System Reliability Optimization Research Articles

Analysis of Reliability and Cost of Complex Systems with Metaheuristic Algorithms

Introduction. The assessment of the reliability and cost of complex systems, such as Complex Bridge Systems (CBS) and Life Support Systems in Space Capsules (LSSSC), is fascinating. To achieve the ideal system design through diverse constraints and increase overall system reliability, researchers have extensively explored system reliability and cost optimization problems. Hence, the significant advancement in metaheuristic methods is the primary source of further system reliability and cost optimization process refinement.
 Aim and tasks. This research attempts to enhance the reliability and cost of complex systems named CBC and LSSSC has been presented. 
 Results. The structure is based on few recent metaheuristic techniques, such as Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Gazelle Optimization Algorithm (GOA), Dragonfly Algorithm (DA), and Coati Optimization Algorithm (COA). Comparing the acquired findings to those found in other proposed techniques demonstrates the usefulness of a methodology based on COA. The proposed COA algorithm exhibits enhanced efficiency by offering superior solutions to reliability and cost-optimization problems. In addition, a non-parametric Friedman ranking was performed for validation. The results of this research are based on improving the reliability of the parameters and decreasing complex systems’ costs used by the five metaheuristic methods. Observing the convergence graph, Friedman ranking, statistical results test, and tables determined that COA is the most effective algorithm for a complex system’s cost and reliability parameters compared to other existing approaches, and also provided a faster solution.
 Conclusions. This study proposes unique ways to reduce costs while increasing parameter reliability in complex systems. After analysing the comparative solution, the authors found that when comparing these approaches (GOA, DA, MFO, WOA, and COA), the COA provided the best minimum solution for the cost and reliability of complex systems. Hence, the suggested COA procedure was more successful than that described in this study.

History of System Reliability Optimization

History of System Reliability Optimization

Time series importance measure-based reliability optimization for cellular manufacturing systems

Cellular manufacturing systems (CMSs) can improve the quality and efficiency of the manufacturing process by multiple processing cells with different functions and group technology. CMSs require high reliability to complete processing missions successively, so reliability optimization is an important part to guarantee system performance. This paper proposes a binary decision diagram-based three-step evaluation method to analyze CMS reliability. A reliability optimization model of CMS is constructed by considering the limited cost to determine the optimal combination of machine degradation parameters. Considering the advantages of ant colony optimization (ACO) and time series importance measure (TIM), a TIM-based ant colony optimization (TIACO) is developed to solve the optimization model. To verify the performance of TIACO, system reliability and running time are introduced to compare with genetic algorithm (GA), ACO, and time series importance measure-based genetic algorithm (TIGA). (1) System reliability obtained by TIACO is always the best. (2) Running time of TIACO is smaller. A case study of an unmanned aerial vehicle manufacturing company verifies the effectiveness of TIACO, and machines with higher TIMs should be given priority to improving their degradation parameters, which provides a new idea for reliability evaluation and optimization of CMSs.

Reliability optimization of multi-state systems with two performance sharing groups

Reliability optimization of multi-state systems with two performance sharing groups

A Review of Optimization Studies for System Appointment Scheduling

In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation optimization research is of great significance. This study reviews the research progress on appointment scheduling system optimization. Firstly, we classify and conclude the existing appointment scheduling system structures and decision-making frameworks. Subsequently, we summarize the system reliability optimization framework from three aspects: appointment scheduling system optimization objectives, decision variables and constraints. Following that, we methodically review the most applied system optimization algorithms in different appointment scheduling systems. Lastly, a literature bibliometric analysis is provided. During our review of the literature, we observe that (1) optimization methods in ASs predominantly involve the application of genetic algorithms and simulation optimization algorithms; (2) neural networks and deep learning methods are core technologies in health management optimization; (3) a bibliometric analysis reveals a heightened interest in the optimization technology of ASs within China compared to other nations; and (4) further advancements are essential in the comprehensive optimization of the system, exploration of practical usage scenarios, and the application of advanced simulation and modeling techniques in this research.

