A bi-objective model to optimize reliability and cost of k-out-of-n series-parallel systems with tri-state components

Abstract

Abstract. 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 the 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 con gured in parallel and the subsystem works under k-out-of-n policy. Furthermore, to enhance system reliability, technical and organizational activities that can a ect failure rates of the components, and hence can improve the system performance are also taken into account. The aim is to nd 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.

Reliability-redundancy allocation Problem of a queueing system considering energy consumption

“…In the SQP algorithm, we use the center point of the grid as the initial point. For instance, the initial point for grid (1,1) is considered as 12 ( 2, 2)…”

“…Due to the complexity of the RRAP, most of the researches has focused on developing heuristic and metaheuristic approaches. In this regard, we can refer to Simulated Annealing (SA) [8], Genetic Algorithm (GA) [4,[9][10][11][12], Particle Swarm Optimization (PSO) [5,6,[13][14][15][16], Artificial bee colony algorithm [3,17], Artificial immune search [18], Biogeography-based optimization (BBO) [19], fruit fly optimization algorithm [20], Markov decision process [21], Stochastic Fractal Search (SFS) [22], and hybrid algorithms such as SFS-GA [23]. In addition to heuristic and metaheuristic algorithms, simulation-based solution approaches [24] and exact solution methods such as implicit enumeration, branch-and-bound, and dynamic programming have also been used to solve RRAP [23].…”

Redundancy allocation problem with a mixed strategy for a system with k-out-of-n subsystems and time-dependent failure rates based on Weibull distribution: An optimization via simulation approach

“…Also, Tavakkoli-Moghaddam et al [11] solved Coit (2003) model using GA with a new definition of chromosomes, crossovers and mutations. Also, there are many papers in RAP scope which used meta-heuristic methods like [12][13][14][15]. Garg et al [16] presented a bi-objective reliability redundancy allocation problem for series-parallel system where reliability of the system and the corresponding designing cost are considered as two different objectives.…”