Redundancy allocation is one of the adopted approaches that is used by system designers to improve the performance of systems. In this article, a new model and a novel‐solving method are provided to address the nonexponential redundancy allocation problem in series‐parallel systems with repairable components based on optimization via simulation approach and artificial neural network technique. Despite the previous researches, in this model the failure and repair times of the each component were considered to have nonnegative exponential distributions. This assumption makes the model closer to the reality where most of used components have greater chance to face a breakdown in comparison to new ones. The main aim of this research is the optimization of mean time to the first failure of the system via allocating the best redundant components for each subsystem. Since this objective function of the problem could not be explicitly mentioned, the simulation technique and artificial neural network were applied to model the problem, and different experimental designs were produced using design of experiment methods. To solve the problem, some metaheuristic algorithms were integrated with the simulation method. Several experiments were performed to test the proposed approach, and as the results show, the proposed approach is much more real than previous models, and also the near optimum solutions are promising.
Quality and Reliability Engineering International – Wiley
Published: Jan 1, 2018
Keywords: ; ; ;
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera