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 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.
neurofuzzy modeling vehicle assignments simulated annealing