Cellular Versus Functional Layouts Under a Variety of Shop Operating Conditions
ABSTRACT This paper addresses the suitability of cellular manufacturing under a variety of operating conditions. Queueing theoretic and simulation models of cellular and functional layouts are developed for various shop operating environments to investigate several factors believed to influence the...
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| Veröffentlicht in: | Decision sciences 1993-05, Vol.24 (3), p.665-682 |
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| container_title | Decision sciences |
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| creator | Shafer, Scott M. Charnes, John M. |
| description | ABSTRACT
This paper addresses the suitability of cellular manufacturing under a variety of operating conditions. Queueing theoretic and simulation models of cellular and functional layouts are developed for various shop operating environments to investigate several factors believed to influence the benefits associated with a cellular manufacturing layout. The queueing models show how operations overlapping, which is more practical with a cellular layout, becomes more beneficial as the lot size increases. The simulation models are developed to study the performance of cellular and functional layouts in a wide variety of operating environments by varying the levels of four factors: (1) the degree to which natural part families occur, (2) the number of operations required to process the parts, (3) the processing times of the parts at each machine, and (4) the lot size. Two response variables are used to measure shop performance: the average time spent by a batch in the system, and the average work‐in‐process level. Statistically significant reductions in the average time in the system and average work‐in‐process measures were detected for the cellular layouts in all the operating environments studied. |
| doi_str_mv | 10.1111/j.1540-5915.1993.tb01297.x |
| format | Article |
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This paper addresses the suitability of cellular manufacturing under a variety of operating conditions. Queueing theoretic and simulation models of cellular and functional layouts are developed for various shop operating environments to investigate several factors believed to influence the benefits associated with a cellular manufacturing layout. The queueing models show how operations overlapping, which is more practical with a cellular layout, becomes more beneficial as the lot size increases. The simulation models are developed to study the performance of cellular and functional layouts in a wide variety of operating environments by varying the levels of four factors: (1) the degree to which natural part families occur, (2) the number of operations required to process the parts, (3) the processing times of the parts at each machine, and (4) the lot size. Two response variables are used to measure shop performance: the average time spent by a batch in the system, and the average work‐in‐process level. Statistically significant reductions in the average time in the system and average work‐in‐process measures were detected for the cellular layouts in all the operating environments studied.</description><identifier>ISSN: 0011-7315</identifier><identifier>EISSN: 1540-5915</identifier><identifier>DOI: 10.1111/j.1540-5915.1993.tb01297.x</identifier><identifier>CODEN: DESCDQ</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>and Simulation ; Group technology ; Layouts ; Manufacturing cells ; Plant Design ; Production planning ; Production/Operations Management ; Queueing Systems ; Statistical data ; Studies</subject><ispartof>Decision sciences, 1993-05, Vol.24 (3), p.665-682</ispartof><rights>Copyright American Institute for Decision Sciences May/Jun 1993</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3845-ef829f382bfb86a6c795b567373eab67df53a299cc2f4a6739bfb719074419373</citedby><cites>FETCH-LOGICAL-c3845-ef829f382bfb86a6c795b567373eab67df53a299cc2f4a6739bfb719074419373</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fj.1540-5915.1993.tb01297.x$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fj.1540-5915.1993.tb01297.x$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Shafer, Scott M.</creatorcontrib><creatorcontrib>Charnes, John M.</creatorcontrib><title>Cellular Versus Functional Layouts Under a Variety of Shop Operating Conditions</title><title>Decision sciences</title><description>ABSTRACT
This paper addresses the suitability of cellular manufacturing under a variety of operating conditions. Queueing theoretic and simulation models of cellular and functional layouts are developed for various shop operating environments to investigate several factors believed to influence the benefits associated with a cellular manufacturing layout. The queueing models show how operations overlapping, which is more practical with a cellular layout, becomes more beneficial as the lot size increases. The simulation models are developed to study the performance of cellular and functional layouts in a wide variety of operating environments by varying the levels of four factors: (1) the degree to which natural part families occur, (2) the number of operations required to process the parts, (3) the processing times of the parts at each machine, and (4) the lot size. Two response variables are used to measure shop performance: the average time spent by a batch in the system, and the average work‐in‐process level. Statistically significant reductions in the average time in the system and average work‐in‐process measures were detected for the cellular layouts in all the operating environments studied.</description><subject>and Simulation</subject><subject>Group technology</subject><subject>Layouts</subject><subject>Manufacturing cells</subject><subject>Plant Design</subject><subject>Production planning</subject><subject>Production/Operations Management</subject><subject>Queueing Systems</subject><subject>Statistical data</subject><subject>Studies</subject><issn>0011-7315</issn><issn>1540-5915</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1993</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqVkFFPgzAUhRujiXP6H5q9gy1tKfXFGNzmEnQPuvnYFNYqiDBbiOPfC2HZu_flJveec3LvB8AMIx_3dVv4mFHkMYGZj4UgfpMiHAjuH87A5LQ6BxOEMPY4wewSXDlXIIRCRskErGNdlm2pLNxq61oHF22VNXldqRImqqvbxsFNtdMWKrhVNtdNB2sDXz_rPVzvtVVNXn3AuK52-eBy1-DCqNLpm2Ofgs1i_hY_ecl6uYofEi8jEWWeNlEgDImC1KRRqMKMC5aykBNOtEpDvjOMqECILAsMVf1c9EKOBeKUYtGrpmA25u5t_dNq18iibm1_tZNYRBhz1NOYgrtRlNnaOauN3Nv8W9lOYiQHfrKQAyQ5QJIDP3nkJw-9-X40_-al7v7hlI_zeBWGrE_wxoTcNfpwSlD2Sw6fMvn-spTkmVAUJQtJyR9CBoaM</recordid><startdate>199305</startdate><enddate>199305</enddate><creator>Shafer, Scott M.</creator><creator>Charnes, John M.</creator><general>Blackwell Publishing Ltd</general><general>American Institute for Decision Sciences</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7TB</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AF</scope><scope>8BJ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JBE</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M2P</scope><scope>M7S</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>199305</creationdate><title>Cellular Versus Functional Layouts Under a Variety of Shop Operating Conditions</title><author>Shafer, Scott M. ; 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This paper addresses the suitability of cellular manufacturing under a variety of operating conditions. Queueing theoretic and simulation models of cellular and functional layouts are developed for various shop operating environments to investigate several factors believed to influence the benefits associated with a cellular manufacturing layout. The queueing models show how operations overlapping, which is more practical with a cellular layout, becomes more beneficial as the lot size increases. The simulation models are developed to study the performance of cellular and functional layouts in a wide variety of operating environments by varying the levels of four factors: (1) the degree to which natural part families occur, (2) the number of operations required to process the parts, (3) the processing times of the parts at each machine, and (4) the lot size. Two response variables are used to measure shop performance: the average time spent by a batch in the system, and the average work‐in‐process level. Statistically significant reductions in the average time in the system and average work‐in‐process measures were detected for the cellular layouts in all the operating environments studied.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/j.1540-5915.1993.tb01297.x</doi><tpages>18</tpages></addata></record> |
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| subjects | and Simulation Group technology Layouts Manufacturing cells Plant Design Production planning Production/Operations Management Queueing Systems Statistical data Studies |
| title | Cellular Versus Functional Layouts Under a Variety of Shop Operating Conditions |
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