Designing an efficient method for tandem automated guided vehicle (AGV) network design problem using tabu search

A tandem AGV configuration connects all cells of a manufacturing area by means of non-overlapping, single-vehicle closed loops. Each loop has at least one additional P/D station, provided as interface between adjacent loops. This study describes the development of a tabu search algorithm for the design of tandem AGV systems. Starting from an initial partition generated by a k-means clustering method, the tabu search algorithm partitions the stations into loops by minimizing the maximum workload of the system, without allowing the paths of loops to cross each other. The new algorithm and the partitioning algorithm presented by Bozer and Srinivasan are compared on randomly generated problems. Results show that in large scale problems, the partitioning algorithm often leads to infeasible configurations with crossed loops in spite of its shorter running time. However the newly developed algorithm avoids infeasible configurations and often yields better objective function values.