Article ID: | iaor20013368 |
Country: | South Korea |
Volume: | 25 |
Issue: | 4 |
Start Page Number: | 97 |
End Page Number: | 109 |
Publication Date: | Dec 2000 |
Journal: | Journal of the Korean ORMS Society |
Authors: | Jang Jaejin, Rhee Jongtae |
This work proposes a neural network approach to solve vehicle routing problems which have diverse application areas such as vehicle routing and robot programming. In solving these problems, classical mathematical approaches have many difficulties. In particular, it is almost impossible to implement a real-time vehicle routing with multiple vehicles. Recently, many researchers proposed methods to overcome the limitation by adopting heuristic algorithms, genetic algorithms, neural network techniques and others. The most basic model for path planning is the Travelling Salesman Problem (TSP) for a minimum distance path. We extend this for a problem with dynamic upcoming of new positions with multiple vehicles. In this paper, we propose an algorithm based on SOM (Self-Organization Map) to obtain a sub-optimal solution for a real-time vehicle routing problem. We develop a model of a generalized multiple TSP and suggest an efficient solving procedure.