Two-layer Optimization Scheduling Model of Quay Cranes and Automated Guided Vehicles with Data-driven Methods at Automated Container Terminals
The quay cranes and Automated Guided Vehicles(AG Vs)in an automated container terminal are closely interconnected.This necessitates comprehensive planning for overall optimization.A two-layer optimization model is proposed to enhance coordination between quay cranes and AGV operational systems,concurrently determining the configuration and operational sequence of both quay cranes and AGVs.Within the context of AGVs'automatic path optimization mode,this study aims to manage the uncertainty in AGV travel times.Leveraging historical operational data from the terminal,a distributionally robust optimization method is employed to characterize AGV travel times.Additionally,a fuzzy chance constraint is established to mitigate the risk of quay crane waiting,resulting in the construction of a data-driven two-layer optimization model and its equivalent formulation.To solve this model,an adaptive large neighborhood search algorithm,combining simulated annealing and neighborhood search algorithms,is designed within the framework of the L-shaped algorithm.Experimental findings demonstrate that the proposed model and algorithm effectively enhance the coordination of quay cranes and AGVs,successfully addressing real-world operational scale cases,thereby improving the overall efficiency of the transportation system.
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