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

Soylu M., Ozdemirel N., Kayaligil S.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, vol.121, no.1, pp.124-137, 2000 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 121 Issue: 1
  • Publication Date: 2000
  • Doi Number: 10.1016/s0377-2217(99)00032-6
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.124-137
  • Keywords: neural networks, AGV routing, self-organizing maps, asymmetric traveling salesman problem, TRAVELING-SALESMAN PROBLEM, FEATURE MAPS, SYSTEMS
  • Middle East Technical University Affiliated: Yes


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 rested 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. (C) 2000 Elsevier Science B.V. All rights reserved.