Details
| Original language | English |
|---|---|
| Number of pages | 12 |
| Publication status | Published - 2020 |
| Event | 2020 Summer Computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020 - Virtual, Online Duration: 20 Jul 2020 → 22 Jul 2020 |
Conference
| Conference | 2020 Summer Computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020 |
|---|---|
| City | Virtual, Online |
| Period | 20 Jul 2020 → 22 Jul 2020 |
Abstract
Despite the success in developments of wind energy technology, there remain challenges, for example, in offshore wind energy installation. Due to changeable and unstable offshore weather conditions, it is hard to effectively schedule the installation logistics. In this work, we propose a simulation-based scheduling strategy to help the operators and the project managers, to make the main decisions during the installation to increase efficiency, e.g. how many offshore wind turbines should be loaded onto the installation vessel. The offshore logistic concept is modeled using timed Petri nets (TPN) approach. The timed transitions in the TPN model are assigned with operation times estimated by means of discrete-time Markov chain (DTMC) approach, which uses historical weather data. Besides, operability is introduced in this work as an indicator to evaluate schedules of a certain time period.
Keywords
- Data-driven scheduling, Discrete-time Markov chain, Offshore wind installation, Timed Petri nets
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
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2020. Paper presented at 2020 Summer Computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020, Virtual, Online.
Research output: Contribution to conference › Paper › Research
}
TY - CONF
T1 - Simulation-based scheduling for offshore wind farm installation using timed petri nets approach
AU - Peng, Shengrui
AU - Rippel, Daniel
AU - Lütjen, Michael
AU - Becker, Matthias
AU - Szczerbicka, Helena
N1 - Funding Information: The authors gratefully acknowledge the financial support by the DFG (German Research Foundation) for the Project "Offshore Plan", grant number LU 2049/1-1; SZ 51/33-1.
PY - 2020
Y1 - 2020
N2 - Despite the success in developments of wind energy technology, there remain challenges, for example, in offshore wind energy installation. Due to changeable and unstable offshore weather conditions, it is hard to effectively schedule the installation logistics. In this work, we propose a simulation-based scheduling strategy to help the operators and the project managers, to make the main decisions during the installation to increase efficiency, e.g. how many offshore wind turbines should be loaded onto the installation vessel. The offshore logistic concept is modeled using timed Petri nets (TPN) approach. The timed transitions in the TPN model are assigned with operation times estimated by means of discrete-time Markov chain (DTMC) approach, which uses historical weather data. Besides, operability is introduced in this work as an indicator to evaluate schedules of a certain time period.
AB - Despite the success in developments of wind energy technology, there remain challenges, for example, in offshore wind energy installation. Due to changeable and unstable offshore weather conditions, it is hard to effectively schedule the installation logistics. In this work, we propose a simulation-based scheduling strategy to help the operators and the project managers, to make the main decisions during the installation to increase efficiency, e.g. how many offshore wind turbines should be loaded onto the installation vessel. The offshore logistic concept is modeled using timed Petri nets (TPN) approach. The timed transitions in the TPN model are assigned with operation times estimated by means of discrete-time Markov chain (DTMC) approach, which uses historical weather data. Besides, operability is introduced in this work as an indicator to evaluate schedules of a certain time period.
KW - Data-driven scheduling
KW - Discrete-time Markov chain
KW - Offshore wind installation
KW - Timed Petri nets
UR - http://www.scopus.com/inward/record.url?scp=85099276547&partnerID=8YFLogxK
M3 - Paper
T2 - 2020 Summer Computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020
Y2 - 20 July 2020 through 22 July 2020
ER -