An analytical approach for using simulation in real-time decision making in FMSs

Abstract This paper presents an analytical approach to determine the suitability of and to adapt discrete-event simulation for real-time decision making in flexible manufacturing systems (FMSs). First, a formula is developed to predict the simulation CPU run time for a given manufacturing system, a planning horizon, and a computer system. It is shown that there are only three main factors that affect simulation run time: the planning horizon, the overall system average interarrival time, and the average number of workstations per part routing. In light of this, an approach to reduce simulation run time is presented. It is based on aggregating workstations to reduce the average number of workstations per part routing. The validity of the approach is justified by comparing the performance measures of example systems with and without aggregation. A theoretical approximation of the error incurred as a result of aggregations is derived. The results show that time savings of up to 400% can be achieved at the expense of only a few percentage points loss in accuracy of the average flowtime performance measure. The developed concepts have been integrated in an algorithm to serve as a front end for discrete-event simulation to adapt simulation models to real-time decision-making requirements. The application of this algorithm is illustrated with an example.

[1]  Catherine M. Harmonosky,et al.  Investigating the application potential of simulation to real-time control decisions , 1995 .

[2]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[3]  C. M. Harmonosky,et al.  Real-time scheduling in computer integrated manufacturing: a review of recent research , 1991 .

[4]  Robert M. O'Keefe,et al.  Using artificial intelligence to facilitate manufacturing systems simulation , 1990 .

[5]  Richard A. Wysk,et al.  An inference structure for the control and scheduling of manufacturing systems , 1990 .

[6]  Hoda A. ElMaraghy.  Automated tool management in flexible manufacturing , 1985 .

[7]  Hoda A. ElMaraghy.  Simulation and graphical animation of advanced manufacturing systems , 1982 .

[8]  R. Suri.  Rapid modeling: how it assists manufacturing competitiveness , 1989, WSC '89.

[9]  Paul Rogers,et al.  Simulation for real-time decision making in manufacturing systems , 1993, WSC '93.

[10]  Rajan Suri,et al.  Rapid modeling tools for manufacturing simulation and analysis , 1988, WSC '88.

[11]  Kurt Maly,et al.  An efficient data structure for the simulation event set , 1977, CACM.

[12]  Richard A. Wysk,et al.  An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing , 1989 .

[13]  Liang Chen,et al.  A fast simulation approach for tandem queueing systems , 1990, 1990 Winter Simulation Conference Proceedings.

[14]  Catherine M. Harmonosky,et al.  The system generator concept for FMS research and analysis , 1990 .

[15]  Albert Jones,et al.  A real-time production scheduler for a stochastic manufacturing environment , 1988 .

[16]  Catherine M. Harmonosky,et al.  Implementation issues using simulation for real-time scheduling, control, and monitoring , 1990, 1990 Winter Simulation Conference Proceedings.

[17]  Murali Krishnamurthi,et al.  Domain-based on-line simulation for real-time decision support , 1993, WSC '93.