EU directives set out targets for renewable electricity, heat and transport. Low carbon technologies (LCTs) such as heat pumps (HPs) and electric vehicles (EVs) are critical components of the Government of Ireland’s Climate Action Plan to respond to the need to decarbonise heating and transport. The rate of adoption of these technologies is uncertain, but they pose considerable new challenges if they are to become fully- integrated components of the national infrastructure. In particular, the adoption of EVs on a large scale makes it necessary to solve large-scale, modified routing problems with many constraints more urgently than before.
This project responds to the need to identify how energy systems can be transformed to be secure (reliable), clean (green and sustainable), and fair (ensuring the citizen is at the centre of, and benefits from the transformed system). The project aims, in particular, to explore the design demands of electric vehicle routing problems and how they might be resolved efficiently and effectively.
Specifically, for problems such as the Travelling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP), the traditional approaches of integer linear programming solvers and constraint programming solvers do not scale to larger problem instances. The project will build on existing supervised, unsupervised and reinforcement learning techniques to develop scalable solutions for these optimisation problems. These machine learning techniques will be used to improve algorithmic efficiency, in terms of solution-time and to solve more difficult routing problems with more constraints while enabling developers to automatically design their algorithms to solve specific problem variants. Building on the recent advances in these fundamental machine learning areas, the project will aim to achieve the energy system transformation by providing fast, scalable, ML-assisted approaches to VRPs and, in particular, E-VRPs. Also, the project has opportunities to explore the generalisation of these techniques to other difficult, important combinatorial optimisation problems by designing problem-independent techniques.