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Details of Grant 

EPSRC Reference: EP/W028492/1
Title: Digitalisation for operational efficiency and GHG emission reduction at container ports
Principal Investigator: Song, Professor D
Other Investigators:
Xie, Professor Y
Researcher Co-Investigators:
Project Partners:
Port of Felixstowe
Department: Management School
Organisation: University of Liverpool
Scheme: Standard Research - NR1
Starts: 01 February 2022 Ends: 31 October 2022 Value (£): 42,605
EPSRC Research Topic Classifications:
Energy Efficiency Mathematical Aspects of OR
Transport Ops & Management
EPSRC Industrial Sector Classifications:
Energy Information Technologies
Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
28 Oct 2021 Siemens EPSRC Digital Solutions for Energy Demand Reduction Announced
Summary on Grant Application Form
Ports are regarded as concentrated areas producing air pollutants and greenhouse gas (GHG) emissions. Container ports play an important role in the global economy as they handle over 50% of seaborne world trade by value. Due to surging trade volume, disruptive events, and lack of coordination across relevant stakeholders, container ports often experience inefficiency and severe congestion. Port congestion creates the requirements for extra and unproductive moves when containers are stacking or retrieving, resulting in longer turnaround times for vessels and trucks.

According to the Environmental Report 2019-20 produced by the Port of Felixstowe, about 60% GHG emissions (equivalent to 34.3K tons of CO2) from port operations originate from fossil fuelled yard cranes and internal trucks. The deployed fleet of trucks travels more than 14 million km a year, consuming about 4.2 million litres of diesel fuel per year and producing 26.5K tons of CO2 per year. The fleet of cranes consumes around 6.0 million litres of diesel fuel per year and generates nearly 7.8K tons of CO2 yearly. The port acknowledges that nearly 30% crane movement is unproductive, and improvements in yard management, reducing the empty travel time, can dramatically reduce both fuel consumption and GHG emissions (potentially by 15%, i.e. 1.5 million litres of fuel and 6.1K tons of CO2). This project applies digital technologies such as machine learning and optimisation techniques to develop a new decision support system to reduce unproductive crane movement and truck travel distance. As a result, the product productivity and efficiency will be improved, more containers can be handled within time windows, and vessel and truck turnaround times will be reduced. GHG emissions from trucks, ocean-going vessels and cargo handling equipment will be reduced. The project will directly benefit container ports, by improving ocean freight efficiency. The decision support system will work as a part of a physical and digital ecosystem which will facilitate the development of maritime autonomy and support the UK's transition towards 'zero-emission' shipping. The project will also indirectly benefit other stakeholders including shipping lines, rail operators and shippers, by automating process, reducing their costs, boosting trading volume and economic growth.

Our innovation focuses on: (i) the pioneering attempt to apply digital technologies to predict import containers' out-terminals at the point when they are discharged from vessels to improve stacking operations; (ii) using the ground-breaking approach of combining predictive models with prescriptive models to support yard container allocation decisions; (iii) advance the knowledge on the relative importance of determinant factors (container attributes) to predict containers' out-terminals and quantify the contributions made by each factor to the prediction. The quantifiable information will inform maritime policy making, for example, introducing appropriate regulations or incentive programs, to encourage information sharing between ports and the stakeholders, so as to improve operational efficiency and reduce GHG emissions at ports.
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Organisation Website: http://www.liv.ac.uk