A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching

Xinan Chen, Ruibin Bai, Rong Qu, Haibo Dong, Jianjun Chen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

International and domestic maritime trade has been expanding dramatically in the last few decades, seaborne container transportation has become an indispensable part of maritime trade efficient and easy-to-use containers. As an important hub of container transport, container terminals use a range of metrics to measure their efficiency, among which the hourly container throughput (i.e., the number of twentyfoot equivalent unit containers, or TEUs) is the most important objective to improve. This paper proposes a genetic programming approach to build a dynamic truck dispatching system trained on real-world stochastic operations data. The experimental results demonstrated the superiority of this dynamic approach and the potential for practical applications.

Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169293
DOIs
Publication statusPublished - Jul 2020
Event2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

Name2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

Conference

Conference2020 IEEE Congress on Evolutionary Computation, CEC 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow
Period19/07/2024/07/20

Keywords

  • container terminal
  • dynamic
  • genetic programming (GP)
  • truck dispatching

ASJC Scopus subject areas

  • Control and Optimization
  • Decision Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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