Intelligent port logistics

   Faculty of Business and Law

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  Dr Jana Ries, Dr Rosa González Ramírez  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the Subject Group Operations and Systems Management and will be supervised by Dr Jana Ries (University of Portsmouth) and Dr Rosa González Ramírez (Universidad de los Andes, Chile).

The work on this project will involve:

  •   Exploring core concepts of machine learning and data analytics in the context of port logistics
  •   Design and Implementation of computational experiments
  •   Quantitative data analysis

Project description

Recent global events have made operational planning in freight networks increasingly challenging. Whilst, for example, the growth forecast for maritime trade was estimated to be 3.5% (2019-2024), the Covid pandemic and economic uncertainty have caused economies worldwide to decelerate. Now, in the current period of recovery, global supply chains require an extraordinary level of efficiency with intermodal hubs such as maritime ports taking a central role in ensuring this by reducing operational costs while providing significant service levels. 

Intelligent decision making, using machine learning and data analytics, has become increasingly important to support the relevant decision making in container ports. The project explores the use of these disciplines in the area of yard management by drawing an initial focus on the container storage space allocation (SSAP). The SSAP considers the allocation of a set of containers arriving from the seaside and/or hinterland into the yard. It aims to ensure that the allocation ensures port efficiency (e.g. reducing ineffective moves of containers and waiting times, and maximising fuel efficiency, etc.). Considering the SSAP isolated to other yard operations, the research will focus on investigating hybrid algorithmic strategies using machine learning to provide a proactive and/or adaptive approach to disturbances within and outside of the container transport network. 

Further extensions to the project proposal in the area of container yard management are invited, with a particular interest in investigating the interdependence of the SSAP with other decision making problems in the container yard, including resource planning, vehicle routing and pre-marshalling. 

General admissions criteria

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements 

We particularly welcome applications from students with an understanding in Operational Research (e.g. industrial engineering, computer science, mathematics and other relevant disciplines). 

How to Apply

We’d encourage you to contact Dr Jana Ries  ([Email Address Removed]) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Operational Research and Logistics PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘How to Apply’ page offers further guidance on the PhD application process. 

Please also include a research proposal of 1,000 words outlining the main features of your proposed research design – including how it meets the stated objectives, the challenges this project may present, and how the work will build on or challenge existing research in the above field. 

When applying please quote project code: O&SM4841021

Computer Science (8) Mathematics (25)

Funding Notes

Self-funded PhD students only.
Please for tuition fee information and discounts.
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