Marvin Kastner, M.Sc.
Adresse
Technische Universität Hamburg
Institut für Maritime Logistik
Am Schwarzenberg-Campus 4 (D)
21073 Hamburg
Kontaktdaten & Profile
Büro: Gebäude D Raum 5.007
Tel.: +49 40 42878 4793
E-Mail: marvin.kastner(at)tuhh(dot)de
ORCiD: 0000-0001-8289-2943
LinkedIn: https://www.linkedin.com/in/marvin-kastner/
ResearchGate: https://www.researchgate.net/profile/Marvin-Kastner
Google scholar: https://scholar.google.de/citations?user=lAR-oVAAAAAJ&hl=de&oi=ao
Scopus: https://www.scopus.com/authid/detail.uri?authorId=57221938031
Forschungsschwerpunkte
- simulationsgestütztes Planen von Container-Terminals
- Optimierung der Ablaufplanung im Yard von Container-Terminals
- technologiegestützte Verbesserung der maritimen Sicherheit
- Maschinelles Lernen in der maritimen Logistik
- Optimierung multivariater Black-box Funktionen
Vorträge und Workshops (Auszug)
- 25.01.2023 ein Vortrag auf dem 7. Suderburger Logistik-Forum: "KI-unterstützte Planung von Güterumschlaganlagen am Beispiel von Containerterminals"
- 15.09.2022 ein Vortrag bei den MLE-Days 2022: "Synthetische Daten für das Reinforcement-Learning bei Container-Terminal-Steuerungen"
- 28.06.2022 ein Workshop an der Graduiertenakademie der TUHH: "Einführung in Jupyter Notebooks" [mehr]
- 02.07.2021 ein Workshop bei den MLE-Days 2021: "Methoden des Maschinellen Lernens in der Maritimen Logistik" [zip]
- 16.03.2021 ein Workshop an der Graduiertenakademie der TUHH: "Einführung in Jupyter Notebooks" [mehr]
- 30.11.2020 im Rahmen der Vortragsreihe "Train Your Engineering Network" der MLE-Initiative: "How to Talk About Machine Learning with Jupyter Notebooks" [mehr]
- 22.11.2019 auf der DISRUPT NOW! AI for Hamburg: "Künstliche Intelligenz in der maritimen Wirtschaft" [mehr]
- 29.10.2019 im Rahmen der forschungsbörse: "Maritime Logistik - Ein Rundumschlag" [mehr]
- 23.10.2019 bei der Open Access Week 2019 an der TUHH: "Datenanalyse - Offener Workshop: Daten auswerten und visualisieren mit Jupyter Notebooks" [mehr] [git]
- 16.11.2018 beim GI DevCamp Hamburg: "Mobility Research and GDPR"
- 27.09.2018 beim SGKV AK zum Thema Lkw-Ankünfte: "Prognoseverfahren und neuronale Netze – Was ist möglich?"
Veröffentlichungen (Auszug)
2024
| [182407] |
| Title: Model-based Optimisation with Tree-structured Parzen Estimation for Discrete Event Simulation at Container Terminals: Modellbasierte Optimierung mit baumstrukturierter Kerndichteschätzung für ereignisdiskrete Simulation auf Container-Terminals. <em>Simulation in Produktion und Logistik</em> |
| Written by: Kastner, Marvin and Nellen, Nicole and Jahn, Carlos |
| in: <em>ASIM 2019</em>. (2019). |
| Volume: Number: |
| on pages: 489-498 |
| Chapter: |
| Editor: In Putz, Matthias and Schlegel, Andreas (Eds.) |
| Publisher: Wissenschaftliche Scripten: |
| Series: |
| Address: Auerbach /Vogtl. |
| Edition: 1 |
| ISBN: 9783957351135 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: http://www.asim-fachtagung-spl.de/asim2019/papers/47_Proof_164.pdf |
| ARXIVID: |
| PMID: |
Note:
Abstract: Traditionally discrete event simulation serves as a tool for estimating the impact of managerial decisions at container terminals. For complex situations, simulation-based optimisation can be employed to lower the amount of executed simulation experiments and therefore reduce the time to obtain results. The search is guided by the already run experiments. This paper examines the applicability of the Tree-structured Parzen Estimator for optimizing simulation models of container terminals. The reasoning is based both on prior literature in the field of simulation and machine learning, as well as a numerical study. Within the experiments, the performance of the Tree-structured Parzen Estimator is compared with Simulated Annealing and Random Search. The Tree-structured Parzen Estimator displays a more explorative and robust search behaviour than Simulated Annealing
2023
| [182407] |
| Title: Model-based Optimisation with Tree-structured Parzen Estimation for Discrete Event Simulation at Container Terminals: Modellbasierte Optimierung mit baumstrukturierter Kerndichteschätzung für ereignisdiskrete Simulation auf Container-Terminals. <em>Simulation in Produktion und Logistik</em> |
