Co-op-Solve-M: A Co-operative Algorithm Framework for Solving Large-Scale Heterogeneous Problems with Multiple Objectives

Rapid developments needed in new energy technologies (e.g. for scalable generation plants that can provide a continuous supply) will depend upon solving linked and large-scale global optimisation problems. Whereas algorithms for academic benchmark problems are well-studied, progress in more realistic optimisation settings is being stifled by a lack of deep understanding. Most real problems are multi-objective and are composites of several problem types, and what’s needed is a deep dive into how to efficiently solve such problems cooperatively. There are a number of distinct but related research threads that can be brought together to tackle this challenge and make progress. These include: using teams of single-objective solvers to cooperatively tackle multiobjective problems; automatic algorithm configuration and selection methods that can combine and tune algorithmic components; and so-called ‘interwoven’ problems. Our aim will be to discover common principles among these ideas, and bring them together into a new paradigm. For more details see https://lopez-ibanez.eu/icase-phd-2024.

Research questions

1. Under which conditions can we decompose multiple objective problems, for which we would like to obtain p different trade-off solutions, into p separate single-objective subspace problems by co-operatively maximising ‘distance’ from p−1 other solutions?
2. What other methods of multiobjective problem decomposition provide a promising general method, and what are their properties?
3. How can we adapt automatic configuration (hyper-parameter optimisation) methods such as irace to combine and tune cooperative collections of single-objetive solvers to tackle a multiobjective problem? Can this be done efficiently?
4. Heterogeneity exists in multiobjective problems in several different ways. Can we identify meta-features and a single “language” for describing such problems to enhance the meta-learning and meta-optimisation that arise during the automatic configuration phase?
5. How can we enrich the description of interwoven problems made up of separate but interlinked elements, and can we identify common challenges in such problems?

Industrial partner

SLB is the new name for Schlumberger (after rebranding in 2022). SLB is a global energy technology company employing 95,000 people with 160 nationalities, and operating in over 120 countries. SLB has been an international leader in supplying the technology for most of the main energy firms since the 1920s. It prides itself on its people, technology and performance. Nowadays, SLB is committed to bringing the tools needed for a global energy transition at scale. It is investing significantly in innovation of diverse solutions to energy production and storage, carbon capture and related new opportunities. It also sees digital solutions as a core growth area and enabling technology of the business.

Schlumberger Cambridge Research, located on one of the science parks at Cambridge University (near to the famous Cavendish Laboratory and Cambridge Computer Laboratory among others), is a subsidiary of SLB, and represents the pinnacle of SLB’s research capability. It employs around 70 scientists with expertise in all areas of energy development and digital technologies. Its purpose is to develop technologies in TRL1,2,3 (the most innovative) and with a 5-10 year development horizon.

The academic supervisory team includes Manuel Lopez-Ibanez (Alliance Manchester Business School) and Joshua Knowles (University of Birmingham). For queries regarding the project, please email the supervisors directly (manuel.lopez-ibanez@manchester.ac.uk, j.knowles@cs.bham.ac.uk)

Entry requirements

• 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. 

Applications are sought from talented and motivated candidates with an academic background in at least one of these fields: Computer Science, Mathematics, Statistics, Operations Research, Data Science, Machine Learning, Industrial / Business Engineering or Business Analytics. 

English language requirements (for international/EU candidates)

You have must have one or more of the following:

• IELTS test minimum scores – 7.0 overall, 6.5 other sections.
• TOEFL (internet based) test minimum scores – 100 overall, 25 in all sections.
• Pearson Test of English (PTE) UKVI/SELT or PTE Academic minimum scores – 76 overall, 76 in writing, 70 in other sections.
• To demonstrate that you have taken an undergraduate or postgraduate degree in a majority English speaking nation within the last 5 years.
• Other tests may be considered.

How to Apply:

Candidates should submit an online application for PhD Business & Management, and indicate that they wish to be considered for the EPSRC/SLB INDUSTRIAL CASE PhD Studentship for “Co-op-Solve-M: A Co-operative Algorithm Framework for Solving Large-Scale Heterogeneous Problems with Multiple Objectives”.

Apply here.

For questions related to your application, contact: HUMS.doctoralacademy.admissions@manchester.ac.uk

Supporting Documents:

• A 3,000 word PhD research proposal.
• Copies of the academic transcript and certificate from both your Bachelor’s and Master’s degrees. If your Master’s degree is pending, please provide an interim transcript.
• An up to date academic CV, detailing your education and qualifications; employment history; publications; and any other relevant information.
• You must nominate two academic referees (including one from your most recent institution). Your referees will be contacted directly via the Referee Portal following submission of your application form. You may wish to contact your referees to request they submit your reference in a reasonable timeframe as this forms part of the review process.
• An Equal Opportunities Monitoring Form
• International applicants must additionally provide English Language evidence (e.g IELTS).