An uncertain multi-objective programming model for machine scheduling problem

This paper discusses a parallel machine scheduling problem in which the processing times of jobs and the release dates are independent uncertain variables with known uncertainty distributions. An uncertain programming model with multiple objectives is obtained, whose first objective is to minimize t...

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Vydáno v:	International journal of machine learning and cybernetics Ročník 8; číslo 5; s. 1493 - 1500
Hlavní autoři:	Ning, Yufu, Chen, Xiumei, Wang, Zhiyong, Li, Xiangying
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2017 Springer Nature B.V
Témata:	Artificial Intelligence Completion time Complex Systems Computational Intelligence Control Engineering Fuzzy sets Genetic algorithms Independent variables Mathematical programming Mechatronics Multiple objective analysis Original Article Pattern Recognition Random variables Release dates Robotics Scheduling Set theory Subject specialists Systems Biology Machine scheduling problem Uncertain variable Uncertain programming Genetic algorithm
ISSN:	1868-8071, 1868-808X
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Abstract	This paper discusses a parallel machine scheduling problem in which the processing times of jobs and the release dates are independent uncertain variables with known uncertainty distributions. An uncertain programming model with multiple objectives is obtained, whose first objective is to minimize the maximum completion time or makespan, and second objective is to minimize the maximum tardiness time. A genetic algorithm is employed to solve the proposed uncertain machine scheduling model, and its efficiency is illustrated by some numerical experiments.
AbstractList	This paper discusses a parallel machine scheduling problem in which the processing times of jobs and the release dates are independent uncertain variables with known uncertainty distributions. An uncertain programming model with multiple objectives is obtained, whose first objective is to minimize the maximum completion time or makespan, and second objective is to minimize the maximum tardiness time. A genetic algorithm is employed to solve the proposed uncertain machine scheduling model, and its efficiency is illustrated by some numerical experiments.
Author	Wang, Zhiyong Chen, Xiumei Ning, Yufu Li, Xiangying
Author_xml	– sequence: 1 givenname: Yufu surname: Ning fullname: Ning, Yufu email: yufuning@163.com organization: School of Information Engineering, Shandong Youth University of Political Science, Key Laboratory of Information Security and Intelligent Control in Universities of Shandong – sequence: 2 givenname: Xiumei surname: Chen fullname: Chen, Xiumei organization: School of Information Engineering, Shandong Youth University of Political Science, Key Laboratory of Information Security and Intelligent Control in Universities of Shandong – sequence: 3 givenname: Zhiyong surname: Wang fullname: Wang, Zhiyong organization: School of Information Engineering, Shandong Youth University of Political Science, Key Laboratory of Information Security and Intelligent Control in Universities of Shandong – sequence: 4 givenname: Xiangying surname: Li fullname: Li, Xiangying organization: School of Information Engineering, Shandong Youth University of Political Science, Key Laboratory of Information Security and Intelligent Control in Universities of Shandong
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Cites_doi	10.1007/978-3-540-89484-1 10.1186/s40467-015-0036-6 10.1287/mnsc.6.1.1 10.1016/j.cie.2014.09.029 10.1016/S0167-5060(08)70743-X 10.1287/mnsc.23.10.1144 10.1287/opre.13.3.358 10.1016/j.ins.2003.05.012 10.1016/S0167-5060(08)70356-X 10.1287/opre.31.3.559 10.1016/j.cie.2014.07.024 10.1016/0165-0114(94)90210-0 10.1287/opre.38.1.22 10.1007/s13042-014-0243-3 10.1016/j.cor.2009.04.002 10.1016/j.cie.2005.01.002 10.2307/1914185 10.1007/978-3-642-13959-8 10.1002/nav.3800010110 10.1007/s00500-014-1392-8 10.1016/j.rcim.2008.12.005 10.1108/01443579710159932 10.1007/s10845-008-0216-z 10.1016/0165-0114(79)90022-8 10.1007/978-3-662-44354-5 10.1016/j.amc.2015.09.009 10.1007/s10845-014-0982-8
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Snippet	This paper discusses a parallel machine scheduling problem in which the processing times of jobs and the release dates are independent uncertain variables with...
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SubjectTerms	Artificial Intelligence Completion time Complex Systems Computational Intelligence Control Engineering Fuzzy sets Genetic algorithms Independent variables Mathematical programming Mechatronics Multiple objective analysis Original Article Pattern Recognition Random variables Release dates Robotics Scheduling Set theory Subject specialists Systems Biology
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Title	An uncertain multi-objective programming model for machine scheduling problem
URI	https://link.springer.com/article/10.1007/s13042-016-0522-2 https://www.proquest.com/docview/2920625321
Volume	8
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