A self-organizing neural network approach for the single AGV routing problem

In this research, a special form of Automated Guided Vehicle (AGV) routing problem is investigated. The objective is to ®nd the shortest tour for a single, free-ranging AGV that has to carry out multiple pick and deliver (P&D) requests. This problem is an incidence of the asymmetric traveling salesman problem which is known to be NP-complete. An arti®cial neural network algorithm based on Kohonen's self-organizing feature maps is developed to solve the problem, and several improvements on the basic features of self-organizing maps are proposed. Performance of the algorithm is tested under various parameter settings for di€erent P&D request patterns and problem sizes, and compared with the optimal solution and the nearest neighbor rule. Promising results are obtained in terms of solution quality and computation time. Ó 2000 Elsevier Science B.V. All rights reserved.