Simulating growth dynamics in complex adaptive supply networks

This paper discusses an extended adaptive supply network simulation model that explicitly captures growth (in terms of change in size over time, and birth and death) based on Utterback's (Utterback 1994) industrial growth model. The paper discusses the detailed behavioral modeling of the key components in the model with the help of statechart and decision tree representations. The design of a distributed, multiparadigm, agent-based simulation that addresses the issue of scalability and computational efficiency is presented. The system is targeted to run on a supercomputing grid infrastructure at Vanderbilt University. We present a method for validating this model using an experimental design that models the growth dynamics of the US automobile industry supply network over the past 80 years. The experimental work is now in progress and the results and analysis of this work are presented.

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