INTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS)

Persistent Link:
http://hdl.handle.net/10150/195427
Title:
INTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS)
Author:
Celik, Nurcin
Issue Date:
2010
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Discrete-event simulation has become one of the most widely used analysis tools for large-scale, complex and dynamic systems such as supply chains as it can take randomness into account and address very detailed models. However, there are major challenges that are faced in simulating such systems, especially when they are used to support short-term decisions (e.g., operational decisions or maintenance and scheduling decisions considered in this research). First, a detailed simulation requires significant amounts of computation time. Second, given the enormous amount of dynamically-changing data that exists in the system, information needs to be updated wisely in the model in order to prevent unnecessary usage of computing and networking resources. Third, there is a lack of methods allowing dynamic data updates during the simulation execution. Overall, in a simulation-based planning and control framework, timely monitoring, analysis, and control is important not to disrupt a dynamically changing system. To meet this temporal requirement and address the above mentioned challenges, a Dynamic-Data-Driven Adaptive Multi-Scale Simulation (DDDAMS) paradigm is proposed to adaptively adjust the fidelity of a simulation model against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update. To the best of our knowledge, the proposed DDDAMS methodology is one of the first efforts to present a coherent integrated decision making framework for timely planning and control of distributed manufacturing enterprises.To this end, comprehensive system architecture and methodologies are first proposed, where the components include 1) real time DDDAM-Simulation, 2) grid computing modules, 3) Web Service communication server, 4) database, 5) various sensors, and 6) real system. Four algorithms are then developed and embedded into a real-time simulator for enabling its DDDAMS capabilities such as abnormality detection, fidelity selection, fidelity assignment, and prediction and task generation. As part of the developed algorithms, improvements are made to the resampling techniques for sequential Bayesian inferencing, and their performance is benchmarked in terms of their resampling qualities and computational efficiencies. Grid computing and Web Services are used for computational resources management and inter-operable communications among distributed software components, respectively. A prototype of proposed DDDAM-Simulation was successfully implemented for preventive maintenance scheduling and part routing scheduling in a semiconductor manufacturing supply chain, where the results look quite promising.
Type:
text; Electronic Dissertation
Keywords:
Adaptive simulations; Distributed simulation; Dynamic data driven simulations; Particle filtering; Resampling rules; Simulation-based control
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Systems & Industrial Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Son, Young-Jun
Committee Chair:
Son, Young-Jun

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleINTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS)en_US
dc.creatorCelik, Nurcinen_US
dc.contributor.authorCelik, Nurcinen_US
dc.date.issued2010en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractDiscrete-event simulation has become one of the most widely used analysis tools for large-scale, complex and dynamic systems such as supply chains as it can take randomness into account and address very detailed models. However, there are major challenges that are faced in simulating such systems, especially when they are used to support short-term decisions (e.g., operational decisions or maintenance and scheduling decisions considered in this research). First, a detailed simulation requires significant amounts of computation time. Second, given the enormous amount of dynamically-changing data that exists in the system, information needs to be updated wisely in the model in order to prevent unnecessary usage of computing and networking resources. Third, there is a lack of methods allowing dynamic data updates during the simulation execution. Overall, in a simulation-based planning and control framework, timely monitoring, analysis, and control is important not to disrupt a dynamically changing system. To meet this temporal requirement and address the above mentioned challenges, a Dynamic-Data-Driven Adaptive Multi-Scale Simulation (DDDAMS) paradigm is proposed to adaptively adjust the fidelity of a simulation model against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update. To the best of our knowledge, the proposed DDDAMS methodology is one of the first efforts to present a coherent integrated decision making framework for timely planning and control of distributed manufacturing enterprises.To this end, comprehensive system architecture and methodologies are first proposed, where the components include 1) real time DDDAM-Simulation, 2) grid computing modules, 3) Web Service communication server, 4) database, 5) various sensors, and 6) real system. Four algorithms are then developed and embedded into a real-time simulator for enabling its DDDAMS capabilities such as abnormality detection, fidelity selection, fidelity assignment, and prediction and task generation. As part of the developed algorithms, improvements are made to the resampling techniques for sequential Bayesian inferencing, and their performance is benchmarked in terms of their resampling qualities and computational efficiencies. Grid computing and Web Services are used for computational resources management and inter-operable communications among distributed software components, respectively. A prototype of proposed DDDAM-Simulation was successfully implemented for preventive maintenance scheduling and part routing scheduling in a semiconductor manufacturing supply chain, where the results look quite promising.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectAdaptive simulationsen_US
dc.subjectDistributed simulationen_US
dc.subjectDynamic data driven simulationsen_US
dc.subjectParticle filteringen_US
dc.subjectResampling rulesen_US
dc.subjectSimulation-based controlen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineSystems & Industrial Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSon, Young-Junen_US
dc.contributor.chairSon, Young-Junen_US
dc.contributor.committeememberSon, Young-Junen_US
dc.contributor.committeememberSzidarovszky, Ferencen_US
dc.contributor.committeememberBayraksan, Guzinen_US
dc.contributor.committeememberRam, Sudhaen_US
dc.identifier.proquest11124en_US
dc.identifier.oclc752260982en_US
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