Abstract
We model choice of dispatching rules in real time (system state dependent) as a pattern recognition problem, using a modified version of Data Envelopment Analysis. A data base of system state and performance values is created from extensive simulation, and this data base is used to train the pattern‐recognition model. Our results show that the model is very effective in choosing a mix of dispatching rules over a period of time, varying the mix with system objectives, and performing better than the strategy of using fixed rules. We show how “If‐Then” decision rules can be created from the model and portrayed in a decision‐tree‐like diagram. Since such decision rules are based on rigorous mathematical foundations, optimization will be ensured in our approach.
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