Solving equilibrium standby redundancy optimization problem by hybrid PSO algorithm

Redundancy allocation is a direct way of enhancing the series-parallel system lifetime and reliability. Since it is difficult to obtain the exact probability distributions about the lifetimes of components, fuzzy random variables are used to characterize them. Under the given system weights and cost...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Soft computing (Berlin, Germany) Germany), 2018-09, Vol.22 (17), p.5631-5645
Hauptverfasser: Chen, Yanju, Gao, Jinwu, Yang, Guoqing, Liu, Yankui
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 5645
container_issue 17
container_start_page 5631
container_title Soft computing (Berlin, Germany)
container_volume 22
creator Chen, Yanju
Gao, Jinwu
Yang, Guoqing
Liu, Yankui
description Redundancy allocation is a direct way of enhancing the series-parallel system lifetime and reliability. Since it is difficult to obtain the exact probability distributions about the lifetimes of components, fuzzy random variables are used to characterize them. Under the given system weights and cost constraints, we maximize the equilibrium optimistic system lifetime of redundant elements. This paper proposes an equilibrium optimization model for the standby redundancy system. Since the exact analytical expressions of the equilibrium optimistic system lifetimes are unavailable in general case, the proposed model cannot be analytically solved. Under mild assumptions, the new equilibrium model can be divided into its equivalent stochastic programming subproblems. Moreover, a new approximation method is proposed to solve the general equilibrium model. For the equivalent stochastic programming subproblems, sample average approximation (SAA) is adapted to gain their SAA problems. A hybrid particle swarm optimization algorithm with local search is designed to solve the SAA problems. Several numerical experiments are conducted to investigate the effectiveness of proposed model and designed solution method. The comparative studies indicate the randomness, and fuzziness cannot be ignored in the equilibrium standby redundancy optimization problem.
doi_str_mv 10.1007/s00500-017-2552-4
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2918058735</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2918058735</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-9e5d3e92b146f6adf7ee1b55303927d13e0cff0093353f6a54d1fee1f37f9aa63</originalsourceid><addsrcrecordid>eNp1kE1LAzEQhoMoWKs_wFvAc3SSbDbdoxS_QKhQPYfsJmlT9qNNdoX115u6gidPM4fneWd4EbqmcEsB5F0EEAAEqCRMCEayEzSjGedEZrI4_dkZkXnGz9FFjDsARqXgM7Red_WnbzfYHgZf-zL4ocGx160pRxysGVqj22rE3b73jf_Sve9avA9dWdsGJ2Q7JsXgt_UK63rTBd9vm0t05nQd7dXvnKOPx4f35TN5XT29LO9fScVp3pPCCsNtwUqa5S7XxklraSkEB14waSi3UDkHUHAueAJEZqhLiOPSFVrnfI5uptz0z2GwsVe7bghtOqlYQRcgFjKZc0QnqgpdjME6tQ--0WFUFNSxOzV1p1J36tidypLDJicmtt3Y8Jf8v_QNvwFymw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2918058735</pqid></control><display><type>article</type><title>Solving equilibrium standby redundancy optimization problem by hybrid PSO algorithm</title><source>ProQuest Central UK/Ireland</source><source>SpringerLink Journals - AutoHoldings</source><source>ProQuest Central</source><creator>Chen, Yanju ; Gao, Jinwu ; Yang, Guoqing ; Liu, Yankui</creator><creatorcontrib>Chen, Yanju ; Gao, Jinwu ; Yang, Guoqing ; Liu, Yankui</creatorcontrib><description>Redundancy allocation is a direct way of enhancing the series-parallel system lifetime and reliability. Since it is difficult to obtain the exact probability distributions about the lifetimes of components, fuzzy random variables are used to characterize them. Under the given system weights and cost constraints, we maximize the equilibrium optimistic system lifetime of redundant elements. This paper proposes an equilibrium optimization model for the standby redundancy system. Since the exact analytical expressions of the equilibrium optimistic system lifetimes are unavailable in general case, the proposed model cannot be analytically solved. Under mild assumptions, the new equilibrium model can be divided into its equivalent stochastic programming subproblems. Moreover, a new approximation method is proposed to solve the general equilibrium model. For the equivalent stochastic programming subproblems, sample average approximation (SAA) is adapted to gain their SAA problems. A hybrid particle swarm optimization algorithm with local search is designed to solve the SAA problems. Several numerical experiments are conducted to investigate the effectiveness of proposed model and designed solution method. The comparative studies indicate the randomness, and fuzziness cannot be ignored in the equilibrium standby redundancy optimization problem.</description><identifier>ISSN: 1432-7643</identifier><identifier>EISSN: 1433-7479</identifier><identifier>DOI: 10.1007/s00500-017-2552-4</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Approximation ; Artificial Intelligence ; Comparative studies ; Computational Intelligence ; Control ; Design ; Engineering ; Equilibrium ; Equivalence ; Failure ; Focus ; Fuzzy sets ; Genetic algorithms ; Integer programming ; Lifetime ; Mathematical Logic and Foundations ; Mechatronics ; Methods ; Normal distribution ; Optimization ; Optimization models ; Particle swarm optimization ; Random variables ; Randomness ; Redundancy ; Robotics ; Stochastic programming</subject><ispartof>Soft computing (Berlin, Germany), 2018-09, Vol.22 (17), p.5631-5645</ispartof><rights>Springer-Verlag Berlin Heidelberg 2017</rights><rights>Springer-Verlag Berlin Heidelberg 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-9e5d3e92b146f6adf7ee1b55303927d13e0cff0093353f6a54d1fee1f37f9aa63</citedby><cites>FETCH-LOGICAL-c316t-9e5d3e92b146f6adf7ee1b55303927d13e0cff0093353f6a54d1fee1f37f9aa63</cites><orcidid>0000-0002-3633-2184</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00500-017-2552-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918058735?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,41488,42557,43805,51319,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Chen, Yanju</creatorcontrib><creatorcontrib>Gao, Jinwu</creatorcontrib><creatorcontrib>Yang, Guoqing</creatorcontrib><creatorcontrib>Liu, Yankui</creatorcontrib><title>Solving equilibrium standby redundancy optimization problem by hybrid PSO algorithm</title><title>Soft computing (Berlin, Germany)</title><addtitle>Soft Comput</addtitle><description>Redundancy allocation is a direct way of enhancing the series-parallel system lifetime and reliability. Since it is difficult to obtain the exact probability distributions about the lifetimes of components, fuzzy random variables are used to characterize them. Under the given system weights and cost constraints, we maximize the equilibrium optimistic system lifetime of redundant elements. This paper proposes an equilibrium optimization model for the standby redundancy system. Since the exact analytical expressions of the equilibrium optimistic system lifetimes are unavailable in general case, the proposed model cannot be analytically solved. Under mild assumptions, the new equilibrium model can be divided into its equivalent stochastic programming subproblems. Moreover, a new approximation method is proposed to solve the general equilibrium model. For the equivalent stochastic programming subproblems, sample average approximation (SAA) is adapted to gain their SAA problems. A hybrid particle swarm optimization algorithm with local search is designed to solve the SAA problems. Several numerical experiments are conducted to investigate the effectiveness of proposed model and designed solution method. The comparative studies indicate the randomness, and fuzziness cannot be ignored in the equilibrium standby redundancy optimization problem.</description><subject>Algorithms</subject><subject>Approximation</subject><subject>Artificial Intelligence</subject><subject>Comparative studies</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Design</subject><subject>Engineering</subject><subject>Equilibrium</subject><subject>Equivalence</subject><subject>Failure</subject><subject>Focus</subject><subject>Fuzzy sets</subject><subject>Genetic algorithms</subject><subject>Integer programming</subject><subject>Lifetime</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Methods</subject><subject>Normal distribution</subject><subject>Optimization</subject><subject>Optimization models</subject><subject>Particle swarm optimization</subject><subject>Random variables</subject><subject>Randomness</subject><subject>Redundancy</subject><subject>Robotics</subject><subject>Stochastic programming</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kE1LAzEQhoMoWKs_wFvAc3SSbDbdoxS_QKhQPYfsJmlT9qNNdoX115u6gidPM4fneWd4EbqmcEsB5F0EEAAEqCRMCEayEzSjGedEZrI4_dkZkXnGz9FFjDsARqXgM7Red_WnbzfYHgZf-zL4ocGx160pRxysGVqj22rE3b73jf_Sve9avA9dWdsGJ2Q7JsXgt_UK63rTBd9vm0t05nQd7dXvnKOPx4f35TN5XT29LO9fScVp3pPCCsNtwUqa5S7XxklraSkEB14waSi3UDkHUHAueAJEZqhLiOPSFVrnfI5uptz0z2GwsVe7bghtOqlYQRcgFjKZc0QnqgpdjME6tQ--0WFUFNSxOzV1p1J36tidypLDJicmtt3Y8Jf8v_QNvwFymw</recordid><startdate>20180901</startdate><enddate>20180901</enddate><creator>Chen, Yanju</creator><creator>Gao, Jinwu</creator><creator>Yang, Guoqing</creator><creator>Liu, Yankui</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-3633-2184</orcidid></search><sort><creationdate>20180901</creationdate><title>Solving equilibrium standby redundancy optimization