Mingchuan Luo

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Research Associate

Name

Dipl.-Ing. Mingchuan Luo

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Organization Name

Chair of Air Transport Technology and Logistics

Chair of Air Transport Technology and Logistics

Address work

Visiting address:

Gerhart-Potthoff-Bau (POT), Room 166 Hettnerstraße 1-3

01069 Dresden

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work Tel.

Mingchuan Luo received the Diploma degree (Dipl.-Ing.) in electrical engineering from Technical University Dresden, Germany, where he is currently pursuing a Ph.D. degree at the Chair of Air Transport Technology and Logistics. His research mainly focuses on airport management, multi-agent model to investigate the airport operations, and the data-driven methods to support airport digital transformation, in particular machine learning algorithms and their application to the complex airport environment.

Research Interests

• Total airport management
• Multi-agent based modelling (Digital twin) to support airport ground resource planing
• Multi-agent reinforcement learning to Airport CDM
• Data analysis in airport ground operations

Reseach Projects

IMHOTEP: Integrated Multimodal Airport Operations for Efficient Passenger Flow Management

ABM4APOC: Use of agent-based modelling in support to APOC decision-making processes and Total Airport Management, including passenger flow and turnaround

Publications

• Mingchuan Luo, Michael Schultz, Hartmut Fricke, and Bruno Desart, (2022). Data-driven fusion of turnaround sub-processes to predict aircraft ground time. In ATRS, 2022, Antwerp, Belgium.                                                                                                             The four finalists for Conference Best Paper Award.
• Miguel Mujica Mota, Paolo Scala, Michael Schultz, Daniel Lubig, Mingchuan Luo, and Edgar Jimenez Perez , (2021). The Rise of the Smart Passenger I: Analysis of impact on Departing Passenger Flow in Airports. In 11th SESAR Innovation Days.
• Mingchuan Luo, Michael Schultz, Hartmut Fricke, Bruno Desart, Floris Herrema, and Roc ́ıo Barrag ́an Montes, (2021), Agent-based simulation for aircraft stand operations to predict ground time using machine learning. In DASC, 2021.                        Best paper award in session Airport surface operations.
• Michael Schultz, Mingchuan Luo, Daniel Lubig, Miguel Mujica Mota, Paolo Scala,  (2021). COVID-19-RELATED CHALLENGES FOR NEW NORMALITY IN AIRPORT TERMINAL OPERATIONS. In Winter Simulation Conference 2021.