Integration of neurocomputing and system simulation for modeling continuous improvement systems in manufacturing

The use of a neural network embedded in a larger general-purpose simulation system (GPSS) simulation used to model continuous improvement systems (CIS) policies in a factory setting is described. The neural network is used to accelerate the identification of an effective CIS policy by providing a more realistic simulation framework. The interface between general simulation theory and neural network simulation is examined. Neural networks, when embedded in larger general-purpose simulations, are found to offer the potential for improving on the capabilities of those simulations, in particular manufacturing simulations for continuous improvement of production processes.

[1]  Ali A. Minai,et al.  Back-propagation heuristics: a study of the extended delta-bar-delta algorithm , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[2]  Maling Ebrahimpour,et al.  Dynamic Simulation of a Kanban Production Inventory System , 1985 .

[3]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[4]  John S. Carson,et al.  Getting started with GPSS/H , 1989 .

[5]  L. C. Rabelo,et al.  Synergy of artificial neural networks and knowledge-based expert systems for intelligent FMS scheduling , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[6]  Thomas J. Schriber,et al.  Simulation Using GPSS , 1974 .

[7]  Loren Paul Rees,et al.  A SIMULATION ANALYSIS OF THE JAPANESE JUST‐IN‐TIME TECHNIQUE (WITH KANBANS) FOR A MULTILINE, MULTISTAGE PRODUCTION SYSTEM , 1983 .

[8]  Heimo H. Adelsberger,et al.  Expert systems and simulation , 1985 .

[9]  R. Schonberger.  Japanese manufacturing techniques : nine hidden lessons simplicity , 1982 .

[10]  C. H. Dagli,et al.  Possible applications of neural networks in manufacturing , 1989, International 1989 Joint Conference on Neural Networks.

[11]  S. Jordan.  Analysis and Approximation of a JIT Production Line , 1988 .

[12]  Bernard J. Schroer,et al.  Just-In-Time (JIT), with Kanban, manufacturing system simulation on a microcomputer , 1985 .

[13]  Phillip S. Mitchell.  Japanese Manufacturing Techniques: Nine Hidden Lessons in Simplicity , 1983 .

[14]  Amrik S. Sohal,et al.  A Review of Literature Relating to JIT , 1989 .

[15]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[16]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[17]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[18]  T. Maxwell,et al.  Computational advantages of higher order neural networks , 1988, Neural Networks.

[19]  John B. Kidd,et al.  Toyota Production System , 1993 .

[20]  Bernard Widrow,et al.  Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[21]  Kazuo Machida,et al.  Fuzzy control using neural network techniques , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[22]  Gary Fellers.  Use of GPSS/PC to establish manning levels of a proposed just-in-time production facility , 1986 .

[23]  Kenji Baba,et al.  Explicit representation of knowledge acquired from plant historical data using neural network , 1990, 1990 IJCNN International Joint Conference on Neural Networks.