Multi-criteria Automatic Algorithm Configuration under Streaming Problem Instances (Manchester-Melbourne Dual Award)

Many critical problems in logistics, manufacturing, healthcare and other fields are solved by optimisation and machine learning algorithms. Thanks to advances in automatic configuration tools, we are able to automatically tune the parameters of these algorithms for new problems with minimal human effort. Unfortunately, these tools are designed to tune algorithms according to a single criteria and assume that the characteristics of a problem do not change over time. In the real-world, however, the users of such algorithms often face conflicting criteria, such as the time required to solve the problem versus the expected quality of the solution returned by the algorithm. Moreover, it is often the case that similar problems must be solved regularly (e.g. daily) such as in the case of a parcel delivery service, a manufacturing plant processing orders in daily batches or the daily planning of operating theatres in hospitals. In those cases, the characteristics of the daily instances of the problem may evolve over time due to economical, societal and technological changes.

This project aims to extend the capabilities of automatic configuration tools to handle multiple conflicting criteria and adapt to such changes in the problem characteristics. For this purpose, the teams at Manchester and Melbourne will join their expertise in automatic configuration of algorithms and instance space analysis.

The result of this project will be more powerful tools for tuning and deploying the critical algorithms that the modern world relies on, so that they can better adapt to changes in the problems being solved and let users decide the most appropriate trade-off among conflicting criteria.

The PhD candidate will benefit from the combined expertise of the project supervisors, and the embedding into two research environments. Dr Manuel López-Ibáñez (PI) is a worldwide expert on the automated configuration of algorithms and Dr Julia Handl is an expert in multi-objective optimisation and machine learning, including for streaming data. The team led by Melbourne Laureate Prof Kate Smith-Miles, will contribute by extending unique tools for Instance Space Analysis to analyse and visualise the relationship between instance features and configuration criteria as well as how a stream of instances evolves over time.

This PhD project will be based at the University of Manchester with a minimum 12-month stay at the University of Melbourne.

The candidate will be enrolled in the PhD program at the Alliance Manchester Business School at the University of Manchester and in the PhD program at the School of Mathematics and Statistics at the University of Melbourne.

The supervisory team at Manchester are both members of the Institute for Data Science and Artificial Intelligence (IDSAI) of the University of Manchester and Turing Fellows of the Alan Turing Institute. Both institutes provide access to a large network of researchers within the University of Manchester and the UK. In particular, colleagues at IDSAI working on mathematical optimisation and machine learning algorithms for manufacturing, logistics and healthcare problems will be interested in learning more about the capabilities provided by the methodology and MATILDA tool developed by Melbourne.

Entry requirements

Candidates will need to meet the minimum entry requirements of both Universities to be accepted and will be registered at both institutions for the duration of the programme. The entry criteria for the University of Melbourne can be found on their ‘How to Apply’ webpage.

Applicants must hold a Bachelors degree with Honours (to UK standard) of First or Upper Second (2:1) Class, and a Masters degree (to UK standard) with results of Merit at 65% or above (or overseas equivalent). 

Please note: Due to variations in the grading structures of international institutions, higher results may be required than stated here. 

English language:

Applicants whose first language is not English require one of the following:

• IELTS test minimum scores - 7 overall, 6.5 writing, 6.5 other sections
• TOEFL (internet-based) test minimum scores - 100 overall, 22 writing, 22 other sections
• Pearson Test of English (PTE Academic/Academic UKVI) minimum scores - 76 overall, 70 writing, 70 other sections

Application procedure

The application deadline will be Midnight (BST) on 19/01/2024

Apply online for PhD Business and Management in the University of Manchester. Please indicate your interest in the Manchester-Melbourne award.

Please ensure you include all required supporting documents at the time of submission, as incomplete applications may not be considered. A Personal Statement is NOT required to be submitted. You should select 'Supporting Statement is not required for this programme'.

The application must include:

• two academic references;
• photocopies of degree certificates if the degree has already been awarded and official transcripts of previous and current study;
• research proposal;
• a copy of your passport (if you will need a visa to study here);

Interview requirements

The University requires an interview for all applicants to whom we consider making an offer.

Interviews will be conducted by two academics, usually the proposed main supervisor and the subject PGR Director (or an assigned representative).

The interview can be either face-to-face or via conference call or email.

The interview serves several purposes, allowing us to:

• get a better picture of your ability to carry out the proposed doctoral project than the research proposal on its own;
• tell you what the proposed supervisor(s) can bring to the project;
• discuss with you directly any potential problems with the practical aspects of your studies and explore solutions together.

Further information

If you have any questions or would like to discuss this further, please contact Dr Manuel Lopez-Ibanez ([Email Address Removed])

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