A successful review milestone
Last week marked a key moment for the MILA project with the official review meeting. The FAIR meeting with the reviewers confirmed that all activities are progressing as planned and
the project is delivering on its ambitious goals.
Coordinated by Optit under the
FAIR programme – Spoke 8 “Pervasive AI” – the project focuses on
hybrid decision-support models in the
Logistics and
Environmental sectors.
The positive feedback received validates the quality and consistency of the work carried out, highlighting the project’s contribution to both
scientific advancement and
real-world applications.
From concept to prototype: what we’ve built so far
Since the beginning of 2025, the project has moved from feasibility analysis to the
design,
prototyping, and
partial integration of hybrid models that c
ombine symbolic methods with machine learning techniques.
Notable developments include a
prototype for
correcting and inferring master data for routing algorithms, a
demonstrator for GPS-based road speed estimation, and
forecasting modules capable of handling anomalies and missing values in time series. These efforts have already led to the integration of new features in
Optit’s dispatching platform and the creation of demonstrators for workforce planning and load optimization.
We also presented a joint scientific paper with the
University of Milan at
ISC 2025, one of the leading international conferences on high-performance computing and its applications.
The paper focuses on the use of
LSTM models to learn decision-makers’ preferences in routing, bringing hybrid AI closer to real operational needs.
Real-world impact: use cases in Logistics and Waste management
Concrete use cases explored during the project underscore the potential of
hybrid AI in
improving operational decisions.
In Retail Logistics, we developed
machine learning models to automatically
infer delivery time windows and vehicle compatibility for new customer locations. In the load building domain, a
smart parameter tuning method was applied to
metaheuristic algorithms, resulting in a
3.5x speed-up and better optimization results within fixed time budgets. In the Waste sector, we tested
hybrid clustering approaches to divide urban areas into efficient collection zones, and we designed
solutions to enhance road network modelling based on GPS data.
These case studies demonstrate that
hybrid AI is not just a promising research direction, but a
powerful enabler of smarter, more efficient, and more sustainable operations.
As the MILA project enters its final phase, the results achieved so far provide a solid foundation for continued innovation, driven by the integration of symbolic and data-driven method, and open new avenues for industrial adoption and academic collaboration.
The MILA project has been funded under the FAIR programme (code PE00000013), as part of Italy’s National Recovery and Resilience Plan (PNRR), with funding from the European Union – NextGenerationEU. A successful review milestone
Last week marked a key moment for the MILA project with the official review meeting. The FAIR meeting with the reviewers confirmed that all activities are progressing as planned and
the project is delivering on its ambitious goals.
Coordinated by Optit under the
FAIR programme – Spoke 8 “Pervasive AI” – the project focuses on
hybrid decision-support models in the
Logistics and
Environmental sectors.
The positive feedback received validates the quality and consistency of the work carried out, highlighting the project’s contribution to both
scientific advancement and
real-world applications.
From concept to prototype: what we’ve built so far
Since the beginning of 2025, the project has moved from feasibility analysis to the
design,
prototyping, and
partial integration of hybrid models that c
ombine symbolic methods with machine learning techniques.
Notable developments include a
prototype for
correcting and inferring master data for routing algorithms, a
demonstrator for GPS-based road speed estimation, and
forecasting modules capable of handling anomalies and missing values in time series. These efforts have already led to the integration of new features in
Optit’s dispatching platform and the creation of demonstrators for workforce planning and load optimization.
We also presented a joint scientific paper with the
University of Milan at
ISC 2025, one of the leading international conferences on high-performance computing and its applications.
The paper focuses on the use of
LSTM models to learn decision-makers’ preferences in routing, bringing hybrid AI closer to real operational needs.
Real-world impact: use cases in Logistics and Waste management
Concrete use cases explored during the project underscore the potential of
hybrid AI in
improving operational decisions.
In Retail Logistics, we developed
machine learning models to automatically
infer delivery time windows and vehicle compatibility for new customer locations. In the load building domain, a
smart parameter tuning method was applied to
metaheuristic algorithms, resulting in a
3.5x speed-up and better optimization results within fixed time budgets. In the Waste sector, we tested
hybrid clustering approaches to divide urban areas into efficient collection zones, and we designed
solutions to enhance road network modelling based on GPS data.
These case studies demonstrate that
hybrid AI is not just a promising research direction, but a
powerful enabler of smarter, more efficient, and more sustainable operations.
As the MILA project enters its final phase, the results achieved so far provide a solid foundation for continued innovation, driven by the integration of symbolic and data-driven method, and open new avenues for industrial adoption and academic collaboration.
The MILA project has been funded under the FAIR programme (code PE00000013), as part of Italy’s National Recovery and Resilience Plan (PNRR), with funding from the European Union – NextGenerationEU. 