| Written by: Kastner, Marvin and Nellen, Nicole and Jahn, Carlos |
| in: <em>ASIM 2019</em>. (2019). |
| Volume: Number: |
| on pages: 489-498 |
| Chapter: |
| Editor: In Putz, Matthias and Schlegel, Andreas (Eds.) |
| Publisher: Wissenschaftliche Scripten: |
| Series: |
| Address: Auerbach /Vogtl. |
| Edition: 1 |
| ISBN: 9783957351135 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: http://www.asim-fachtagung-spl.de/asim2019/papers/47_Proof_164.pdf |
| ARXIVID: |
| PMID: |
Note:
Abstract: Traditionally discrete event simulation serves as a tool for estimating the impact of managerial decisions at container terminals. For complex situations, simulation-based optimisation can be employed to lower the amount of executed simulation experiments and therefore reduce the time to obtain results. The search is guided by the already run experiments. This paper examines the applicability of the Tree-structured Parzen Estimator for optimizing simulation models of container terminals. The reasoning is based both on prior literature in the field of simulation and machine learning, as well as a numerical study. Within the experiments, the performance of the Tree-structured Parzen Estimator is compared with Simulated Annealing and Random Search. The Tree-structured Parzen Estimator displays a more explorative and robust search behaviour than Simulated Annealing
2022
| [182407] |
| Title: Model-based Optimisation with Tree-structured Parzen Estimation for Discrete Event Simulation at Container Terminals: Modellbasierte Optimierung mit baumstrukturierter Kerndichteschätzung für ereignisdiskrete Simulation auf Container-Terminals. <em>Simulation in Produktion und Logistik</em> |
| Written by: Kastner, Marvin and Nellen, Nicole and Jahn, Carlos |
| in: <em>ASIM 2019</em>. (2019). |
| Volume: Number: |
| on pages: 489-498 |
| Chapter: |
| Editor: In Putz, Matthias and Schlegel, Andreas (Eds.) |
| Publisher: Wissenschaftliche Scripten: |
| Series: |
| Address: Auerbach /Vogtl. |
| Edition: 1 |
| ISBN: 9783957351135 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: http://www.asim-fachtagung-spl.de/asim2019/papers/47_Proof_164.pdf |
| ARXIVID: |
| PMID: |
Note:
Abstract: Traditionally discrete event simulation serves as a tool for estimating the impact of managerial decisions at container terminals. For complex situations, simulation-based optimisation can be employed to lower the amount of executed simulation experiments and therefore reduce the time to obtain results. The search is guided by the already run experiments. This paper examines the applicability of the Tree-structured Parzen Estimator for optimizing simulation models of container terminals. The reasoning is based both on prior literature in the field of simulation and machine learning, as well as a numerical study. Within the experiments, the performance of the Tree-structured Parzen Estimator is compared with Simulated Annealing and Random Search. The Tree-structured Parzen Estimator displays a more explorative and robust search behaviour than Simulated Annealing
2021
| [182407] |
| Title: Model-based Optimisation with Tree-structured Parzen Estimation for Discrete Event Simulation at Container Terminals: Modellbasierte Optimierung mit baumstrukturierter Kerndichteschätzung für ereignisdiskrete Simulation auf Container-Terminals. <em>Simulation in Produktion und Logistik</em> |
| Written by: Kastner, Marvin and Nellen, Nicole and Jahn, Carlos |
| in: <em>ASIM 2019</em>. (2019). |
| Volume: Number: |
| on pages: 489-498 |
| Chapter: |
| Editor: In Putz, Matthias and Schlegel, Andreas (Eds.) |
| Publisher: Wissenschaftliche Scripten: |
| Series: |
| Address: Auerbach /Vogtl. |
| Edition: 1 |
| ISBN: 9783957351135 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: http://www.asim-fachtagung-spl.de/asim2019/papers/47_Proof_164.pdf |
| ARXIVID: |
| PMID: |
Note:
