Nicole Nellen, M. Sc.
Address
Hamburg University of Technology
Institute of Maritime Logistics
Am Schwarzenberg-Campus 4 (D)
21073 Hamburg
Contact Details
Office: building D room 5.005
Phone: +49 40 42878 6136
E-mail: nicole.nellen(at)tuhh(dot)de
ORCiD: 0000-0002-3911-1811
Research Focus
- Port Drayage and Hinterland Transports
- Container Terminals
- Discrete Event Simulation
- Simulation-based process optimization of intermodal terminlas
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