The Algorithm People (TAP) and Teesside University are working together to create a solution for dynamic vehicle-to-vehicle optimisation, which could transform final-mile deliveries and eliminate routine back-to-base reloading.

The two organisations have entered into a Knowledge Transfer Partnership (KTP) to take the project from initial research, big data analysis and AI development phases through to successful commercialisation.

The Mobile and Transient Hubs Project extends TAP’s optimisation platform My Transport Planner, (MTP) which combines algorithms and machine learning to improve journey efficiency and load allocation for ICE, EV or mixed fleets.

Teesside University academics from its Centre for Digital Innovation have then developed the artificial intelligence which will enable vehicle-to-vehicle optimisation in addition to MTP’s fleet optimisation capabilities.

The most immediate real-world application for the project is the elimination of back-to-depot reloading, instead allowing smaller vehicles to reload from a larger vehicle. This is a game-changer for final mile deliveries, akin to aviation’s in-flight refuelling. It potentially allows small and micro vehicles to out-perform larger back-to-base vans and trucks.

Fleet optimisation with MTP typically delivers fleet savings of up to 20%, including minimised mileage and fuel use and the associated emissions. The Mobile and Transient Hubs project seeks to  deliver, an additional 20% savings on top of this.

Teesside University recognised the enormous potential for the development, and won Innovate UK funding to finance the work of project supervisors Dr Yingke Chen and Dr Claudio Angione, both associate professors at the School of Computing, Engineering & Digital Technologies, and Dr Ross Conroy, who is seconded to TAP to create new algorithms to power the artificial intelligence (AI).

Dr Angione says: “This KTP stems from our research in machine learning and optimisation. Being applied to real-world data and in live client environments, this project represents a promising case study of novel machine-learning algorithms, which we will refine as part of this journey. We are creating a system which can dynamically adjust a delivery hub’s location and capacity in relation to the vehicles it serves. This will make TAP’s optimisation products even more effective in terms of throughput and efficiency.”

KTPs are a university-business collaboration which develop and embed new expertise into an organisation to meet a strategic objective. This means that IP and innovation created by university research can meet the needs of industry and be successfully commercialised.

CEO and founder of The Algorithm People, Colin Ferguson says: “Decarbonising our cities and our deliveries requires new ways of looking at vehicle movements, and sophisticated technology to ensure that there is no waste in our supply chains.

“This project is important because it offers a quantum leap in urban logistics efficiency and it facilitates the commercial use of different types of net-zero vehicle which can work safely even in pedestrianised areas.”

Dynamic reloading is a solution to a previously unconsidered logistics challenge which could help solve urban and environmental problems. It can help to reduce the number of large and ICE goods vehicles in urban spaces, significantly reducing congestion, harmful emissions and the road safety threat to vulnerable road users.

The major obstacle to replacing HGVs or large vans with smaller vehicles is their much smaller payloads which means that many more vehicles would be required in a traditional model. The Mobile and Transient Hubs product overcomes this obstacle and drastically reduces vehicle mileage.

A pilot with if.Vehicles’ micro electric quadricycles has shown that using vehicle-to-vehicle reloading enabled by the new technology, one van plus four Micros could deliver the same daily quantities as seven standard vans.