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

Date

2000-02-15

Author

Soylu, M
Özdemirel, Nur Evin
Kayaligil, S

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Neural networks, Asymmetric traveling salesman problem, Self-organizing maps, AGV routing

URI

https://hdl.handle.net/11511/37761

Journal

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

DOI

https://doi.org/10.1016/s0377-2217(99)00032-6

Collections

Department of Industrial Engineering, Article

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Citation Formats

M. Soylu, N. E. Özdemirel, and S. Kayaligil, “A self-organizing neural network approach for the single AGV routing problem,” EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, pp. 124–137, 2000, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37761.