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
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2023
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2022
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2021
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2020
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2019
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2018
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2017
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2016
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2015
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2014
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software
2013
[182402] |
Title: Container Flow Generation for Maritime Container Terminals. <em>Dynamics in Logistics. Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany</em> |
Written by: Kastner, Marvin and Grasse, Ole and Jahn, Carlos |
in: 2 (2022). |
Volume: Number: |
on pages: 133-143 |
Chapter: |
Editor: In Freitag, Michael and Kinra, Aseem, and Kotzab, Herbert, and Megow, Nicole (Eds.) |
Publisher: Springer: |
Series: LDIC 2022 |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.1007/978-3-031-05359-7_11 |
URL: https://link.springer.com/chapter/10.1007/978-3-031-05359-7_11 |
ARXIVID: |
PMID: |
Note: conflowgen
Abstract: In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software