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Abstract
This paper presents an overview of simulation-based shop floor control. Much of the work described is based on research conducted in the Computer Integrated Manufacturing (CIM) Lab at The Pennsylvania State University, the Texas A&M Computer Aided Manufacturing Lab (TAMCAM), Technion in Israel, and the University of Arizona CIM lab over the past decade. In this approach, a discrete event simulation is used not only as a traditional analysis and evaluation tool but also as a task generator that drives shop floor operations in real time. To enable this, a special feature of the ArenaTM simulation language was used whereby the simulation model interacts directly with a shop floor execution system by sending and receiving messages. This control simulation reads process plans and master production orders from external databases that are updated by a process planning system and coordinated via an external business system. The control simulation also interacts with other external programs such as a planner, a scheduler, and an error detection and recovery function. In this paper, the architecture, implementation, and the integration of all the components of the proposed simulationbased control system are described in detail. Finally, extensions to this approach, including automatic model generation, are described.
Keywords: Shop Floor Control, CIM, Real-Time Scheduling, Simulation
1. Introduction
Simulation is a commonly used tool to gain insight into the operational behavior of manufacturing systems. The traditional use of simulation has been limited to planning and design activities, and several commercial simulation languages have been developed specifically for this purpose: for example, ArenaTM, AutoModTM, ProModelTM, and so on. Simulation models developed for planning and design are often aggregate models using statistical distributions to model the stochastic nature of the environment. These models are used to perform what-if analysis to determine values of design variables, control strategies, and develop estimates of system performance. These models are usually discarded after the initial plans are finalized.
Several authors have reported the expanded role of simulation to include "real-time" scheduling as part of intelligent simulation to dynamically select scheduling policies based on real-time shop floor status (Wu and Wysk 1989; Harmonosky and Robohn 1991; Rogers and Flanagan 1991; Smith et al. 1994; Drake and Smith 1996; Jones, Rabelo, Yuchwera 1995; Tunali 1995). These authors...