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Reinforcement Learning for multi-mile logistics optimisation


   Centre for Computational Science and Mathematical Modelling

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  Prof James Brusey  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Coventry University is inviting applications from suitably qualified graduates for a fully-funded PhD studentship within the Centre for Computational Science and Mathematical Modelling (CSMM) to work on the project Reinforcement Learning for multi-mile logistics optimisation led by Professor James Brusey.

Coventry University has been voted ‘Modern University of the Year’ three times running by The Times/Sunday Times Good University Guide. Ranked in the UK’s top 15 (Guardian University Guide), we have a global reputation for high quality teaching and research with impact. Almost two-thirds (61%) of our research was judged ‘world leading’ or ‘internationally excellent’ in the Research Excellence Framework (REF) 2014.

Our Centre provides a hub to develop cutting edge research in the areas of Artificial Intelligence, Data Science and Future Computing, linking fundamental science to real-world applications. Data Science deals with the analysis and exploitation of large amounts of data (Big Data) drawing together disciplines as diverse as Computer Science, Artificial Intelligence, Statistics and Mathematics. This project sits within the ‘AI for Cyber Physical Systems’ research theme of the centre.

This research project is a collaboration between Coventry University and Singapore’s Agency for Science, Technology and Research (A*STAR). The successful candidate will have the opportunity to conduct their research project both at Coventry University, UK and for up to 2 years at the A*STAR in Singapore.

Multi-mile / 4PL logistics involves transmitting products or items through a delivery network, potentially with several stops and in some cases, with some operation (such as, order regrouping) occurring. The overall schedule of large numbers of items is therefore highly complex and difficult to calculate given the various constraints (such as, limited capacity of vehicles). At the same time, logistics operators wish to optimise this schedule to reduce their costs and minimise the waiting time for the end customer. Current state of the art is based on heuristic rules but it is not necessarily optimal or even nearly optimal. Solvers, such as A* search provide an optimal solution but are only tractable for relatively small-scale scheduling problems. Even if A* search can find a solution for a single order, when there are large numbers of orders and limited capacity vehicles the problem is completely intractable.

The successful applicant will start with developing a solution that can work in a simulated logistic network and then gradually include additional real-world aspects, such as limited capacity, order regrouping. A possible approach that will be explored to solve the scheduling problem is Reinforcement Learning but other options will also be considered. Throughout the PhD programme, the applicant will be interacting and collaborating with the industry contacts to ensure that the approach meets their needs. The final goal is to provide the industry with a solution that can be brought to market and used within the supply chain network.

The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.

The research centre has a vibrant, inclusive and thriving research culture, in which the doctoral researcher will be stimulated to undertake significant and high-quality work. The candidate will benefit from the Centre’s research seminar series featuring national or international speakers and a collegiate culture, situated in brand new premises and state of the art labs. We encourage doctoral researchers to also give seminars and present their work to their peers and staff within a friendly and supportive environment.  

Each studentship will include full fees: 

  • full tuition fees
  • a stipend for up to 4 years (£15500/year) subject to satisfactory progress
  • It is essential that applicants have expertise and practical experience of chemistry preferably polymer chemistry. 
  • The candidate must be able to demonstrate experience of safe working practises in a chemical laboratory. 
  • Familiarity with lignin and bio polymers, chemical modifications and polymer materials characterisation would be beneficial.
  • It would be advantageous if the applicant had knowledge of flame retardant materials.
  • Good communication, presentation and writing skills. 
  • Excellent team skills and able to work in a multi-cultural research group.
  • Applicants with publications will be highly regarded.

Entry criteria for applicants to PHD 

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average. 

PLUS 

  • the potential to engage in innovative research and to complete the PhD within a 3.5 years
  • Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).  
  • All applications require full supporting documentation, CV, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project. 

How to apply:

Please make sure that you contact Prof James Brusey prior to applying ([Email Address Removed]).

When ready to apply on line please visit: https://pgrplus.coventry.ac.uk/ All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

For further details see: https://www.coventry.ac.uk/research/research-students/making-an-application/


Funding Notes

bursary plus tuition fees - UK/EU/International
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