Prof. Dr.-Ing. Carlos Jahn
Adresse
Technische Universität Hamburg
Institut für Maritime Logistik
Am Schwarzenberg-Campus 4 (D)
21073 Hamburg
Kontaktdaten
Büro: Gebäude D Raum 5.002a
Anmeldung bei Fr. Beckmann (Raum 5.003)
Tel.: +49 40 42878 4450
Fax: +49 40 42731 4478
E-Mail: carlos.jahn(at)tuhh(dot)de
ORCiD: 0000-0002-5409-0748
Veröffentlichungen (Auszug)
2024
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2023
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2022
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2021
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2020
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2019
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2018
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2017
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2016
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2015
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2014
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach
2013
[182412] |
Title: Robust berth scheduling using machine learning for vessel arrival time prediction. |
Written by: Kolley, Lorenz and Rückert, Nicolas and Kastner, Marvin and Jahn, Carlos and Fischer, Kathrin |
in: <em>Flexible Services and Manufacturing Journal</em>. 9 (2022). |
Volume: <strong>35</strong>. Number: |
on pages: 29-69 |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/s10696-022-09462-x |
URL: https://doi.org/10.1007/s10696-022-09462-x |
ARXIVID: |
PMID: |
Note: i3lab
Abstract: In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach