DATA AND MODEL DUAL-DRIVEN APPROACH OPTIMIZING APPOINTMENT QUOTA OF EXTERNAL CONTAINER TRUCK

Cuijie Diao, Huiyun Yang, Zhihong Jin

Abstract


The truck appointment system sets quotas for each period to control the volume of external trucks arriving at the port. Optimizing appointment quotas is crucial for reducing the dwell time of external trucks and enhancing the utilization of terminal resources. Therefore, a method that combines data-driven and model-driven approaches was proposed to optimize appointment quotas by leveraging historical data. Gaussian process regression was employed to mine the correlation between the number of external trucks arriving at terminals and the truck turnaround time under different operation types in each appointment period. The objective was to minimize waiting costs for external trucks and transfer costs associated with deviations from the expected arrival periods. A non-linear mixed-integer programming model was formulated, and a genetic algorithm was designed for its solution to optimize appointment quotas under different operation types in each appointment period. The data-driven results indicate that Gaussian process regression yields a 2% lower relationship error than polynomial regression. The optimization model, which refined the operation types, reduces the total cost by 5.13% compared to traditional methods and decreases the extreme variance of appointment quotas by 64%. 

Keywords


Truck appointment; Appointment quota; Data-driven; Model-driven; Gaussian process regression.

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DOI: https://dx.doi.org/10.21622/MARLOG.2024.13.1.57

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The International Maritime Transport and Logistics Journal (MARLOG)

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