Multi-objective genetic algorithms for scheduling mateiral handling equipment at automated air cargo terminals

In order to improve the productivities of a typical cargo handling system, it is important to reduce the waiting time of stacker cranes (SCs) and the total traveling time of automated guided vehicles (AGVs) through efficient scheduling of SCs and AGVs, which are cooperating tightly to perform cargo handling operations in an optimal way. In this paper, we develop and investigate the application of the multi-objective genetic algorithm (MOGA) to solve such scheduling problem with the objectives of minimizing the AGV total traveling time and the total delay time of the SC. The results of the experiments demonstrated that MOGA produces better solutions than the single objective genetic algorithms.