An application of neurofuzzy modeling: The vehicle assignment problem

When assigning vehicles to transportation requests, dispatchers usually have built-in fuzzy rules which they use to assign a given amount of freight to be sent to a given distance in a given vehicle. Fuzzy systems equipped with learning capabilities can be trained to control complex processes like the dispatcher. They usually begin with a few very crude rules obtained from the dispatcher. Or they may work out the rules from the observed dispatcher's behavior. In this paper, a neural network is used to refine and adapt the fuzzy system to achieve better performance. As a result of the study, on a real set of numerical data, it was shown that the proposed feedforward adaptive neural networks with supervised learning capabilities can be used to tune the initial fuzzy systems.