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CALSCALE:GREGORIAN
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TZID:Asia/Kolkata
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DTSTART:19700101T000000
TZOFFSETFROM:+0530
TZOFFSETTO:+0530
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BEGIN:VEVENT
UID:6a235c39de684@www.maritimeinstitute.sg
DTSTAMP:20260605T233105Z
DTSTART;TZID=Asia/Kolkata:20190802T080000
DTEND;TZID=Asia/Kolkata:20190802T080000
LOCATION:Innovation 4.0, Level 1 Conference Room, National University of Singapore, 3 Research Link, Singapore 117602
SUMMARY:SMI Breakfast Series
DESCRIPTION:As part of Singapore Maritime Institute’s (SMI) continuing efforts to promote research collaboration between the industry and academia, the SMI Breakfast Series provides a casual setting where research topics can be shared and discussed over breakfast. Participants can learn more about existing and upcoming R&D trends and initiatives, and how they can be relevant to the development of the maritime industry.SMI is partnering the Centre of Excellence in Modelling and Simulation for Next Generation Ports (C4NGP) for the SMI Breakfast Series on Friday, 2 August 2019.Programme:0800 – 0830 Registration and Breakfast0830 – 0840 Welcome Remarks by Dr Sanjay Kuttan, Executive Director, SMI0840 – 0910 Presentation: Smart Digital Twin Solution for Next Generation Ports by Prof Chew Ek Peng, Director, C4NGP0910 – 0930 Q&A Session0930 – 1000 End of Event and NetworkingDue to limited seating, registration for the session is compulsory.About Smart Digital Twin Solution for Next Generation PortsThe digital twin is a digital manifestation of the physical systems and it has the ability to assist in the designing, planning and operations of the port systems. The plaform is based on the object oriented Discrete Event Simulation modelling framework – ODES.Net. This platform is not only able to evaluate a system but has the ability to search for optimal or good enough solutions as it can be integrated with optimisation methods. Furthermore, for the digital twin to assist in real time port operations, it will need to embed analytics and learning engine to replace the optimisation model as simulation optimisation methods are computationally expensive in time. The idea is to learn from optimal results offline by solving the optimisation model using many different scenarios and then derive simple policies to replace the optimisation model.
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