Container port truck dispatching optimization using Real2Sim based deep reinforcement learning

Research output: Journal PublicationArticlepeer-review

Abstract

In marine container terminals, truck dispatching optimization is often considered as the primary focus as it provides crucial synergy between the sea-side operations and yard-side activities and hence can greatly affect the terminal throughput and quay crane utilization. However, many existing studies rely on strong assumptions that often overlook the uncertainties and dynamics innate to real-life applications. In this work, we propose a dynamic truck dispatching system for container ports equipped with the latest IoT technologies. The system is comprised of Real2Sim simulation and a truck dispatch agent, trained through a spatial-attention based deep reinforcement learning module, supported by an expert network. The proposed Real2Sim framework has the ability to model the non-linear complexities and non-deterministic events while our attention-aware deep reinforcement learning module is capable of making full use of both historical and real-time port data to learn a high-quality truck dispatching policy under uncertainties. Extensive experiments show our proposed method has good generalization and achieves the state-of-the-art results on the problems derived from real-life data of a large international port.
Original languageEnglish
JournalEuropean Journal of Operational Research
Early online date28 Nov 2023
DOIs
Publication statusPublished Online - 28 Nov 2023

Keywords

  • Transportation
  • Deep reinforcement learning
  • Vehicle routing
  • Digital port
  • Uncertainties

ASJC Scopus subject areas

  • General Computer Science
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Fingerprint

Dive into the research topics of 'Container port truck dispatching optimization using Real2Sim based deep reinforcement learning'. Together they form a unique fingerprint.

Cite this