Abstract: Traditionally discrete event simulation serves as a tool for estimating the impact of managerial decisions at container terminals. For complex situations, simulation-based optimisation can be employed to lower the amount of executed simulation experiments and therefore reduce the time to obtain results. The search is guided by the already run experiments. This paper examines the applicability of the Tree-structured Parzen Estimator for optimizing simulation models of container terminals. The reasoning is based both on prior literature in the field of simulation and machine learning, as well as a numerical study. Within the experiments, the performance of the Tree-structured Parzen Estimator is compared with Simulated Annealing and Random Search. The Tree-structured Parzen Estimator displays a more explorative and robust search behaviour than Simulated Annealing
2020
| [182407] |
| Title: Model-based Optimisation with Tree-structured Parzen Estimation for Discrete Event Simulation at Container Terminals: Modellbasierte Optimierung mit baumstrukturierter Kerndichteschätzung für ereignisdiskrete Simulation auf Container-Terminals. <em>Simulation in Produktion und Logistik</em> |
| Written by: Kastner, Marvin and Nellen, Nicole and Jahn, Carlos |
| in: <em>ASIM 2019</em>. (2019). |
| Volume: Number: |
| on pages: 489-498 |
| Chapter: |
| Editor: In Putz, Matthias and Schlegel, Andreas (Eds.) |
| Publisher: Wissenschaftliche Scripten: |
| Series: |
| Address: Auerbach /Vogtl. |
| Edition: 1 |
| ISBN: 9783957351135 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: http://www.asim-fachtagung-spl.de/asim2019/papers/47_Proof_164.pdf |
| ARXIVID: |
| PMID: |
Note:
Abstract: Traditionally discrete event simulation serves as a tool for estimating the impact of managerial decisions at container terminals. For complex situations, simulation-based optimisation can be employed to lower the amount of executed simulation experiments and therefore reduce the time to obtain results. The search is guided by the already run experiments. This paper examines the applicability of the Tree-structured Parzen Estimator for optimizing simulation models of container terminals. The reasoning is based both on prior literature in the field of simulation and machine learning, as well as a numerical study. Within the experiments, the performance of the Tree-structured Parzen Estimator is compared with Simulated Annealing and Random Search. The Tree-structured Parzen Estimator displays a more explorative and robust search behaviour than Simulated Annealing
2019
| [182407] |
| Title: Model-based Optimisation with Tree-structured Parzen Estimation for Discrete Event Simulation at Container Terminals: Modellbasierte Optimierung mit baumstrukturierter Kerndichteschätzung für ereignisdiskrete Simulation auf Container-Terminals. <em>Simulation in Produktion und Logistik</em> |
| Written by: Kastner, Marvin and Nellen, Nicole and Jahn, Carlos |
| in: <em>ASIM 2019</em>. (2019). |
| Volume: Number: |
| on pages: 489-498 |
| Chapter: |
| Editor: In Putz, Matthias and Schlegel, Andreas (Eds.) |
| Publisher: Wissenschaftliche Scripten: |
| Series: |
| Address: Auerbach /Vogtl. |
| Edition: 1 |
| ISBN: 9783957351135 |
| how published: |
| Organization: |
| School: |
| Institution: |
| Type: |
| DOI: |
| URL: http://www.asim-fachtagung-spl.de/asim2019/papers/47_Proof_164.pdf |
| ARXIVID: |
| PMID: |
Note:
Abstract: Traditionally discrete event simulation serves as a tool for estimating the impact of managerial decisions at container terminals. For complex situations, simulation-based optimisation can be employed to lower the amount of executed simulation experiments and therefore reduce the time to obtain results. The search is guided by the already run experiments. This paper examines the applicability of the Tree-structured Parzen Estimator for optimizing simulation models of container terminals. The reasoning is based both on prior literature in the field of simulation and machine learning, as well as a numerical study. Within the experiments, the performance of the Tree-structured Parzen Estimator is compared with Simulated Annealing and Random Search. The Tree-structured Parzen Estimator displays a more explorative and robust search behaviour than Simulated Annealing