An Adaptive Dynamic Scheduling Policy for the Integrated Optimization Problem in Automated Transshipment Hubs

This study considers the multi-period dynamic integrated optimization problem in automated transshipment hubs, which includes berth allocation, quay crane assignment, and yard assignment problems. The planner needs to determine the berth and storage schedule in real-time. In the previous literature, the scholars always assume that all of the required information, especially the arrival time and operation time of vessels, is accessible prior to making any design decisions, and formulate the problem into an integrated model. However, the information of vessels is updated dynamically and changed frequently. Therefore, we consider a problem in a dynamic setting that decisions are made in each period and the scenario at each period depends on the decisions in previous periods. We formulate the problem into a multi-objective model, which aims to maximize total revenue, saved time deviation, saved transportation distance, and service quality. An efficient adaptive dynamic scheduling policy is developed, which adaptively balances the trade-offs between multiple objectives. Through numerical experiments, we demonstrate that our approach presents solutions with a delicate balance between multiple objectives and brings value to the automated container terminals compared to benchmark policies.

Automated summary

This study focuses on the multi-period dynamic integrated optimization problem in automated transshipment hubs, specifically berth allocation, quay crane assignment, and yard assignment problems. The authors propose a multi-objective model to maximize total revenue, saved time deviation, saved transportation distance, and service quality. They develop an efficient adaptive dynamic scheduling policy that balances the trade-offs between multiple objectives. Numerical experiments demonstrate the effectiveness of their approach compared to benchmark policies, offering solutions with a delicate balance between multiple objectives and bringing value to automated container terminals.