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SmartDelivery

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Machine Learning and the Internet of Things for Optimisation of the Last Mile De...

Machine Learning and the Internet of Things for Optimisation of the Last Mile Delivery 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 appl... ver más

28/02/2027

USAL

207K€

Presupuesto del proyecto: 207K€

Líder del proyecto

UNIVERSIDAD DE SALAMANCA No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 1149

Ver 3 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-04-27

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 ExpectedOutcome:

I+D

Cerrada hace 2 años

0% 100% 100%

3 Participantes

USAL

206.64K€ | Lider

KAIROS DIGITAL ANALITYCS AND...

N.D. | Participante

MDU

N.D. | Participante

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Duración del proyecto: 46 meses Fecha Inicio: 2023-04-27
Fecha Fin: 2027-02-28

Líder del proyecto

UNIVERSIDAD DE SALAMANCA No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 1149

Presupuesto del proyecto 207K€

Descripción del proyecto 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 senseparameter. A ML module will predict the best among a chosen portfolio of different heuristics/metaheuristics algorithms to optimise the route.

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