Keeping the Routes Open: Optimal Maintenance of Infrastructure Networks.

The maintenance and repair of large-scale transportation infrastructure is known to be very costly, as is the loss of efficiency and capacity when components of such infrastructure has been damaged. It is therefore of interest to allocate resources optimally to maintain such infrastructure. Specifically, given that the infrastructure is in some given state of repair or disrepair, with certain routes operating well and other routes damaged and not fit for use, we want to select or prioritise certain routes for repair so as to allocate resources as efficiently as possible. These decisions must also be made sequentially: every time a new route is known to have been damaged, or an ongoing repair has been completed, we want to make a new decision on what to do next.
Generic algorithms already exist which learn to control these sequential decision-making problems optimally. For our problem, optimal control would mean that we minimise the average combined cost of ongoing repairs and loss of capacity over a long period of time. However, for complicated problems such as ours, these algorithms are known to be extremely slow and are therefore unsuitable, so we must turn our attention to more approximate or heuristic (rule-of-thumb) techniques. Such techniques can explicitly incorporate any theoretical understanding of the problem, allowing optimal approaches to be learned much faster by exploiting any known properties. It is the creation and evaluation of such techniques, and the discovery or proof of theoretical properties, that is the focus of our research.
Our hope is that by finding suitable approximations or heuristics, these could be rolled out and applied to any network-based infrastructure, and yield results that outperform any simple naive approaches.

In partnership with Naval Postgraduate School.

This proposal will benefit (i) the UK economy and society, (ii) our industrial partners, (iii) the wider community of non-academic employers of doctoral graduates in STOR, (iv) the scientific disciplines of statistics and operational research and associated academic communities, (v) UK doctoral students in STOR, and (vi) the CDT students themselves.

Below we outline how each of these communities will realise these benefits:

(i) The UK economy will gain a competitive edge through a significant increase in the supply and diversity of doctoral STOR professionals with the skills required to undertake influential, responsible and impactful research, and who have been trained to become future leaders. Our goal is that our future alumni who enter industry assume leading roles in realising the major impact that STOR can make in achieving effective data driven decision-making. Our existing alumni are already starting to achieve this. A wider societal benefit will accrue from research contributions to EPSRC Prosperity Outcomes, e.g. to the UK being a Productive and Resilient Nation.

(ii) Our industrial partners will particularly benefit from the skills supply identified in item (i), as likely employers of STOR-i graduates. They will further benefit from teaming with a community of leading edge STOR researchers in the solution of substantive industrial challenges. Mechanisms for the latter include doctoral projects co-supervised with industry, industrial internships, engagement in research clusters and industrial problem-solving days. Our training programme will give students the skills they need to ensure that research is conducted responsibly and that outcomes are successfully communicated to beneficiaries. The value that our industrial partners place on working with STOR-i can be seen through the pledged cash support of £1.7M.

(iii) A wider benefit will accrue from the employment of STOR-i graduates, equipped as described in items (i) and (ii), across non-partner public and private sector organisations. The breadth and depth of training provided by the CDT will enable students to quickly make a difference in these organisations, using their research skills to affect significant change.

(iv) The STOR academic community will benefit from methodological advances and from the increase and diversity in the supply of STOR researchers who value, and have experience of, collaborative research. Our alumni will be leaders in 21st Century Statistics with a strong culture of, and training in, reproducible research and a focus on achieving impact with excellence. Our recruitment strategy will further benefit this community in achieving a healthier supply of high-quality doctoral candidates from diverse backgrounds. Our research internship programme gives top mathematically able individuals from across the UK an experience of STOR research and has been shown to increase applications for STOR PhD programmes across the UK.

(v) Elements of the STOR-i programme will benefit the wider community of UK doctoral students in STOR. Using financial support from our industrial partners, we will continue our National Associate Scheme. This will provide up to 50 UK STOR doctoral students with funding and access to elements of STOR-i's training programme. An annual conference will provide opportunities for learning, networking and sharing research progress to members of the scheme.

(vi) STOR-i students will benefit from a personalised programme that will support each individual in fully achieving their research leadership potential, whether in academia or industry. Students will be given the tools and opportunities to develop research and broader skills that will enable them to achieve maximum scientific impact for their work. Our current alumni provide strong evidence that these future graduates will be extremely employable.