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 find 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 artificial 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 different 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.
neural networks agv routing asymmetric traveling salesman problem self-organizing maps