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This paper proposes an improved GA (genetic algorithm)-based integrated optimization of automated container terminal scheduling. The three-stage integrated optimization model of automated container terminal scheduling is suggested, and the objective is the minimal operation time of the loading and unloading tasks at the automated container terminal. To solve the difficult combination problem, an improved GA, which is named PGA (Probability Genetic Algorithm), is developed. As traditional GA does not change the probability according to the specific iterations in the population, PGA improves the above limitation to improve population distribution and accelerate convergence. Different from published literature, the study of this paper can be presented in two aspects. One is in the modeling; it includes (i) formalizing the description of the purpose of the model and (ii) having a real-world coordination of three types of equipment that are incorporated at automated container terminals. The other is that PGA is applied to deal with the integrated scheduling whose results can be gotten with better solving speed and convergence. Numerical experiments show that the model constructed in this paper has important reference value for the optimum ratio of QCs, AGVs, and ASCs in automated container operation, which is of great significance to improve the efficiency of the automated terminal. Furthermore, compared with the results of traditional GA and PSO (particle swarm optimization), the speed and convergence of PGA have been greatly improved.

The Three-Stage Integrated Optimization of Automated Container Terminal Scheduling Based on Improved Genetic Algorithm