Container Terminal Workload Modeling Using Machine Learning Techniques

Üstün Atak; Tolga Kaya; Yasin Arslanoğlu

doi:10.1007/978-3-030-51156-2_134

Container Terminal Workload Modeling Using Machine Learning Techniques

Üstün Atak^*, Tolga Kaya, Yasin Arslanoğlu

^*Corresponding author for this work

Istanbul Technical University

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

5 Citations (Scopus)

Abstract

Container terminals are complex facilities which serve to maritime transportation. Effectivity and productivity of the terminals are very crucial for international transportation. Often, terminal managers deal with logistic problems with high costs. Reducing operation cost and time are the key elements for a smooth and secure process. To minimize the port stay time of the vessels, each part of the operation should be optimized. In order to serve container vessels efficiently, the port management should analyze the cargo handling process considering the ship and container characteristics. In this scope, the purpose of this study to model quay crane handling time in traditional container terminals based on port operations data using machine learning techniques. To conduct operational efficiency analysis, we have analyzed container terminal operations using alternative regression models based on operations data of more than 400.000 handling movements in a traditional terminal in Turkey. Results suggest that the efficiency of the terminal can be increased in planned rush hours such as the periods before the mealtime and the vessels’ operation completion.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques
Subtitle of host publication	Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga
Publisher	Springer
Pages	1149-1155
Number of pages	7
ISBN (Print)	9783030511555
DOIs	https://doi.org/10.1007/978-3-030-51156-2_134
Publication status	Published - 2021
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2020 - Istanbul, Turkey Duration: 21 Jul 2020 → 23 Jul 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1197 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2020
Country/Territory	Turkey
City	Istanbul
Period	21/07/20 → 23/07/20

Bibliographical note

Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Container terminal
Machine learning
Operational efficiency analysis
Quay crane handling
Regression

UN SDGs

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

Access to Document

10.1007/978-3-030-51156-2_134

Cite this

Atak, Ü., Kaya, T., & Arslanoğlu, Y. (2021). Container Terminal Workload Modeling Using Machine Learning Techniques. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference (pp. 1149-1155). (Advances in Intelligent Systems and Computing; Vol. 1197 AISC). Springer. https://doi.org/10.1007/978-3-030-51156-2_134

Atak, Üstün ; Kaya, Tolga ; Arslanoğlu, Yasin. / Container Terminal Workload Modeling Using Machine Learning Techniques. Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A. Cagri Tolga. Springer, 2021. pp. 1149-1155 (Advances in Intelligent Systems and Computing).

@inproceedings{28158026778a4e0cb9ce802e73c59fa6,

title = "Container Terminal Workload Modeling Using Machine Learning Techniques",

abstract = "Container terminals are complex facilities which serve to maritime transportation. Effectivity and productivity of the terminals are very crucial for international transportation. Often, terminal managers deal with logistic problems with high costs. Reducing operation cost and time are the key elements for a smooth and secure process. To minimize the port stay time of the vessels, each part of the operation should be optimized. In order to serve container vessels efficiently, the port management should analyze the cargo handling process considering the ship and container characteristics. In this scope, the purpose of this study to model quay crane handling time in traditional container terminals based on port operations data using machine learning techniques. To conduct operational efficiency analysis, we have analyzed container terminal operations using alternative regression models based on operations data of more than 400.000 handling movements in a traditional terminal in Turkey. Results suggest that the efficiency of the terminal can be increased in planned rush hours such as the periods before the mealtime and the vessels{\textquoteright} operation completion.",

keywords = "Container terminal, Machine learning, Operational efficiency analysis, Quay crane handling, Regression",

author = "{\"U}st{\"u}n Atak and Tolga Kaya and Yasin Arslanoğlu",

note = "Publisher Copyright: {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2020 ; Conference date: 21-07-2020 Through 23-07-2020",

year = "2021",

doi = "10.1007/978-3-030-51156-2_134",

language = "English",

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series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "1149--1155",

editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Selcuk Cebi and Tolga, {A. Cagri}",

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Atak, Ü, Kaya, T & Arslanoğlu, Y 2021, Container Terminal Workload Modeling Using Machine Learning Techniques. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. Advances in Intelligent Systems and Computing, vol. 1197 AISC, Springer, pp. 1149-1155, International Conference on Intelligent and Fuzzy Systems, INFUS 2020, Istanbul, Turkey, 21/07/20. https://doi.org/10.1007/978-3-030-51156-2_134

Container Terminal Workload Modeling Using Machine Learning Techniques. / Atak, Üstün; Kaya, Tolga ; Arslanoğlu, Yasin.
Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A. Cagri Tolga. Springer, 2021. p. 1149-1155 (Advances in Intelligent Systems and Computing; Vol. 1197 AISC).

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

TY - GEN

T1 - Container Terminal Workload Modeling Using Machine Learning Techniques

AU - Atak, Üstün

AU - Kaya, Tolga

AU - Arslanoğlu, Yasin

PY - 2021

Y1 - 2021

N2 - Container terminals are complex facilities which serve to maritime transportation. Effectivity and productivity of the terminals are very crucial for international transportation. Often, terminal managers deal with logistic problems with high costs. Reducing operation cost and time are the key elements for a smooth and secure process. To minimize the port stay time of the vessels, each part of the operation should be optimized. In order to serve container vessels efficiently, the port management should analyze the cargo handling process considering the ship and container characteristics. In this scope, the purpose of this study to model quay crane handling time in traditional container terminals based on port operations data using machine learning techniques. To conduct operational efficiency analysis, we have analyzed container terminal operations using alternative regression models based on operations data of more than 400.000 handling movements in a traditional terminal in Turkey. Results suggest that the efficiency of the terminal can be increased in planned rush hours such as the periods before the mealtime and the vessels’ operation completion.

AB - Container terminals are complex facilities which serve to maritime transportation. Effectivity and productivity of the terminals are very crucial for international transportation. Often, terminal managers deal with logistic problems with high costs. Reducing operation cost and time are the key elements for a smooth and secure process. To minimize the port stay time of the vessels, each part of the operation should be optimized. In order to serve container vessels efficiently, the port management should analyze the cargo handling process considering the ship and container characteristics. In this scope, the purpose of this study to model quay crane handling time in traditional container terminals based on port operations data using machine learning techniques. To conduct operational efficiency analysis, we have analyzed container terminal operations using alternative regression models based on operations data of more than 400.000 handling movements in a traditional terminal in Turkey. Results suggest that the efficiency of the terminal can be increased in planned rush hours such as the periods before the mealtime and the vessels’ operation completion.

KW - Container terminal

KW - Machine learning

KW - Operational efficiency analysis

KW - Quay crane handling

KW - Regression

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U2 - 10.1007/978-3-030-51156-2_134

DO - 10.1007/978-3-030-51156-2_134

M3 - Conference contribution

AN - SCOPUS:85088744728

SN - 9783030511555

T3 - Advances in Intelligent Systems and Computing

SP - 1149

EP - 1155

BT - Intelligent and Fuzzy Techniques

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A. Cagri

PB - Springer

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2020

Y2 - 21 July 2020 through 23 July 2020

ER -

Atak Ü, Kaya T , Arslanoğlu Y. Container Terminal Workload Modeling Using Machine Learning Techniques. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Techniques: Smart and Innovative Solutions - Proceedings of the INFUS 2020 Conference. Springer. 2021. p. 1149-1155. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-51156-2_134

Container Terminal Workload Modeling Using Machine Learning Techniques

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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