There are over 1,600 registered social housing providers in England, managing in excess of 4.4 million homes. Each of these properties requires statutory inspections to check gas, asbestos and water hygiene, in addition to general upkeep.

However, there is not currently a scheduling system available that offers integration between key maintenance and safety contractors, resulting in additional site visits and increased travel costs and re-work.

Aston University computer scientists will use machine-learning and AI to create a maintenance prioritisation system that will centralise job requests and automatically allocate them to the relevant contractors.

The collaboration is through a Knowledge Transfer Partnership (KTP) - a collaboration between a business, an academic partner and a highly-qualified researcher, known as a KTP associate.

This partnership builds on the outcomes of TL’s first collaboration with Aston University, by expanding the system developed for the company’s in-house use - which directs its field staff to jobs.

The project team will improve the system developed during the current KTP to enable it to interact with client and contractor systems, by combining an input data processing unit, enhanced optimisation algorithms, customer enhancements and third-party add-ons into a single dynamic system.

The Aston University team will be led by Aniko Ekart, professor of artificial intelligence. She said: “It is a privilege to be involved in the creation of this system, which will select the best contractor for each job based on their skill set, availability and location and be reactive to changing priorities of jobs.”

TL, based in Fenstanton, just outside Cambridge, provides asbestos consultancy, project management and training to businesses, local authorities, social housing and education facilities, using a fleet of mobile engineers across the UK.

John Richards, managing director at Thames Laboratories, said: “This partnership will allow us to adopt the latest research and expertise from a world-leading academic institute to develop an original solution to improving the efficiency of social housing repairs, maintenance and improvements to better meet the needs of social housing residents.”

Professor Ekart will be joined by Dr Alina Patelli as academic supervisor. Dr Patelli brings experience of software development in the commercial sector as well as expertise in applying optimisation techniques with focus on urban systems. She said: “This is a great opportunity to enhance state-of-the-art optimisation and machine learning in order to fit the needs of the commercial sector and deliver meaningful impact to Thames Laboratories.”