System redundancy optimization with uncertain stress-based component reliability: Minimization of regret

Abstract

a b s t r a c tSystem reliability design optimization models have been developed for systems exposed to changing and diverse stress and usage conditions. Uncertainty is addressed through defining a future operating environment where component stresses have shifted or changed for different future usage scenarios. Due to unplanned variations or changing environments and operating stresses, component and system reliability often cannot be predicted or estimated without uncertainty. Component reliability can vary due to a relative increase/decrease of stresses or operating conditions. The uncertain parameters of stresses have been incorporated directly into the new decision-making model. Risk analysis perspectives, including risk-neutral and risk-averse, are considered as system reliability objective functions. A regret function is defined, and minimization of the maximum regret provides an objective function based on random future usage stresses. This is an entirely new formulation of the redundancy allocation problem, but it is a relevant one for some problem domains. The redundancy allocation problem is solved to select the best design solution when there are multiple choices of components and system-level constraints. Nonlinear programming and a neighborhood search heuristic method are recommended to obtain the integer solutions for risk-based formulations.

Reliability Equivalence Factors for Coherent System using Survival Signature

“…It is also applicable in the sensitivity analysis as marked by the research workers (Feng et al, 2016;Miro et al, 2019). Chatwattanasiri et al (2016) considered the uncertain stress-based reliability of components while optimizing the system redundancy. The survival signature approach was used by Wang et al (2023) to assess the reliability of standby redundant systems.…”

A weighted K-means clustering approach to solve the redundancy allocation problem of systems having components with different failures

“…Interested readers are referred to Soltani 16 for a comprehensive review of the works on reliability optimization of binary-state non-repairable systems. Chatwattanasiri et al 17 introduced an optimization reliability model considering uncertain parameters of stress in a new decision-making model. Cao et al 18 proposed a multi-objective reliability model.…”

A hybrid Jaya algorithm for reliability–redundancy allocation problems

“…Artificial Bee Colony (ABC) (Yeh and Hsieh 2011), a hybrid algorithm of space partitioning and tabu-genetic (SP/TG) (Ouzineb, Nourelfath, and Gendreau 2011) for non-homogeneous RRAP, Honey Bee Mating Optimization (HBMO) (Sadjadi and Soltani 2012), a new mixed strategy which uses cold-standby and active strategies with a proposed GA for reliability optimization of series-parallel systems (Ardakan and Hamadani 2014b), compromise programming (Soltani, Sadjadi, and Tavakkoli-Moghaddam 2015), binary equivalent models and Mixed Integer Nonlinear Programming (MINLP) for the cold standby RRAP (Feizollahi, Soltani, and Feyzollahi 2015), Immune Algorithm (IA) (Chen and You 2005;Chen 2006), a multi-objective multi-stage reliability growth planning strategy (Li, Mobin, and Keyser 2016) using a modified non-dominated sorting GA (NSGA-II) in the early product-development stage and also multi-objective reliability optimization using GA proposed by (Ardakan, Hamadani, and Alinaghian 2015), Improved Bat Algorithm (IBA) (Liu 2016), neighbourhood search heuristic method with nonlinear programming (Chatwattanasiri, Coit, and Wattanapongsakorn 2016), and a Penalty Jaya algorithm (Rao 2016) is a new simple and efficient algorithm. Similar to the other algorithms, it only has the common parameters that will be determined by the user like population number and iterations of algorithm without need of any specific control parameters that would be determined by the user.…”