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    A Penalty Function-Based Dynamic Hybrid Shop Floor Control System

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    Author
    Zhao, Xiaobing
    Issue Date
    2006
    Keywords
    hybrid shop floor control system
    penalty function
    production scheduling
    BDI agent
    error detection and recovery
    Advisor
    Son, Young-Jun
    Committee Chair
    Son, Young-Jun
    
    Metadata
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    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
    To cope with dynamics and uncertainties, a novel penalty function-based hybrid, multi-agent shop floor control system is proposed in this dissertation. The key characteristic of the proposed system is the capability of adaptively distributing decision-making power across different levels of control agents in response to different levels of disturbance. The subordinate agent executes tasks based on the schedule from the supervisory level agent in the absence of disturbance. Otherwise, it optimizes the original schedule before execution by revising it with regard to supervisory level performance (via penalty function) and disturbance. Penalty function, mathematical programming formulations, and quantitative metrics are presented to indicate the disturbance levels and levels of autonomy. These formulations are applied to diverse performance measurements such as completion time related metrics, makespan, and number of late jobs. The proposed control system is illustrated, tested with various job shop problems, and benchmarked against other shop floor control systems. In today's manufacturing system, man still plays an important role together with the control system Therefore, better coordination of humans and control systems is an inevitable topic. A novel BDI agent-based software model is proposed in this work to replace the partial decision-making function of a human. This proposed model is capable of 1) generating plans in real-time to adapt the system to a changing environment, 2) supporting not only reactive, but also proactive decision-making, 3) maintaining situational awareness in human language-like logic to facilitate real human decision-making, and 4) changing the commitment strategy adaptive to historical performance. The general purposes human operator model is then customized and integrated with an automated shop floor control system to serve as the error detection and recovery system. This model has been implemented in JACK software; however, JACK does not support real-time generation of a plan. Therefore, the planner sub-module has been developed in Java and then integrated with the JACK. To facilitate integration of an agent, real-human, and the environment, a distributed computing platform based on DOD High Level Architecture has been used. The effectiveness of the proposed model is then tested in several scenarios in a simulated automated manufacturing environment.
    Type
    text
    Electronic Dissertation
    Degree Name
    DEng
    Degree Level
    doctoral
    Degree Program
    Systems & Industrial Engineering
    Graduate College
    Degree Grantor
    University of Arizona
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