| Project Detail |
New tools to help logistics experts navigate the last mile challenge
In the world of logistics, the last-mile delivery (LMD) problem is one of the most significant challenges, creating bottlenecks that drive up costs and inefficiencies. Traditional route optimisation methods falter in the face of real-time disruptions, leaving logistics experts grappling with an age-old dilemma. With the support of the Marie Sklodowska-Curie Actions programme, the SmartDelivery project will leverage the synergy of Machine Learning (ML) and IoT to tackle LMD. Specifically, it introduces a novel hardware/software architecture, utilising real-time vehicle data to continuously enhance routing algorithms. Additionally, an innovative IoT-based approach dynamically assigns routes to drivers, guided by a unique ‘sixth sense’ parameter. A ML module predicts the optimal heuristic/metaheuristic algorithm to perfect the route.
Scientific advances in recent years have brought to light a series of potentially disruptive technologies in the ICT landscape. They are becoming, and will increasingly become, key enabling technologies for the development of applications and services designed to improve the quality of life of citizens and make processes more efficient. Among these, we can identify some which research has recently focused on with particular attention: Machine Learning and Internet of Things. In this project we propose a combined use of these two technological enablers to solve one of the main issues which all logistics experts have to face: the problem of optimising the last mile delivery (LMD). LMD is a crucial step of the entire delivery process, as it causes bottlenecks and is typically the most costly, problematic and inefficient part. Improving the LMD process in terms of route optimisation using classic approaches is difficult: static algorithms are not suitable, and even heuristic algorithms do not find high-quality solutions, as they do not consider several factors such as unpredictable real-time events which may occur. To address these challenges, a novel hardware/software architecture which exploits real-time vehicles’ positions to continuously improve performances of the routing algorithms is proposed, together with a new IoT-based methodology to automatically/dynamically assign routes to drivers based on the values of a defined “sixth sense”parameter. A ML module will predict the best among a chosen portfolio of different heuristics/metaheuristics algorithms to optimise the route. |
