Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times
This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration o...
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| Veröffentlicht in: | PloS one 2016-12, Vol.11 (12), p.e0167427-e0167427 |
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| creator | Yang, Xin Zeng, Zhenxiang Wang, Ruidong Sun, Xueshan |
| description | This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems. |
| doi_str_mv | 10.1371/journal.pone.0167427 |
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The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0167427</identifier><identifier>PMID: 27907163</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Biology and Life Sciences ; Classification ; Completion time ; Energy consumption ; Energy Metabolism ; Engineering ; Engineering and Technology ; Genetic algorithms ; Job shops ; Linear programming ; Manufacturing ; Manufacturing industry ; Mathematical models ; Methods ; Models, Theoretical ; Optimization ; Physical Sciences ; Problems ; Production scheduling ; Research and Analysis Methods ; Researchers ; Scheduling ; Small & medium sized enterprises-SME ; Sorting algorithms ; Stochastic Processes ; Stochasticity ; Studies</subject><ispartof>PloS one, 2016-12, Vol.11 (12), p.e0167427-e0167427</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Yang et al 2016 Yang et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c725t-1f83c6cbb41dce03290221cbde2d85015a4d524e8b9298acf45ef2e44bdb85c3</citedby><cites>FETCH-LOGICAL-c725t-1f83c6cbb41dce03290221cbde2d85015a4d524e8b9298acf45ef2e44bdb85c3</cites><orcidid>0000-0001-6636-9853</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5131930/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5131930/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,862,883,2098,2917,23849,27907,27908,53774,53776,79351,79352</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27907163$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Song, Houbing</contributor><creatorcontrib>Yang, Xin</creatorcontrib><creatorcontrib>Zeng, Zhenxiang</creatorcontrib><creatorcontrib>Wang, Ruidong</creatorcontrib><creatorcontrib>Sun, Xueshan</creatorcontrib><title>Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Classification</subject><subject>Completion time</subject><subject>Energy consumption</subject><subject>Energy Metabolism</subject><subject>Engineering</subject><subject>Engineering and Technology</subject><subject>Genetic algorithms</subject><subject>Job shops</subject><subject>Linear programming</subject><subject>Manufacturing</subject><subject>Manufacturing industry</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Models, Theoretical</subject><subject>Optimization</subject><subject>Physical Sciences</subject><subject>Problems</subject><subject>Production scheduling</subject><subject>Research and Analysis Methods</subject><subject>Researchers</subject><subject>Scheduling</subject><subject>Small & medium sized enterprises-SME</subject><subject>Sorting algorithms</subject><subject>Stochastic 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One</addtitle><date>2016-12-01</date><risdate>2016</risdate><volume>11</volume><issue>12</issue><spage>e0167427</spage><epage>e0167427</epage><pages>e0167427-e0167427</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27907163</pmid><doi>10.1371/journal.pone.0167427</doi><tpages>e0167427</tpages><orcidid>https://orcid.org/0000-0001-6636-9853</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analysis Biology and Life Sciences Classification Completion time Energy consumption Energy Metabolism Engineering Engineering and Technology Genetic algorithms Job shops Linear programming Manufacturing Manufacturing industry Mathematical models Methods Models, Theoretical Optimization Physical Sciences Problems Production scheduling Research and Analysis Methods Researchers Scheduling Small & medium sized enterprises-SME Sorting algorithms Stochastic Processes Stochasticity Studies |
| title | Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times |
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