Availability modeling and optimization of dynamic multi-state series–parallel systems with random reconfiguration
Most studies on multi-state series–parallel systems focus on the static type of system architecture. However, it is insufficient to model many complex industrial systems having several operation phases and each requires a subset of the subsystems combined together to perform certain tasks. To bridge...
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| Veröffentlicht in: | Reliability engineering & system safety 2014-07, Vol.127, p.47-57 |
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| creator | Li, Y.F. Peng, R. |
| description | Most studies on multi-state series–parallel systems focus on the static type of system architecture. However, it is insufficient to model many complex industrial systems having several operation phases and each requires a subset of the subsystems combined together to perform certain tasks. To bridge this gap, this study takes into account this type of dynamic behavior in the multi-state series–parallel system and proposes an analytical approach to calculate the system availability and the operation cost. In this approach, Markov process is used to model the dynamics of system phase changing and component state changing, Markov reward model is used to calculate the operation cost associated with the dynamics, and universal generating function (UGF) is used to build system availability function from the system phase model and the component models. Based upon these models, an optimization problem is formulated to minimize the total system cost with the constraint that system availability is greater than a desired level. The genetic algorithm is then applied to solve the optimization problem. The proposed modeling and solution procedures are illustrated on a system design problem modified from a real-world maritime oil transportation system. |
| doi_str_mv | 10.1016/j.ress.2014.03.005 |
| format | Article |
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However, it is insufficient to model many complex industrial systems having several operation phases and each requires a subset of the subsystems combined together to perform certain tasks. To bridge this gap, this study takes into account this type of dynamic behavior in the multi-state series–parallel system and proposes an analytical approach to calculate the system availability and the operation cost. In this approach, Markov process is used to model the dynamics of system phase changing and component state changing, Markov reward model is used to calculate the operation cost associated with the dynamics, and universal generating function (UGF) is used to build system availability function from the system phase model and the component models. Based upon these models, an optimization problem is formulated to minimize the total system cost with the constraint that system availability is greater than a desired level. 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Management science ; Optimization ; Phases ; Reliability theory. Replacement problems ; System dynamics ; Transportation and distribution of crude oils and liquid petroleum products. Ships. Pipelines. Terminals. Service stations ; Universal generating function</subject><ispartof>Reliability engineering & system safety, 2014-07, Vol.127, p.47-57</ispartof><rights>2014 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c430t-d4f05b261838982891673beab4b28239e1a88942f7cf6fef13be7b8a89d2d8663</citedby><cites>FETCH-LOGICAL-c430t-d4f05b261838982891673beab4b28239e1a88942f7cf6fef13be7b8a89d2d8663</cites><orcidid>0000-0001-6596-0334 ; 0000-0001-5755-7115</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ress.2014.03.005$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,780,784,885,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28447467$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-04366295$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Y.F.</creatorcontrib><creatorcontrib>Peng, R.</creatorcontrib><title>Availability modeling and optimization of dynamic multi-state series–parallel systems with random reconfiguration</title><title>Reliability engineering & system safety</title><description>Most studies on multi-state series–parallel systems focus on the static type of system architecture. However, it is insufficient to model many complex industrial systems having several operation phases and each requires a subset of the subsystems combined together to perform certain tasks. To bridge this gap, this study takes into account this type of dynamic behavior in the multi-state series–parallel system and proposes an analytical approach to calculate the system availability and the operation cost. In this approach, Markov process is used to model the dynamics of system phase changing and component state changing, Markov reward model is used to calculate the operation cost associated with the dynamics, and universal generating function (UGF) is used to build system availability function from the system phase model and the component models. Based upon these models, an optimization problem is formulated to minimize the total system cost with the constraint that system availability is greater than a desired level. The genetic algorithm is then applied to solve the optimization problem. The proposed modeling and solution procedures are illustrated on a system design problem modified from a real-world maritime oil transportation system.</description><subject>Applied sciences</subject><subject>Availability</subject><subject>Cost analysis</subject><subject>Crude oil, natural gas and petroleum products</subject><subject>Dynamical systems</subject><subject>Dynamics</subject><subject>Energy</subject><subject>Engineering Sciences</subject><subject>Exact sciences and technology</subject><subject>Fuels</subject><subject>Genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Ground, air and sea transportation, marine construction</subject><subject>Marine and water way transportation and traffic</subject><subject>Markov process</subject><subject>Markov reward model</subject><subject>Mathematical models</subject><subject>Multi-state series–parallel system</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Optimization</subject><subject>Phases</subject><subject>Reliability theory. Replacement problems</subject><subject>System dynamics</subject><subject>Transportation and distribution of crude oils and liquid petroleum products. Ships. Pipelines. Terminals. 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Management science</topic><topic>Optimization</topic><topic>Phases</topic><topic>Reliability theory. Replacement problems</topic><topic>System dynamics</topic><topic>Transportation and distribution of crude oils and liquid petroleum products. Ships. Pipelines. Terminals. Service stations</topic><topic>Universal generating function</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Y.F.</creatorcontrib><creatorcontrib>Peng, R.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Safety Science and Risk</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Reliability engineering & system safety</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Y.F.</au><au>Peng, R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Availability modeling and optimization of dynamic multi-state series–parallel systems with random reconfiguration</atitle><jtitle>Reliability engineering & system safety</jtitle><date>2014-07-01</date><risdate>2014</risdate><volume>127</volume><spage>47</spage><epage>57</epage><pages>47-57</pages><issn>0951-8320</issn><eissn>1879-0836</eissn><abstract>Most studies on multi-state series–parallel systems focus on the static type of system architecture. However, it is insufficient to model many complex industrial systems having several operation phases and each requires a subset of the subsystems combined together to perform certain tasks. To bridge this gap, this study takes into account this type of dynamic behavior in the multi-state series–parallel system and proposes an analytical approach to calculate the system availability and the operation cost. In this approach, Markov process is used to model the dynamics of system phase changing and component state changing, Markov reward model is used to calculate the operation cost associated with the dynamics, and universal generating function (UGF) is used to build system availability function from the system phase model and the component models. Based upon these models, an optimization problem is formulated to minimize the total system cost with the constraint that system availability is greater than a desired level. 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| ispartof | Reliability engineering & system safety, 2014-07, Vol.127, p.47-57 |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Applied sciences Availability Cost analysis Crude oil, natural gas and petroleum products Dynamical systems Dynamics Energy Engineering Sciences Exact sciences and technology Fuels Genetic algorithm Genetic algorithms Ground, air and sea transportation, marine construction Marine and water way transportation and traffic Markov process Markov reward model Mathematical models Multi-state series–parallel system Operational research and scientific management Operational research. Management science Optimization Phases Reliability theory. Replacement problems System dynamics Transportation and distribution of crude oils and liquid petroleum products. Ships. Pipelines. Terminals. Service stations Universal generating function |
| title | Availability modeling and optimization of dynamic multi-state series–parallel systems with random reconfiguration |
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