[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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