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

doi:10.1109/CEC48606.2020.9185659

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 proceeding › Conference contribution › peer-review

20 Citations (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 language	English
Title of host publication	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728169293
DOIs	https://doi.org/10.1109/CEC48606.2020.9185659
Publication status	Published - Jul 2020
Event	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Part of 2020 IEEE World Congress on Computational Intelligence, WCCI 2020 - Virtual, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

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

Conference

Conference	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Part of 2020 IEEE World Congress on Computational Intelligence, WCCI 2020
Country/Territory	United Kingdom
City	Virtual, Glasgow
Period	19/07/20 → 24/07/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Free 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

Access to Document

10.1109/CEC48606.2020.9185659

Cite this

Chen, X., Bai, R., Qu, R., Dong, H., & Chen, J. (2020). A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings Article 9185659 (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185659

Chen, Xinan ; Bai, Ruibin ; Qu, Rong et al. / A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching. 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings).

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

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.",

keywords = "container terminal, dynamic, genetic programming (GP), truck dispatching",

author = "Xinan Chen and Ruibin Bai and Rong Qu and Haibo Dong and Jianjun Chen",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Part of 2020 IEEE World Congress on Computational Intelligence, WCCI 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

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doi = "10.1109/CEC48606.2020.9185659",

language = "English",

series = "2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings",

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}

Chen, X, Bai, R, Qu, R, Dong, H & Chen, J 2020, A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching. in 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings., 9185659, 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Part of 2020 IEEE World Congress on Computational Intelligence, WCCI 2020, Virtual, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/CEC48606.2020.9185659

A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching. / Chen, Xinan; Bai, Ruibin; Qu, Rong et al.
2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9185659 (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AU - Chen, Jianjun

PY - 2020/7

Y1 - 2020/7

N2 - 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.

AB - 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.

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KW - dynamic

KW - genetic programming (GP)

KW - truck dispatching

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DO - 10.1109/CEC48606.2020.9185659

M3 - Conference contribution

AN - SCOPUS:85092023362

T3 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Part of 2020 IEEE World Congress on Computational Intelligence, WCCI 2020

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ER -

Chen X, Bai R, Qu R, Dong H, Chen J. A Data-Driven Genetic Programming Heuristic for Real-World Dynamic Seaport Container Terminal Truck Dispatching. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9185659. (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). doi: 10.1109/CEC48606.2020.9185659

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

Abstract

Publication series

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UN SDGs

Free Keywords

ASJC Scopus subject areas

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