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Award Abstract #1543872

EAGER: Cybermanufacturing: Design of an Agile and Smart Manufacturing Exchange: Enabling Small Businesses through Standardized Protocols and Distributed Optimization

NSF Org: ECCS
Div Of Electrical, Commun & Cyber Sys
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Initial Amendment Date: September 4, 2015
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Latest Amendment Date: September 4, 2015
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Award Number: 1543872
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Award Instrument: Standard Grant
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Program Manager: Radhakisan Baheti
ECCS Div Of Electrical, Commun & Cyber Sys
ENG Directorate For Engineering
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Start Date: September 1, 2015
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End Date: August 31, 2018 (Estimated)
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Awarded Amount to Date: $300,000.00
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Investigator(s): Krishnendu Chakrabarty krish@ee.duke.edu (Principal Investigator)
Bruce Maggs (Co-Principal Investigator)
Michael Zavlanos (Co-Principal Investigator)
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Sponsor: Duke University
2200 W. Main St, Suite 710
Durham, NC 27705-4010 (919)684-3030
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NSF Program(s): NANOSCALE: INTRDISCPL RESRCH T,
ENG INTERDISC RES (IDR)
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Program Reference Code(s): 1674, 7915, 7916
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Program Element Code(s): 1674, 7951

ABSTRACT

This EArly-concept Grant for Exploratory Research (EAGER) award supports fundamental research on the design of an agile manufacturing exchange system (MES) in which suppliers of raw materials, assemblers, transportation companies, etc., will participate through standardized protocols to fulfill complex manufacturing orders. This design will provide the foundation for a smart software mediation layer (i.e., a "broker") that will enable a MES to be self-learning and adaptive to dynamic/diverse service requests and resource availability, as well as support a large network of service providers and users within a complex information ecosystem. The economic competitiveness of the U.S. dependss on new and innovative methods for intelligent mass customization systems for the manufacturing sector, which will enable the processing of small-sized and diverse orders that demand almost instant fulfillment. The MES will enable this transformation by supporting on-demand integration of resources, graceful recovery from failures, and dynamic adaptation without any disruption in operations.



In order to meet these goals, research will be focused on adaptation to emerging system behaviors by dynamically evolving optimization strategies in real-time. Users and providers will be connected in a dynamic manufacturing network that will accommodate multiple product flows, uncertainty in links between providers and themselves, and fault tolerance to provide service despite failed network components. This level of adaptation, seamless efficiency, and uninterrupted service will constitute a significant step forward towards a smart MES. The research goals will be accomplished through the design of a distributed real-time optimization and knowledge discovery framework that will address workflow optimization, resource allocation, and data-driven performance prediction in a dynamic manufacturing network of users, brokers, and providers. The specific research tasks include online admission control policies, dynamic production planning, analysis and prediction of service-level performance for forecasting, distributed methods for dynamic resource allocation under uncertainty, and visual analytics techniques to support human decision makers and situational awareness.


PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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S. Lee and M. M. Zavlanos. "Distributed Primal-Dual Methods for Online Constrained Optimization.," Proc. 2016 American Control Conference., 2016.

S. Lee, A. Ribeiro, and M. M. Zavlanos. "Distributed Continuous-time Online Optimization using Saddle-Point Methods," Proc. 55th IEEE Conference on Decision and Control, 2016.

S. Lee and M. M. Zavlanos.. "Distributed Primal-Dual Methods for Online Constrained Optimization," Proc. 2016 American Control Conference, 2016, p. 7171.

S. Lee and M. M. Zavlanos. "Distributed Primal-Dual Methods for Online Constrained Optimization," 2016 American Control Conference, Boston, MA., 2016.

S. Lee, A. Ribeiro, and M. M. Zavlanos. "Distributed Continuous-time Online Optimization using Saddle-Point Methods," Proc. 55th IEEE Conference on Decision and Control, 2016, p. 4314.

 

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