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Advanced machine learning algorithms in manufacturing scheduling problems

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Title
Advanced machine learning algorithms in manufacturing scheduling problems
Name (type = personal)
NamePart (type = family)
Al Mula Abd
NamePart (type = given)
Bilal
NamePart (type = date)
1977-
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Bilal al Mula Abd
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author
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Jeong
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Myong K
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Myong K Jeong
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Advisory Committee
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chair
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Guo
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Weihong
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Weihong Guo
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Advisory Committee
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internal member
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Xi
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Zhimin
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Zhimin Xi
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Gong
NamePart (type = given)
Jie
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Jie Gong
Affiliation
Advisory Committee
Role
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outside member
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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School of Graduate Studies
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school
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Text
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theses
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2018-10
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2018
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2018
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eng
Abstract
Scheduling is a master key to succeed in the manufacturing companies in global competition. Better process scheduling leads to competitive advantage by reducing production cost and increasing productivity. Global competition has obliged the companies to expend their investments in new manufacturing systems. With the arises of these new systems, many of manufacturing problems have appeared such as scheduling problems has gained attention by researchers. Especially, it is important to develop new methodologies in order to improve manufacturing scheduling effectiveness of these new sophisticated systems. Many of researchers have used machine learning for scheduling problems because it is discovering implicit knowledge of expert schedulers that can applied for future schedules generation. In addition, machine learning is able to create flexible schedules depending on the state of the system.
In this dissertation, we present several methodologies for machine learning to scheduling in different manufacturing processes. For the scheduling problem of traditional industries, we first present a machine learning approach for dynamic scheduling of multiple machines. Existing dynamic scheduling algorithms based on classification methods that do not utilize all the available data for the better scheduling problem. A regression-based dynamic scheduling (RDS) algorithm is proposed to improve scheduling performance of classification-based dynamic scheduling. Due to the unknown relationship between predictor variables and output variables, kernel ridge regression is presented to predict the performance of the scheduling based on system status attributes. The scheduling outputs of the proposed RDS algorithm is evaluated with scheduling results of all combinations of dispatching rules from the static job shop scheduling and a classification-based dynamic scheduling.
For the scheduling problem of semiconductor manufacturing system, we present a new machine learning algorithm for complex semiconductor scheduling. An adjustable dispatching rule (ADR) that calculates weighted sum of control factors for determining which job should be processed first. Then, to find near-optimal weight values of the ADR for improving system performance, the real coded genetic algorithm (GA) with fitness approximation is proposed. For the fitness approximation, kernel ridge regression and polynomial regression are applied by using relatively small number of fitness evaluations. The performance of the proposed algorithms is evaluated by using an extensive experiment with existing dispatching rules, fixed weights, and GAs without fitness approximation.
Finally, in order to improve the scheduling performance of semiconductor wafer fabrication, we propose new variable ranking algorithms to identify the contributions of each input variable to output variable. We present a new kernelized general dominance weight (GDW) for ranking of scheduling factors in semiconductor manufacturing system. To build kernel version of GDW, the relevance vector machine regression technique is applied.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (authority = LCSH)
Topic
Machine learning
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Identifier
ETD_9230
Identifier (type = doi)
doi:10.7282/T3251NS0
PhysicalDescription
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (x, 84 pages : illustrations)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Bilal al Mula Abd
Location
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NjNbRU
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Al Mula Abd
GivenName
Bilal
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-09-24 14:08:22
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Name
Bilal Al Mula Abd
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2020-10-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 30th, 2020.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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