Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm [An article from: Reliability Engineering and System Safety] Buy on Amazon

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Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm [An article from: Reliability Engineering and System Safety]

Book Details

PublisherElsevier
ISBN / ASINB000PBZRIY
ISBN-13978B000PBZRI2
MarketplaceFrance  🇫🇷

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This digital document is a journal article from Reliability Engineering and System Safety, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
This paper presents a meta-heuristic algorithm, variable neighborhood search (VNS), to the redundancy allocation problem (RAP). The RAP, an NP-hard problem, has attracted the attention of much prior research, generally in a restricted form where each subsystem must consist of identical components. The newer meta-heuristic methods overcome this limitation and offer a practical way to solve large instances of the relaxed RAP where different components can be used in parallel. Authors' previously published work has shown promise for the variable neighborhood descent (VND) method, the simplest version among VNS variations, on RAP. The variable neighborhood search method itself has not been used in reliability design, yet it is a method that fits those combinatorial problems with potential neighborhood structures, as in the case of the RAP. Therefore, authors further extended their work to develop a VNS algorithm for the RAP and tested a set of well-known benchmark problems from the literature. Results on 33 test instances ranging from less to severely constrained conditions show that the variable neighborhood search method improves the performance of VND and provides a competitive solution quality at economically computational expense in comparison with the best-known heuristics including ant colony optimization, genetic algorithm, and tabu search.
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