An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems
In this study, we propose an improved particle swarm optimization algorithm (IPSOA) to solve discounted redundancy allocation problems (DRAPs) in series-parallel systems. The system consists of subsystems in series with parallel components in each subsystem. Homogeneous redundant components are used...
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Veröffentlicht in: | Journal of intelligent manufacturing 2019-03, Vol.30 (3), p.1175-1194 |
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description | In this study, we propose an improved particle swarm optimization algorithm (IPSOA) to solve discounted redundancy allocation problems (DRAPs) in series-parallel systems. The system consists of subsystems in series with parallel components in each subsystem. Homogeneous redundant components are used to achieve a desirable system reliability. The components of each subsystem are characterized by their cost, weight, and reliability where the cost is calculated under an all unit discount policy. The goal is to find the optimum combination of the components for each subsystem so that the system reliability is maximized. After formulating the mathematical model, the proposed IPOSA is implemented to achieve the solution. Moreover, an experimental design approach is used to calibrate the algorithm’s parameters. Three numerical problems, each of which considered under several configurations, are discussed to demonstrate the applicability of the proposed procedures. In order to evaluate the accuracy and performance of our IPSOA, all the problems are also solved using two other meta-heuristics, namely, the homogeneous particle swarm optimization algorithm and tabu search. The numerical results show that, when solving DRAPs, IPSOA behaves better than the other two algorithms considered from both a solution quality and a computational viewpoint. |
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The system consists of subsystems in series with parallel components in each subsystem. Homogeneous redundant components are used to achieve a desirable system reliability. The components of each subsystem are characterized by their cost, weight, and reliability where the cost is calculated under an all unit discount policy. The goal is to find the optimum combination of the components for each subsystem so that the system reliability is maximized. After formulating the mathematical model, the proposed IPOSA is implemented to achieve the solution. Moreover, an experimental design approach is used to calibrate the algorithm’s parameters. Three numerical problems, each of which considered under several configurations, are discussed to demonstrate the applicability of the proposed procedures. In order to evaluate the accuracy and performance of our IPSOA, all the problems are also solved using two other meta-heuristics, namely, the homogeneous particle swarm optimization algorithm and tabu search. The numerical results show that, when solving DRAPs, IPSOA behaves better than the other two algorithms considered from both a solution quality and a computational viewpoint.</description><identifier>ISSN: 0956-5515</identifier><identifier>EISSN: 1572-8145</identifier><identifier>DOI: 10.1007/s10845-017-1311-9</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Business and Management ; Component reliability ; Control ; Design of experiments ; Experimental design ; Machines ; Manufacturing ; Mathematical models ; Mechatronics ; Optimization algorithms ; Optimization models ; Particle swarm optimization ; Processes ; Production ; Redundancy ; Redundant components ; Robotics ; Subsystems ; System reliability ; Tabu search ; Weight</subject><ispartof>Journal of intelligent manufacturing, 2019-03, Vol.30 (3), p.1175-1194</ispartof><rights>Springer Science+Business Media New York 2017</rights><rights>Journal of Intelligent Manufacturing is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c364t-b02b18b077ea9a697605c3b2dd868994949513a6238e648925f5bf737c8f0c3</citedby><cites>FETCH-LOGICAL-c364t-b02b18b077ea9a697605c3b2dd868994949513a6238e648925f5bf737c8f0c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10845-017-1311-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10845-017-1311-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Mousavi, Seyed Mohsen</creatorcontrib><creatorcontrib>Alikar, Najmeh</creatorcontrib><creatorcontrib>Tavana, Madjid</creatorcontrib><creatorcontrib>Di Caprio, Debora</creatorcontrib><title>An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems</title><title>Journal of intelligent manufacturing</title><addtitle>J Intell Manuf</addtitle><description>In this study, we propose an improved particle swarm optimization algorithm (IPSOA) to solve discounted redundancy allocation problems (DRAPs) in series-parallel systems. The system consists of subsystems in series with parallel components in each subsystem. Homogeneous redundant components are used to achieve a desirable system reliability. The components of each subsystem are characterized by their cost, weight, and reliability where the cost is calculated under an all unit discount policy. The goal is to find the optimum combination of the components for each subsystem so that the system reliability is maximized. After formulating the mathematical model, the proposed IPOSA is implemented to achieve the solution. Moreover, an experimental design approach is used to calibrate the algorithm’s parameters. Three numerical problems, each of which considered under several configurations, are discussed to demonstrate the applicability of the proposed procedures. 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The numerical results show that, when solving DRAPs, IPSOA behaves better than the other two algorithms considered from both a solution quality and a computational viewpoint.</description><subject>Algorithms</subject><subject>Business and Management</subject><subject>Component reliability</subject><subject>Control</subject><subject>Design of experiments</subject><subject>Experimental design</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Mathematical models</subject><subject>Mechatronics</subject><subject>Optimization algorithms</subject><subject>Optimization models</subject><subject>Particle swarm optimization</subject><subject>Processes</subject><subject>Production</subject><subject>Redundancy</subject><subject>Redundant components</subject><subject>Robotics</subject><subject>Subsystems</subject><subject>System reliability</subject><subject>Tabu 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Caprio, Debora</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems</atitle><jtitle>Journal of intelligent manufacturing</jtitle><stitle>J Intell Manuf</stitle><date>2019-03-01</date><risdate>2019</risdate><volume>30</volume><issue>3</issue><spage>1175</spage><epage>1194</epage><pages>1175-1194</pages><issn>0956-5515</issn><eissn>1572-8145</eissn><abstract>In this study, we propose an improved particle swarm optimization algorithm (IPSOA) to solve discounted redundancy allocation problems (DRAPs) in series-parallel systems. The system consists of subsystems in series with parallel components in each subsystem. Homogeneous redundant components are used to achieve a desirable system reliability. The components of each subsystem are characterized by their cost, weight, and reliability where the cost is calculated under an all unit discount policy. The goal is to find the optimum combination of the components for each subsystem so that the system reliability is maximized. After formulating the mathematical model, the proposed IPOSA is implemented to achieve the solution. Moreover, an experimental design approach is used to calibrate the algorithm’s parameters. Three numerical problems, each of which considered under several configurations, are discussed to demonstrate the applicability of the proposed procedures. In order to evaluate the accuracy and performance of our IPSOA, all the problems are also solved using two other meta-heuristics, namely, the homogeneous particle swarm optimization algorithm and tabu search. The numerical results show that, when solving DRAPs, IPSOA behaves better than the other two algorithms considered from both a solution quality and a computational viewpoint.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10845-017-1311-9</doi><tpages>20</tpages></addata></record> |
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subjects | Algorithms Business and Management Component reliability Control Design of experiments Experimental design Machines Manufacturing Mathematical models Mechatronics Optimization algorithms Optimization models Particle swarm optimization Processes Production Redundancy Redundant components Robotics Subsystems System reliability Tabu search Weight |
title | An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems |
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