A synergistic approach to manufacturing systems control using machine learning and simulation |
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Authors: | Alok R. Chaturvedi George K. Hutchinson Derek L. Nazareth |
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Affiliation: | (1) Krannert Graduate School of Management, Purdue University, 47907 West Lafayette, IN, USA;(2) School of Business Administration, University of Wisconsin-Milwaukee, 53201 Milwaukee, WI, USA |
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Abstract: | This paper describes a synergistic approach that is applicable to a wide variety of system control problems. The approach utilizes a machine learning technique, goal-directed conceptual aggregation (GDCA), to facilitate dynamic decision-making. The application domain employed is Flexible Manufacturing System (FMS) scheduling and control. Simulation is used for the dual purpose of providing a realistic depiction of FMSs, and serves as an engine for demonstrating the viability of a synergistic system involving incremental learning. The paper briefly describes prior approaches to FMS scheduling and control, and machine learning. It outlines the GDCA approach, provides a generalized architecture for dynamic control problems, and describes the implementation of the system as applied to FMS scheduling and control. The paper concludes with a discussion of the general applicability of this approach. |
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Keywords: | Machine learning simulation flexible manufacturing systems |
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