A Three-State Space Modeling Method for Aircraft System Reliability Design

Reliability is an inherent attribute of a system through optimal system design. However, during the aircraft system development process, the reliability evaluation and system function design efforts are often disconnected, leading to a divide between reliability experts and system designers in their work schedule. This disconnect results in an inefficient aircraft system reliability optimization process, known as the “two-skin” phenomenon. To address this issue, a three-state space model is proposed. Firstly, an analysis was conducted on the relationship between the system function architecture developed by the system designers and the reliability evaluation performed by the reliability experts. Secondly, based on the principle of function flow, the state of failure was categorized into “physical failure” and “non-physical failure”. Additionally, a new state of “function loss” was introduced as the third state for the system, in addition to the traditional states of “normal” and “faulty”. Thirdly, through the state of “Function loss”, an effective integration of system fault modes and function modes was achieved, leading to an optimized system reliability model. A three-state space modeling method was then developed by transforming the system function architecture into a system reliability model. Finally, this new model was applied to an aircraft’s rudder and fly-by-wire control system. The results demonstrate that the function architecture at the design stage of the system can be accurately transformed into the new three-state space model. The structure aligns closely with the function architecture and can be effectively utilized in quantitative system reliability calculations. In this way, the process of ensuring system reliability can be seamlessly integrated into the system optimization design process. This integration alleviates the issue of disjointed work between reliability experts and system designers, leading to a more streamlined and efficient aircraft system optimization process.

Reliability importance analysis of uncertain random [formula omitted]-out-of-[formula omitted] systems with multiple states

This paper analyzes the importance of components within a multiple-state uncertain random k-out-of-n system, which is called ur-m-k-out-of-n system for short. This system simultaneously contains randomness and uncertainty. The states of components exhibit polymorphism and are defined as uncertain random variables. The component importance analysis is both a prerequisite and a foundation for system reliability optimization. For this reason, the marginal reliability importance (MRI) and joint reliability importance (JRI) are proposed to examine the importance of an individual component or couple components of ur-m-k-out-of-n systems using chance theory. Some theorems are given to calculate MRI and JRI. Moreover, this paper gives several numerical examples to illustrate how to calculate MRI and JRI.

Reliability Analysis and Optimization Method of a Mechanical System Based on the Response Surface Method and Sensitivity Analysis Method

Mechanical system reliability analysis constitutes a primary research focus in the field of engineering. This study aims to address the issue of complex mechanical systems with intricate mechanisms and nonlinear reliability equations that are challenging to solve. To this end, we present a reliability analysis and optimization methodology that merges the response surface and sensitivity analysis methods. A comprehensive formation of reliability assessment and optimization of complex mechanical systems is achieved by creating a response surface model to fit the complex state function and solving the reliability parameters, followed by an error sensitivity analysis to determine the mechanical system’s reliability adjustment strategy. Finally, these methods are applied to a cylindrical material transport device to preliminarily realize the reliability assessment and average reliability optimization goals. The study’s findings may offer a theoretical framework and research opportunities to evaluate and enhance the reliability of intricate mechanical systems.

Reliability Optimization of Uncertain Random Systems Combined With Burn-In and Preventive Maintenance

It is well-known that uncertain random systems suffered from a competing failure process have been applied in industrial applications. Extending previous studies, we optimize system cost combined with burn-in and preventive maintenance strategies. A system in burn-in process is only subject to uncertain continuous stationary degradation process while it experiences competitive degradation and shock processes simultaneously in the period of operation. The dependence assumes that shocks can accelerate the degradation by uncertain additional degradation shifts. A reliability optimization model for uncertain random systems is proposed where the reliability index is defined as a chance measure, and the optimal burn-in parameters and maintenance observation time are derived by the genetic algorithm. Finally, we conclude with a numerical example to illustrate the application of burn-in and maintenance methods.

Modified Wild Horse Optimizer for Constrained System Reliability Optimization

The last few decades have witnessed advancements in intelligent metaheuristic approaches and system reliability optimization. The huge progress in metaheuristic approaches can be viewed as the main motivator behind further refinement in the system reliability optimization process. Researchers have intensively studied system reliability optimization problems (SROPs) to obtain the optimal system design with several constraints in order to optimize the overall system reliability. This article proposes a modified wild horse optimizer (MWHO) for SROPs and investigates the reliability allocation of two complex SROPs, namely, complex bridge system (CBS) and life support system in space capsule (LSSSC), with the help of the same process. The effectiveness of this framework based on MWHO is demonstrated by comparing the results obtained with the results available in the literature. The proposed MWHO algorithm shows better efficiency, as it provides superior solutions to SROPs.

Reliability Optimization Method for Gas–Electric Integrated Energy Systems Considering Cyber–Physical Interactions

With the development in the field of energy and the growing demand for sustainable energy, gas–electric integrated energy systems are attracting attention as an emerging energy supply method. At the same time, with the deep application of information technology, the cyber–physical interactions of gas–electric integrated energy systems are increasingly enhanced. To this end, first, the reliability assessment indices of a gas–electric integrated energy system, which comprehensively considers the interactions between cyber–physical and different energy sources, are established in this paper to quantitatively assess the reliability level of the system under different fault and failure conditions. Second, to solve the reliability optimization problem, a comprehensive reliability enhancement optimization model is constructed in this paper, which targets the sum of the total penalties of the failure rate and average repair time modification. The impact of the cyber systems on the gas–electric integrated energy systems is transformed into a modification of the failure rate and the average repair time, and the model is solved by an adaptive Gaussian particle swarm optimization algorithm. Finally, the applicability and superiority of the adaptive Gaussian particle swarm optimization algorithm to the reliability optimization of the gas–electricity integrated energy system are verified by conducting simulation tests on the gas–electricity integrated energy system coupled with an 8-node distribution system and the 11-node natural gas system in Belgium. Furthermore, the effects of cyber systems and cyber-attacks on system reliability optimization are also analyzed to verify the effectiveness of the proposed method and the rationality of the newly defined reliability indices.

Energy Demand Prediction Based on Deep Learning Techniques

The development of renewable resources and the deregulation of the market have made forecasting energy demand more critical in recent years. Advanced intelligent models are created to ensure accurate power projections for several time horizons to address new difficulties. Intelligent forecasting algorithms are a fundamental component of smart grids and a powerful tool for reducing uncertainty in order to make more cost- and energy-efficient decisions about generation scheduling, system reliability and power optimization, and profitable smart grid operations. However, since many crucial tasks of power operators, such as load dispatch, rely on short-term forecasts, prediction accuracy in forecasting algorithms is highly desired. This essay suggests a model for estimating Denmark’s power use that can precisely forecast the month’s demand. In order to identify factors that may have an impact on the pattern of a number of unique qualities in the city direct consumption of electricity. The current paper also demonstrates how to use an ensemble deep learning technique and Random forest to dramatically increase prediction accuracy. In addition to their ensemble, we showed how well the individual Random forest performed.

System reliability optimization with two-sided power distributed component failure times

Many systems connected in series are subject to failure earlier in their design life. The failure rate of such systems has been described by a two-sided power distribution, which enables modeling the entirety of the bathtub curve failure modes with a single distribution. We propose a time-dependent reliability optimization model for improving a system’s reliability through the optimal allocation of redundant components whose failure distribution follows a two-sided power distribution. To solve the multi-criteria optimization model, we propose an algorithm that (i) identifies the scale tolerance at which a selected component will have a higher mean residual life, (ii) identifies the level of redundancy for enhancing system reliability, and (iii) identifies other optimal system characteristics, including minimized cost and minimized volume.

