Vehicle routing based on self-organization with and without fuzzy inference

This paper deals with a fuzzy-based system to solve the capacitated vehicle routing problem. The proposed method makes use of a neural network employing unsupervised learning guided by a fuzzy rule base. The algorithm is based on a policy of penalties and rewards, on a strategy of neuron inhibition, insertion and pruning, and on certain statistical characteristics of the input space. We make use of fuzzy theory aiming at minimizing drawbacks related to uncertainty and availability of partial information, and at synthesizing an adaptive process of constraint relaxation. The effectiveness of the proposed method is attested by means of a series of computational simulations comparing crisp and fuzzy approaches