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Article: Multiobjective Modeling and Optimization for Scheduling a Stochastic Hybrid Flow Shop With Maximizing Processing Quality and Minimizing Total Tardiness

TitleMultiobjective Modeling and Optimization for Scheduling a Stochastic Hybrid Flow Shop With Maximizing Processing Quality and Minimizing Total Tardiness
Authors
KeywordsJob shop scheduling
Stochastic processes
Manufacturing systems
Uncertainty
Mathematical model
Issue Date2020
PublisherIEEE.
Citation
IEEE Systems Journal, 2020, Epub 2020-08-24, p. 1-12 How to Cite?
AbstractCurrently, manufacturing enterprises attach great importance to improving processing quality and customer satisfaction. Hybrid flow shops have widespread applications in real-world manufacturing systems such as steel production and chemical industry. In a practical production process, uncertainty commonly arises due to the difficulty of knowing exact information of facilities and jobs beforehand. In order to improve processing quality and customer satisfaction of manufacturing systems in uncertain environments, this article proposes a stochastic multiobjective hybrid flow shop scheduling problem aiming at maximizing processing quality and minimizing total tardiness, where the processing time of jobs obeys a known random distribution. To describe jobs’ processing quality mathematically, a quality-based cost function is presented, and further a chance-constrained programming approach is used to formulate this problem. Then, a multiobjective artificial bee colony algorithm incorporating a stochastic simulation approach is designed by considering its characteristics. Simulation experiments are performed on a set of instances and several state-of-the-art multiobjective optimization algorithms are chosen as peer approaches. Experiment results confirm that the proposed algorithm has an excellent performance in handling this problem.
Persistent Identifierhttp://hdl.handle.net/10722/287274
ISSN
2019 Impact Factor: 3.987
2015 SCImago Journal Rankings: 1.143

 

DC FieldValueLanguage
dc.contributor.authorFu, Y-
dc.contributor.authorWang, H-
dc.contributor.authorWang, J-
dc.contributor.authorPu, X-
dc.date.accessioned2020-09-22T02:58:30Z-
dc.date.available2020-09-22T02:58:30Z-
dc.date.issued2020-
dc.identifier.citationIEEE Systems Journal, 2020, Epub 2020-08-24, p. 1-12-
dc.identifier.issn1932-8184-
dc.identifier.urihttp://hdl.handle.net/10722/287274-
dc.description.abstractCurrently, manufacturing enterprises attach great importance to improving processing quality and customer satisfaction. Hybrid flow shops have widespread applications in real-world manufacturing systems such as steel production and chemical industry. In a practical production process, uncertainty commonly arises due to the difficulty of knowing exact information of facilities and jobs beforehand. In order to improve processing quality and customer satisfaction of manufacturing systems in uncertain environments, this article proposes a stochastic multiobjective hybrid flow shop scheduling problem aiming at maximizing processing quality and minimizing total tardiness, where the processing time of jobs obeys a known random distribution. To describe jobs’ processing quality mathematically, a quality-based cost function is presented, and further a chance-constrained programming approach is used to formulate this problem. Then, a multiobjective artificial bee colony algorithm incorporating a stochastic simulation approach is designed by considering its characteristics. Simulation experiments are performed on a set of instances and several state-of-the-art multiobjective optimization algorithms are chosen as peer approaches. Experiment results confirm that the proposed algorithm has an excellent performance in handling this problem.-
dc.languageeng-
dc.publisherIEEE.-
dc.relation.ispartofIEEE Systems Journal-
dc.rightsIEEE Systems Journal. Copyright © IEEE.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectJob shop scheduling-
dc.subjectStochastic processes-
dc.subjectManufacturing systems-
dc.subjectUncertainty-
dc.subjectMathematical model-
dc.titleMultiobjective Modeling and Optimization for Scheduling a Stochastic Hybrid Flow Shop With Maximizing Processing Quality and Minimizing Total Tardiness-
dc.typeArticle-
dc.identifier.emailWang, J: jwwang@hku.hk-
dc.identifier.authorityWang, J=rp01888-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/JSYST.2020.3014093-
dc.identifier.hkuros314564-
dc.identifier.volumeEpub 2020-08-24-
dc.identifier.spage1-
dc.identifier.epage12-
dc.publisher.placeUnited States-
dc.identifier.issnl1932-8184-

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