Nicole Nellen, M.Sc.
Adresse
Technische Universität Hamburg
Institut für Maritime Logistik
Am Schwarzenberg-Campus 4 (D)
21073 Hamburg
Kontaktdaten
Büro: Gebäude D Raum 5.005
Tel.: +49 40 42878 6136
E-Mail: nicole.nellen(at)tuhh(dot)de
ORCiD: 0000-0002-3911-1811
Forschungsschwerpunkte
- Hafeninterne Transporte und Hinterlandtransporte
- Binnen- und Seehafen-Containerterminals
- Ereignisorientierte Simulation
- Simulationsgestützte Ablaufoptimierung von KV-Terminals
Veröffentlichungen (Auszug)
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