented in a general case to audit the capabilities of the proposed approach in realistic situations. This case deploys an Erlang time-to-failure probability density function together with incomplete switching. With its improved reliability and resource functions, the proposed model challenges the existing presumption regarding the superiority of the cold-standby approach in the mentioned field and provides a realistic trade-off between different redundancy strategies. This practical view reflects on reliability, cost, weight, and volume of the switch, simultaneously. The findings revealed that the proposed joint reliability-redundancy allocation problem, with an added freedom of strategy choice, outperforms the pure cold-standby counterpart. Owing to the NP-hard nature of the problem, a simplified particle swarm optimization algorithm is suggested and utilized as a solution method. The performance of the novel view is assessed using multiple benchmark instances including some typical problems from the literature. Our numerical analysis demonstrates the superiority of this approach with our maximum possible index reaching up to %96 compared to the existing results in the past works. Furthermore, the selected solution algorithm is compared with a differential evolution algorithm. We show that this simplified particle swarm optimization algorithm performs considerably better in all tested scenarios.

Hybrid PSO‐GWO algorithm for reliability redundancy allocation problem with Cold Standby Strategy

AbstractReliability allocation for components, redundancy allocation, and reliability redundancy allocation are of great significance for system reliability designing. Generally, standby redundancy gives higher reliability for any system than active redundancy, but standby redundancy has more complex modeling than active redundancy. Cold standby strategy is one of the most consistently applied procedures to accomplish high‐reliability necessity, where backups are performed to guarantee that a backup part can take control over the undertaking successfully when the currently working fizzles. Considering the fact that components may fail during the switching process from standby to active, the impact of an imperfect switch is also applied in the system. In this work, a new hybrid GWO‐PSO(HPSGWO) algorithm, based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), is presented to solve the cold‐standby reliability redundancy allocation problem (RRAP). The RRAP is a popular mixed integer nonlinear programming issue in a system plan that necessitates that the reliability target is set to fulfill the resource utilization requirement. Four contextual analyses are examined to feature the applicability of the proposed algorithm. The outcomes are compared with those obtained from PSO and GWO.

System Reliability-Redundancy optimization with High-Level of subsystems

High systems’ reliability is crucial in competitive industrial plants. System Reliability-Redundancy Allocation (RRA) is an essential design consideration for maximizing the overall systems’ reliability under various systems’ constraints. This paper addresses the system RRAP problem by investigating two effective nature-inspired optimization techniques, namely Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), implemented with penalty functions. Their capability in solving the RRA problem is evaluated regarding a system consisting of fifteen subsystems connected in series. Results show that the PSO is a better approach to solving this problem than the GWO.

A sequential two-stage approach based on variational Bayesian inference for reliability-redundancy allocation

The reliability-related design has been a crucial chain in complex and reliability-critical engineering systems. It serves as a preventive countermeasure to catastrophic failures caused by uncertainties. To keep up with this rapidly developing field, this paper presents a novel metaheuristic, based on the variational Bayesian inference (VBI) to efficiently solve the optimal reliability design. Specifically, the reliability-redundancy allocation problem (RRAP). The proposed metaheuristic starts from a primary population, then leverages VBI to fully excavate the information of feasible individuals from the previous generation, in order to produce the next-generation population. This process is iterated until the solution converges to the optimal decision scheme. In addition, we set up an automatic stratification strategy, so that the new individuals can approach the optimal solution faster. Furthermore, we divide RRAP into a reliability optimization problem (ROP) and a redundancy allocation problem (RAP). This not only reduces the dimension of decision variables, but also speeds up the convergence. ROP and RAP are solved sequentially and iteratively until the preset stopping condition is satisfied. The case studies showcase that the proposed approach can obtain the optimal or near-optimal solution within a reasonable period.

Optimizing the Complex Systems Reliability Using Mixed Strategy in Ultra-fast Gas Turbine Protection System

There are four general ways to improve reliability. One of the most popular of these solutions is to add surplus components to the subsystems of a system (known as RRAP) and the reliability-redundancy allocation problem. In a subsystem, how these surplus components are used is of particular importance. How to use surplus components in the subsystem is known as. There are three general strategies for reliability issues known as active, stored, and mixed strategy. The main aim of the study is to create a new formula on the basis of the subsystems that use these two strategies. Model 4 provides a continuous limit of the Markov chain with continuous time. This new formula enables us to perform complex strategy calculations in a very short time and accurately. Finally the exact function obtained for mixed strategy Protective system ultra-fast gas turbine has been tested. Given the results, the new equation decreased the solution time besides the accurate estimation of the system’s reliability.