Big data-driven ship route planning

Big data-driven ship route planning to maximise safety and energy efficiency

Degree type

PhD

Closing date

18 July 2022

Campus

Launceston

Citizenship requirement

Domestic/International

About the research project

In the fragile global supply chain post-pandemic, reduction in bunker fuel consumption is critical to reduce the global supply chain cost because ships transport more than 90% of international trade cargoes and bunker fuel accounts for a significant portion of operating costs of a ship. Safety of ships and their cargoes are also paramount for global supply chain. In the operational level, these two factors are addressed by deck officers on a daily basis through ship route planning.

This project aims to develop ship route planning models and algorithms driven by a big set of data, including weather archive and forecast data (swell, waves, sea currents, wind) and ship energy consumption recorded data. The proposed models and algorithms will factor in a ship’s dynamic stability requirements in different weather/sea conditions as well as the target of reducing bunker fuel consumption and mitigating ship emissions. To enhance the industry applications, the project also aims to develop novel computerised models and algorithms by integrating the weather forecast data and websites widely-used by seafarers. Safety of ships in terms of dynamic stability, ship energy efficiency, and emissions are the core considerations of the models and the theoretical innovations, while the computerisation of models and algorithm will automate the process of deck officers’ ship route planning. Dynamic stability will focus on the operational limitations posed by Second Generation of Intact Stability Criteria (SGISC), and ship energy efficiency will be quantified by machine learning models.

Primary Supervisor

Meet Dr Bill Du

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

  • a living allowance stipend of $28,854 per annum (2022 rate, indexed annually) for 3.5 years
  • a relocation allowance of up to $2,000
  • a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Eligibility

Applicants should review the Higher Degree by Research minimum entry requirements.

Selection Criteria

The project is competitively assessed and awarded.  Selection is based on academic merit and suitability to the project as determined by the College.

Additional essential selection criteria specific to this project:

  • Strong computer programming capability, such as Python and C++
  • Strong background on machine learning models
  • Knowledge on optimisation and algorithms

Additional desirable selection criteria specific to this project:

  • Knowledge on ship energy efficiency and ship stability modelling

Application process

There is a three-step application process:

  1. Select your project, and check you meet the eligibility and selection criteria;
  2. Contact the Primary Supervisor, Dr Bill Du to discuss your suitability and the project's requirements; and
  3. Submit an application by the closing date listed above.
    • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
    • As part of your application, you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.

Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

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