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We study an inbound container housekeeping problem in an automated container terminal. We propose an integer programming model to characterize the problem and provide a meta-heuristic algorithm to solve it. To tackle the random factors in the future retrieval process, a simulation module is embedded in the process of evaluating the objective value. By computational experiments, we demonstrate the effectiveness and efficiency of our method. Moreover, we also conduct sensitivity analysis to examine the impacts of different algorithm parameters. [ABSTRACT FROM AUTHOR]

With the development of information technology and automation, intelligence techniques have gradually replaced manpower in container terminals. Automating container terminals can significantly improve the operation efficiency of the terminals and reduce energy consumption, time, and transportation resources. Automated guided vehicles (AGVs), used to transport containers between the seaside and the yard side, are very important for automated container terminal (ACT) performance. Meanwhile, container terminals lack systematic resilience and often operate poorly after emergencies. This study considers the problem of resilient scheduling AGVs with battery constraints. We consider the different power consumption of AGVs under loaded and empty conditions and the nonlinearity of battery charging capacity and charging time. In this model, an improved charging strategy, consisting of two thresholds for disconnecting the charging power supply, is proposed for a resilient scheduling model to minimize the total operation time to complete the transport tasks. Furthermore, a new metaheuristic algorithm that uses an adaptive large neighborhood search was proposed to solve the problem. Finally, we tested our improved charging strategy and proposed algorithm in a real case of a large Chinese ACT in the Peral River Delta. Computational results indicate that the proposed AGV charging strategy exhibits effective and efficient performance for ACTs. The sensitivity analysis also shows how the number of AGVs affects the total transport task completion time and the AVG utilization rate. [ABSTRACT FROM AUTHOR]