R. Choe, H. Kim, T. Park, and K.R. Ryu (Korea)
Container terminal, AGV, ALV, traffic flow control, genetic algorithm
Automated guided vehicles (AGV) systems are used for the horizontal movement of containers in an automated container terminal. To achieve high productivity, it is critical to optimize the traffic flow of AGVs to minimize traffic congestion. AGVs are more vulnerable to traffic congestion in a container terminal environment than in a manufacturing system environment. AGV systems are traditionally used in a container terminal environment because a large number of AGVs can simultaneously travel in a limited area. In addition, since the operational conditions of AGVs change dynamically, the traffic flow of AGVs has to be continuously adjusted to keep up with these changes. In this paper, a method that dynamically adapts the traffic flow of AGVs to changing operational conditions is presented. The method uses a genetic algorithm to optimize the traffic flow against the changing operational condition. Four heuristic evaluation criteria are devised to reduce the computation cost. Exploiting the dynamic nature of the problem via an approach that reuses the results of the previous search is used to speed up the convergence of the genetic algorithm. The results of simulation experiments show the efficiency of the proposed method.
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