Publications (Excerpt)

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:

[www]

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:

[www]

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:

[www]

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:

[www]

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:

[www]

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:

[www]

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:

[www]

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:

[www]

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:

[www]

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:

[www]

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:

[www]

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:

[www]

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