Reliability analysis of series and parallel systems with heterogeneous components under random shock environment

This article considers the reliability optimization for series and parallel systems under random shock environment, where the components come from a heterogeneous population consisting of some distinct subpopulations. We propose the extreme and cumulative shock models, where the random shock environment for each subsystem is modeled by the nonhomogeneous Poisson process. We analyze the reliability of systems with different components allocation policies, and then obtain the optimal policy that optimizes the system reliability. At the same time, we discuss the influence of the heterogeneity on the system reliability. An illustrative example is also presented.

Techno-Economic Green Optimization of Electrical Microgrid Using Swarm Metaheuristics

In electrical power engineering, elements such as reliability analysis, modeling, and optimization for complex systems are of the utmost importance. Although there exist myriad studies regarding reliability optimization with conventional methods, researchers are still seeking to find more efficient and accurate methods to address the issue of the redundancy allocation problem. To that effect, an ideal power energy management approach is put forward for the operation of a hybrid microgrid system with different kinds of productions. In the present study, we suggest three algorithms in order to optimize the series-parallel power energy system: the Firefly (FA), Bat (BA), and Interior Search (ISA) algorithms. Moreover, the reliability estimate of the system is solved with the Ushakov algorithm (UMGF). The components may completely fail, which decreases their performance rate. Furthermore, the optimization results are achieved using objective functions that include the total cost of the system, emission gases (NOX, SO2, and CO2) of the power production from fuel cells, diesel generators, and gas turbines, and take into consideration the dependability indices. Devices used in power subsystems are characterized based on their dependabilities, performances, capital costs, and maintenance costs. Reliability hinges on a functioning system, which naturally entails meeting customer demand; as a result, it is influenced by the accumulated batch curve. This method provides an idea with regards to the economic cost optimization of microgrid systems. Finally, we present the results of numeric simulations.

A Review of Optimization for System Reliability of Microgrid

Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids.

Performance indicator-based multi-objective reliability optimization for multi-type production systems with heterogeneous machines

• A novel system model: RRAP-CM-MCCS-based multitype production system • Realize the multiple machine deployment at each workstation (subsystem) • Present a new multi-objective application, a performance indicator-based multi-objective algorithm, ε-MOABC. • Provide quality solutions that are profitable in terms of cost savings. The demand for product manufacturing is gradually leading the direction of production. Therefore, a production line that can produce diversified products is critical for meeting the requirements of modern systems. This study investigates multi-objective optimization of the reliability of a multi-type production system. In particular, machine deployment at each workstation can be associated with different reliability depending on the purchasing year or brand. Consequently, this study applies a component mixing strategy to multi-type production systems to estimate the reliability of an ensemble of heterogeneous machines. The goal of the studied problem is to maximize all production lines, and the cold-standby redundancy strategy based on the continuous-time Markov chain model is employed to calculate the exact reliability of the system. A performance indicator-based multi-objective algorithm, ε-MOABC, was employed to search for a near-optimal solution. The experimental results show that the multi-objective solution effectively converges to the Pareto front of the knee region and, more importantly, it can achieve high system reliability at a streamlined cost.

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.

Optimization of Systems Reliability using RPGT

Optimization of Systems Reliability using RPGT

Methods for the reliability optimization of district-distributed heating systems with prosumers

District-distributed heating systems are considered as the result of the development of existing district heating systems, in which various energy technologies are integrated to achieve maximum efficiency of the heating to consumers. The distributed sector in these systems is associated, first of all, with the implementation of prosumers. The problem statement is determining such values of reliability parameters of components (in particular, failure rates) of studied system that provide the required level of reliability of heating to consumers at the minimum cost for ensuring this level of reliability taking into account the redundancy of prosumers. A methodology is based on the nodal probability of failure-free operation index, the Markov random process, the laws of heat transfer processes in heat supply, and some other methods. A case example is considered, an analysis of the results obtained is carried out, and directions for further research are formulated.