problem by hybrid PSO algorithm</title><author>Chen, Yanju ; Gao, Jinwu ; Yang, Guoqing ; Liu, Yankui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-9e5d3e92b146f6adf7ee1b55303927d13e0cff0093353f6a54d1fee1f37f9aa63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Algorithms</topic><topic>Approximation</topic><topic>Artificial Intelligence</topic><topic>Comparative studies</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Design</topic><topic>Engineering</topic><topic>Equilibrium</topic><topic>Equivalence</topic><topic>Failure</topic><topic>Focus</topic><topic>Fuzzy sets</topic><topic>Genetic algorithms</topic><topic>Integer programming</topic><topic>Lifetime</topic><topic>Mathematical Logic and Foundations</topic><topic>Mechatronics</topic><topic>Methods</topic><topic>Normal distribution</topic><topic>Optimization</topic><topic>Optimization models</topic><topic>Particle swarm optimization</topic><topic>Random variables</topic><topic>Randomness</topic><topic>Redundancy</topic><topic>Robotics</topic><topic>Stochastic programming</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Yanju</creatorcontrib><creatorcontrib>Gao, Jinwu</creatorcontrib><creatorcontrib>Yang, Guoqing</creatorcontrib><creatorcontrib>Liu, Yankui</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Soft computing (Berlin, Germany)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Yanju</au><au>Gao, Jinwu</au><au>Yang, Guoqing</au><au>Liu, Yankui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Solving equilibrium standby redundancy optimization problem by hybrid PSO algorithm</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2018-09-01</date><risdate>2018</risdate><volume>22</volume><issue>17</issue><spage>5631</spage><epage>5645</epage><pages>5631-5645</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>Redundancy allocation is a direct way of enhancing the series-parallel system lifetime and reliability. Since it is difficult to obtain the exact probability distributions about the lifetimes of components, fuzzy random variables are used to characterize them. Under the given system weights and cost constraints, we maximize the equilibrium optimistic system lifetime of redundant elements. This paper proposes an equilibrium optimization model for the standby redundancy system. Since the exact analytical expressions of the equilibrium optimistic system lifetimes are unavailable in general case, the proposed model cannot be analytically solved. Under mild assumptions, the new equilibrium model can be divided into its equivalent stochastic programming subproblems. Moreover, a new approximation method is proposed to solve the general equilibrium model. For the equivalent stochastic programming subproblems, sample average approximation (SAA) is adapted to gain their SAA problems. A hybrid particle swarm optimization algorithm with local search is designed to solve the SAA problems. Several numerical experiments are conducted to investigate the effectiveness of proposed model and designed solution method. The comparative studies indicate the randomness, and fuzziness cannot be ignored in the equilibrium standby redundancy optimization problem.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00500-017-2552-4</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-3633-2184</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 1432-7643
ispartof Soft computing (Berlin, Germany), 2018-09, Vol.22 (17), p.5631-5645
issn 1432-7643
1433-7479
language eng
recordid cdi_proquest_journals_2918058735
source ProQuest Central UK/Ireland; SpringerLink Journals - AutoHoldings; ProQuest Central
subjects Algorithms
Approximation
Artificial Intelligence
Comparative studies
Computational Intelligence
Control
Design
Engineering
Equilibrium
Equivalence
Failure
Focus
Fuzzy sets
Genetic algorithms
Integer programming
Lifetime
Mathematical Logic and Foundations
Mechatronics
Methods
Normal distribution
Optimization
Optimization models
Particle swarm optimization
Random variables
Randomness
Redundancy
Robotics
Stochastic programming
title Solving equilibrium standby redundancy optimization problem by hybrid PSO algorithm
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T12%3A40%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Solving%20equilibrium%20standby%20redundancy%20optimization%20problem%20by%20hybrid%20PSO%20algorithm&rft.jtitle=Soft%20computing%20(Berlin,%20Germany)&rft.au=Chen,%20Yanju&rft.date=2018-09-01&rft.volume=22&rft.issue=17&rft.spage=5631&rft.epage=5645&rft.pages=5631-5645&rft.issn=1432-7643&rft.eissn=1433-7479&rft_id=info:doi/10.1007/s00500-017-2552-4&rft_dat=%3Cproquest_cross%3E2918058735%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2918058735&rft_id=info:pmid/&rfr_iscdi=true