Simplified swarm optimization in disassembly sequencing problems with learning effects
In classical disassembly sequencing problems (DSPs), the disassembly time of each item is assumed fixed and sequence-independent. From a practical perspective, the actual processing time of a component could depend on its position in the sequence. In this paper, a novel DSP called the learning-effec...
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| Veröffentlicht in: | Computers & operations research 2012-09, Vol.39 (9), p.2168-2177 |
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| creator | Yeh, Wei-Chang |
| description | In classical disassembly sequencing problems (DSPs), the disassembly time of each item is assumed fixed and sequence-independent. From a practical perspective, the actual processing time of a component could depend on its position in the sequence. In this paper, a novel DSP called the learning-effect DSP (LDSP) is proposed by considering the general effects of learning in DSP. A modified simplified swarm optimization (SSO) method developed by revising the most recently published variants of SSO is proposed to solve this new problem. The presented SSO scheme improves the update mechanism, which is the core of any soft computing based methods, and revises the self-adaptive parameter control procedure. The conducted computational experiment with up to 500 components reflects the effectiveness of the modified SSO method in terms of final accuracy, convergence speed, and robustness.
► We model a novel disassembly sequencing problems by considering the learning-effect DSP (LDSP). ► We modified the simplified swarm optimization (SSO) to solve LDSP. ► We improved the update mechanism and revised the self-adaptive parameter control procedure. ► The conducted computational experiment with up to 500 components reflects the effectiveness of the modified SSO method in terms of final accuracy, convergence speed, and robustness. |
| doi_str_mv | 10.1016/j.cor.2011.10.027 |
| format | Article |
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► We model a novel disassembly sequencing problems by considering the learning-effect DSP (LDSP). ► We modified the simplified swarm optimization (SSO) to solve LDSP. ► We improved the update mechanism and revised the self-adaptive parameter control procedure. ► The conducted computational experiment with up to 500 components reflects the effectiveness of the modified SSO method in terms of final accuracy, convergence speed, and robustness.</description><identifier>ISSN: 0305-0548</identifier><identifier>EISSN: 1873-765X</identifier><identifier>EISSN: 0305-0548</identifier><identifier>DOI: 10.1016/j.cor.2011.10.027</identifier><identifier>CODEN: CMORAP</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Applied sciences ; Assembly lines ; Digital signal processing ; Disassembly ; Disassembly sequencing problem ; Dismantling ; Exact sciences and technology ; Inventory control, production control. Distribution ; Learning ; Learning effects ; Mechanical engineering. Machine design ; Operational research and scientific management ; Operational research. Management science ; Optimization ; Optimization algorithms ; Robustness ; Self-adaptive parameter control ; Sequencing ; Sequential scheduling ; Simplified swarm optimization (SSO) ; Soft computing ; Studies ; Update mechanism</subject><ispartof>Computers & operations research, 2012-09, Vol.39 (9), p.2168-2177</ispartof><rights>2011 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Pergamon Press Inc. Sep 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c418t-337684080b7215ae30d2252d0fae965b3914540c9ec324922d3c04110d6d52a93</citedby><cites>FETCH-LOGICAL-c418t-337684080b7215ae30d2252d0fae965b3914540c9ec324922d3c04110d6d52a93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0305054811003157$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25609867$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Yeh, Wei-Chang</creatorcontrib><title>Simplified swarm optimization in disassembly sequencing problems with learning effects</title><title>Computers & operations research</title><description>In classical disassembly sequencing problems (DSPs), the disassembly time of each item is assumed fixed and sequence-independent. From a practical perspective, the actual processing time of a component could depend on its position in the sequence. In this paper, a novel DSP called the learning-effect DSP (LDSP) is proposed by considering the general effects of learning in DSP. A modified simplified swarm optimization (SSO) method developed by revising the most recently published variants of SSO is proposed to solve this new problem. The presented SSO scheme improves the update mechanism, which is the core of any soft computing based methods, and revises the self-adaptive parameter control procedure. The conducted computational experiment with up to 500 components reflects the effectiveness of the modified SSO method in terms of final accuracy, convergence speed, and robustness.
