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A synergistic approach to manufacturing systems control using machine learning and simulation

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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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Authors and Affiliations

  1. Krannert Graduate School of Management, Purdue University, 47907, West Lafayette, IN, USA

    Alok R. Chaturvedi

  2. School of Business Administration, University of Wisconsin-Milwaukee, 53201, Milwaukee, WI, USA

    George K. Hutchinson & Derek L. Nazareth

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  1. Alok R. Chaturvedi
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  2. George K. Hutchinson
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  3. Derek L. Nazareth
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Chaturvedi, A.R., Hutchinson, G.K. & Nazareth, D.L. A synergistic approach to manufacturing systems control using machine learning and simulation. J Intell Manuf 3, 43–57 (1992). https://doi.org/10.1007/BF01471750

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