scholarly journals Application of an Intelligent Hybrid Metaheuristic Algorithm for Multiobjective Redundancy Allocation Problem with Sustainable Maintenance

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tri Tjahjono ◽  
Dinesh Mavaluru ◽  
Dowlath Fathima ◽  
Akila Thiyagarajan ◽  
Wanich Suksatan ◽  
...  
Keyword(s):  
System Reliability ◽  
Failure Time ◽  
Spare Parts ◽  
Allocation Problem ◽  
Test Problems ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Repair Costs ◽  
Whale Optimization ◽  
Distribution Parameters

The present study aimed to optimize the redundancy allocation problem based on sustainable maintenance. For this purpose, the goal is to design a complex system based on redundancy allocation by considering the weight and reliability criteria of the system and the maintenance and repair costs through the sustainability approach. In this regard, a mathematical model has been developed. This model minimizes system reliability and system weight simultaneously. There are also budget constraints on repair costs, environmental costs, purchase of spare parts, and energy risk costs. In order to optimize this model, a hybrid algorithm based on Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), and Simulated Annealing (SA) is proposed. Accordingly, 81 test problems are provided and optimized by the proposed algorithm. The obtained numerical results indicate that, with increasing failure time of each component, the system’s reliability increases and the weight of the whole system increases. Moreover, changing the Weibull distribution parameters directly affects the total amount of system reliability, but does not have a definite and accurate effect on the total weight of the system. Moreover, increasing the budget for maintenance leads to finding solutions with more reliability and less weight.

scholarly journals A New Biobjective Model to Optimize Integrated Redundancy Allocation and Reliability-Centered Maintenance Problems in a System Using Metaheuristics

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Shima MohammadZadeh Dogahe ◽  
Seyed Jafar Sadjadi
Keyword(s):  
System Reliability ◽  
Net Present Value ◽  
Programming Model ◽  
Allocation Problem ◽  
Test Problems ◽  
Electronic Components ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Reliability Centered Maintenance ◽  
Multiobjective Particle Swarm Optimization

A novel integrated model is proposed to optimize the redundancy allocation problem (RAP) and the reliability-centered maintenance (RCM) simultaneously. A system of both repairable and nonrepairable components has been considered. In this system, electronic components are nonrepairable while mechanical components are mostly repairable. For nonrepairable components, a redundancy allocation problem is dealt with to determine optimal redundancy strategy and number of redundant components to be implemented in each subsystem. In addition, a maintenance scheduling problem is considered for repairable components in order to identify the best maintenance policy and optimize system reliability. Both active and cold standby redundancy strategies have been taken into account for electronic components. Also, net present value of the secondary cost including operational and maintenance costs has been calculated. The problem is formulated as a biobjective mathematical programming model aiming to reach a tradeoff between system reliability and cost. Three metaheuristic algorithms are employed to solve the proposed model: Nondominated Sorting Genetic Algorithm (NSGA-II), Multiobjective Particle Swarm Optimization (MOPSO), and Multiobjective Firefly Algorithm (MOFA). Several test problems are solved using the mentioned algorithms to test efficiency and effectiveness of the solution approaches and obtained results are analyzed.

Distributionally Robust Design for Redundancy Allocation

2020 ◽  
Vol 32 (3) ◽  
pp. 620-640
Author(s):  
Shuming Wang ◽  
Yan-Fu Li
Keyword(s):  
System Reliability ◽  
Linear Program ◽  
Allocation Problem ◽  
Mixed Integer ◽  
Modeling Framework ◽  
Worst Case ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Case System ◽  
Distributionally Robust

In this paper, we consider a redundancy allocation problem for a series parallel system with uncertain component lifetimes that minimizes system costs while safeguarding system reliability over a given threshold level. We consider mixed redundancy strategies of cold standby and active redundancy with multiple types of components. We address lifetime uncertainty in the framework of distributionally robust optimization. In particular, we assume the probability distributions of the component lifetimes are not exactly known with only limited distributional information (e.g., mean, dispersion, and support) being available. We protect the worst-case system reliability constraint over all the possible component lifetime distributions that are consistent with the given distributional characteristics. The proposed modeling framework enjoys computationally attractive structures. The evaluation of the worst-case system reliability in our redundancy allocation problem can be transformed into a linear program, and the resulting overall redundancy allocation optimization problem can be cast as a mixed integer linear program that does not induce any additional integer variables (other than original allocation variables). In addition, the extreme joint distribution of component lifetimes can be efficiently recovered by solving a linear program. Our modeling framework can also be extended to incorporate the startup failures and common-cause failures for cold standbys and active parallels, respectively, to cater to more computationally complex settings. Finally, the computational experiments positively demonstrate the performance of the proposed approach in protecting system reliability.

Redundancy allocation problem in a bridge system with dependent subsystems

2018 ◽  
Vol 233 (4) ◽  
pp. 658-669
Author(s):  
Kamyar Sabri-Laghaie ◽  
Milad Eshkevari ◽  
Mahdi Fathi ◽  
Enrico Zio
Keyword(s):  
System Reliability ◽  
Allocation Problem ◽  
Bridge Structure ◽  
Swarm Optimization ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation ◽  
Reliability Design ◽  
Copula Theory ◽  
Real World Applications ◽  
System Configurations

The redundancy allocation problem is an important problem in system reliability design. Many researchers have investigated the redundancy allocation problem under different assumptions and for various system configurations. However, most of the studies have disregarded the dependence among components and subsystems. In real-world applications, the performance of components and subsystems can affect each others. For instance, the heat radiated by a subsystem can accelerate degradation of adjacent components or subsystems. In this article, a procedure is proposed for solving the redundancy allocation problem of a bridge structure with dependent subsystems. Copula theory is utilized for modeling dependence among subsystems, and artificial neural network and particle swarm optimization are applied for finding the best redundancy allocation. A numerical example is included to elaborate the proposed procedure and show its applicability.

scholarly journals MILP Models for Complex System Reliability Redundancy Allocation with Mixed Components

2020 ◽  
Vol 32 (3) ◽  
pp. 600-619
Author(s):  
Young Woong Park
Keyword(s):  
Complex System ◽  
Sample Size ◽  
System Reliability ◽  
Optimal Allocation ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Allocation Problem ◽  
Mixed Integer ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation

The redundancy allocation problem (RAP) aims to find an optimal allocation of redundant components subject to resource constraints. In this paper, mixed integer linear programming (MILP) models and MILP-based algorithms are proposed for complex system reliability redundancy allocation problem with mixed components, where the system have bridges or interconnecting subsystems and each subsystem can have mixed types of components. Unlike the other algorithms in the literature, the proposed MILP models view the problem from a different point of view and approximate the nonconvex nonlinear system reliability function of a complex system using random samples. The solution to the MILP converges to the optimal solution of the original problem as sample size increases. In addition, data aggregation-based algorithms are proposed to improve the solution time and quality based on the proposed MILP models. A computational experiment shows that the proposed models and algorithms converge to the optimal or best-known solution as sample size increases. The proposed algorithms outperform popular metaheuristic algorithms in the literature.

Efficiently Solving the Redundancy Allocation Problem Using Tabu Search

2003 ◽  
Vol 35 (6) ◽  
pp. 515-526 ◽  
Author(s):  
Sadan Kulturel-Konak ◽  
Alice E. Smith ◽  
David W. Coit
Keyword(s):  
Tabu Search ◽  
Allocation Problem ◽  
Redundancy Allocation Problem ◽  
Redundancy Allocation
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