Abstract
An artificial agent-based approach has been developed to improve the design and control of stochastic production lines. Genetic algorithms have been used as the premier agents learning mechanism. We benchmark our agent-based approach with other well-studied approaches such as infinitesimal perturbation analysis and mean-value analysis methods. The performances of our agents are comparable with other approaches; in some cases, the agent-based approach discovers even better solutions than the so-called ‘optimal’ solutions by other approaches. The paper is one of the series of our work on multi-agent intelligent enterprise modeling, and it seves as one of the most fundamental building blocks for other related works. © 2000 John Wiley & Sons, Ltd.
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