► We model a novel disassembly sequencing problems by considering the learning-effect DSP (LDSP). ► We modified the simplified swarm optimization (SSO) to solve LDSP. ► We improved the update mechanism and revised the self-adaptive parameter control procedure. ► The conducted computational experiment with up to 500 components reflects the effectiveness of the modified SSO method in terms of final accuracy, convergence speed, and robustness.</description><subject>Applied sciences</subject><subject>Assembly lines</subject><subject>Digital signal processing</subject><subject>Disassembly</subject><subject>Disassembly sequencing problem</subject><subject>Dismantling</subject><subject>Exact sciences and technology</subject><subject>Inventory control, production control. Distribution</subject><subject>Learning</subject><subject>Learning effects</subject><subject>Mechanical engineering. Machine design</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Robustness</subject><subject>Self-adaptive parameter control</subject><subject>Sequencing</subject><subject>Sequential scheduling</subject><subject>Simplified swarm optimization (SSO)</subject><subject>Soft computing</subject><subject>Studies</subject><subject>Update mechanism</subject><issn>0305-0548</issn><issn>1873-765X</issn><issn>0305-0548</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNp9kEtrGzEURkVJoK7bH9DdEAh0M67eMyKrYvICQxZ50J2QpTutzDwc3XGN--ujwaaLLqKN0OW7Rx-HkK-MLhhl-vtm4Ye04JSx_F5QXn0gM1ZXoqy0-nlGZlRQVVIl64_kE-KG5lNxNiMvj7HbtrGJEArcu9QVw3aMXfzrxjj0ReyLENEhQrduDwXC6w56H_tfxTYN6xY6LPZx_F204FI_jaFpwI_4mZw3rkX4crrn5Pnm-ml5V64ebu-XP1all6weSyEqXUta03UuoxwIGjhXPNDGgdFqLQyTSlJvwAsuDedBeCoZo0EHxZ0Rc_LtyM11cjUcbRfRQ9u6HoYd2uzGaC6Y5Dl68V90M-xSn9tZw5TRlTQTjx1DPg2ICRq7TbFz6ZBJE0zbjc2i7SR6GmXReefyBHboXdsklw3hv0WuNDW1nnJXxxxkIX8iJIs-ZpsQYsrObBjiO7-8Aey7klc</recordid><startdate>20120901</startdate><enddate>20120901</enddate><creator>Yeh, Wei-Chang</creator><general>Elsevier Ltd</general><general>Elsevier</general><general>Pergamon Press Inc</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20120901</creationdate><title>Simplified swarm optimization in disassembly sequencing problems with learning effects</title><author>Yeh, Wei-Chang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-337684080b7215ae30d2252d0fae965b3914540c9ec324922d3c04110d6d52a93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Applied sciences</topic><topic>Assembly lines</topic><topic>Digital signal processing</topic><topic>Disassembly</topic><topic>Disassembly sequencing problem</topic><topic>Dismantling</topic><topic>Exact sciences and technology</topic><topic>Inventory control, production control. Distribution</topic><topic>Learning</topic><topic>Learning effects</topic><topic>Mechanical engineering. Machine design</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Robustness</topic><topic>Self-adaptive parameter control</topic><topic>Sequencing</topic><topic>Sequential scheduling</topic><topic>Simplified swarm optimization (SSO)</topic><topic>Soft computing</topic><topic>Studies</topic><topic>Update mechanism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yeh, Wei-Chang</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers & operations research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yeh, Wei-Chang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Simplified swarm optimization in disassembly sequencing problems with learning effects</atitle><jtitle>Computers & operations research</jtitle><date>2012-09-01</date><risdate>2012</risdate><volume>39</volume><issue>9</issue><spage>2168</spage><epage>2177</epage><pages>2168-2177</pages><issn>0305-0548</issn><eissn>1873-765X</eissn><eissn>0305-0548</eissn><coden>CMORAP</coden><abstract>In classical disassembly sequencing problems (DSPs), the disassembly time of each item is assumed fixed and sequence-independent. From a practical perspective, the actual processing time of a component could depend on its position in the sequence. In this paper, a novel DSP called the learning-effect DSP (LDSP) is proposed by considering the general effects of learning in DSP. A modified simplified swarm optimization (SSO) method developed by revising the most recently published variants of SSO is proposed to solve this new problem. The presented SSO scheme improves the update mechanism, which is the core of any soft computing based methods, and revises the self-adaptive parameter control procedure. The conducted computational experiment with up to 500 components reflects the effectiveness of the modified SSO method in terms of final accuracy, convergence speed, and robustness.
► We model a novel disassembly sequencing problems by considering the learning-effect DSP (LDSP). ► We modified the simplified swarm optimization (SSO) to solve LDSP. ► We improved the update mechanism and revised the self-adaptive parameter control procedure. ► The conducted computational experiment with up to 500 components reflects the effectiveness of the modified SSO method in terms of final accuracy, convergence speed, and robustness.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.cor.2011.10.027</doi><tpages>10</tpages></addata></record> |
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| ispartof | Computers & operations research, 2012-09, Vol.39 (9), p.2168-2177 |
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| subjects | Applied sciences Assembly lines Digital signal processing Disassembly Disassembly sequencing problem Dismantling Exact sciences and technology Inventory control, production control. Distribution Learning Learning effects Mechanical engineering. Machine design Operational research and scientific management Operational research. Management science Optimization Optimization algorithms Robustness Self-adaptive parameter control Sequencing Sequential scheduling Simplified swarm optimization (SSO) Soft computing Studies Update mechanism |
| title | Simplified swarm optimization in disassembly sequencing problems with learning effects |
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