| dc.identifier.uri | http://hdl.handle.net/11401/76443 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | Although the widely application of advanced IT technology has enabled production information to become increasingly transparent, detailed and real-time, the utilization of the information for system modeling and control remains largely unexplored. In fact, instantaneously transforming information gathered from a vast array of sources into useful knowledge for effective decision making has been identified as one of the six grand challenges in the vision for manufacturing 2020 and beyond. It is necessary to have a real-time integrated system modeling and control method, which utilizes the production information to quickly respond to unpredictable disturbance to manufacturing system and ensure smooth production and high productivity. This dissertation is devoted to this end. In this dissertation, the impact of disruption events on system productivity in both serial and parallel production lines is quantitatively evaluated. Built on the analysis, an event-based modeling (EBM) approach is developed to estimate the systematic impact of individual machine and supporting activity (e.g. material handling, quality inspection and maintenance). EBM instantaneously captures the system dynamics using distributed sensor information. To evaluate the performance of a production line segment, standalone throughput (SAT) definition and an event-based estimation method are developed. A market demand driven system model is established to unify the analysis of system productivity and market demand satisfaction. A supervisory control algorithm is developed to continuously improve system productivity and market demand satisfaction (MDS). A Markov decision process (MDP) model is built to assist the control decision making in the supervisory control algorithm. | |
| dcterms.available | 2017-09-20T16:50:17Z | |
| dcterms.contributor | Chen, Shikui | en_US |
| dcterms.contributor | Chang, Qing | en_US |
| dcterms.contributor | Ge, Qiaode | en_US |
| dcterms.contributor | Arinez, Jorge. | en_US |
| dcterms.creator | Li, Yang | |
| dcterms.dateAccepted | 2017-09-20T16:50:17Z | |
| dcterms.dateSubmitted | 2017-09-20T16:50:17Z | |
| dcterms.description | Department of Mechanical Engineering. | en_US |
| dcterms.extent | 133 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/76443 | |
| dcterms.issued | 2014-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:50:17Z (GMT). No. of bitstreams: 1
Li_grad.sunysb_0771E_11948.pdf: 2470395 bytes, checksum: 28406777cb821f0acaeb8ca5c941d78a (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Advanced Manufacturing System, Event-based modeling, Real-time, Supervisory control | |
| dcterms.subject | Mechanical engineering | |
| dcterms.title | Event-based Modeling and Control of An Advanced Manufacturing System | |
| dcterms.type | Dissertation | |
