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Research and Innovation

Welcome to our Research and Innovation page, the beating heart of our corporate mission. Learn how we put the latest ideas into action and how we are constantly innovating to create cutting-edge solutions and contribute to a better future for the public transport world.

Be part of the ADDSTRES Project

Join our research team and work with us in the development of optimization algorithms of MAIOR software solutions. Our solutions are designed for the optimization of planning and scheduling of public transport services. The ADDSTRES Project is funded by the Tuscan Region under the "Fund for Development and Cohesion" and may give you the opportunity to work with us on an applied research project in the industry. For more information, contact ricerca@adm.unipi.it

Research challenge now open: timetable and vehicle scheduling witch electric vehicles

The research consortium to which MAIOR collaborates with other 11 universities from Europe, called MINOA and under the HORIZON2020 project, has developed a new research challenge: An Integrated Timetable and Vehicle Scheduling Problem with the extra complexity due to the use of Electric Vehicles. The challenge is open until 31/05/2021 and is open for students, researchers, managers, and other professionals. For more information go to https://minoa-itn.fau.de/?page_id=921


The Tuscan Region will support MAIOR in the TICAMPS project, that aims to enhance the research of the public transportation real-time scheduling. The project will allow for a Ph.D. Student to collaborate with MAIOR at Lucca’s office through 2020.

MINOA Project

The MINOA Project is an EU-funded project led by a consortium of scientific research organizations throughout Europe. Through 2021, researchers will come together to work on the challenges associated with real-time optimization to develop more efficient algorithms and computer implementations for use across a broad spectrum of vertical markets, including energy, data analytics, and transportation. MAIOR will participate with a focus on public transportation, an industry that frequently manages challenging conditions for decision-making. The MINOA team includes MAIORwith other 10 scientific institutions from Italy, France, the Netherlands, Germany, and Austria.

Disruption management project

MAIOR has been awarded an R&D tender by the Tuscany Region to support SME-companies investing in research and innovation. The project aims to study, define, and develop an innovative real-time system to manage disruption problems caused by heavy traffic or road accidents, integrating CAD/AVL data. The goal is to provide agencies with an advanced tool that will help them quickly recover the service level offered to passengers. MAIOR will collaborate with the University of Pisa and the Technical University of Milan to develop the new algorithms needed for this new disruption management tool.

Optimization techniques for long distance duties

The partnership between MAIOR and The Politecnico di Milano, one of the most renowned Universities in Italy, is focused on the research for better scheduling algorithms. The research's topic is the "Constraint programming and shortest path algorithms within a process of Column Generation for the personnel optimization in extra-urban public transport companies". Publication: S. Gualandi, F. Malucelli, “Resource Constrained Shortest Paths with Super Additive Objective Functions, In Proc. of Principle and Practice of Constraint Programming (CP)”, LNCS 7514, pp. 299-315, 2012.

Models for timetable optimization

MAIOR and The University of Pisa collaborate on the research of the "Enhancement of speed and precision performances of optimization algorithm for problems of large dimensions", the "Analysis and implementation of integrated models for the combined optimization of vehicle blocks and driver duties in an extra-urban network", and the "Analysis and implementation of models for the generation of frequency timetables while optimizing the resources required". Publication: Alessandro Bertolini, “Algoritmi per l'ottimizzazione simultanea di orari e turni nel trasporto pubblico urbano” (IT), G. Thesis. Relator: Antonio Frangioni, 2013.

Parallel optimization techniques

MAIOR has been working closely with The University of Pisa on the "Research and re-engineering of algorithm of duty generation finalized to the introduction of parallel calculation techniques for multi-core processors". Publications: F. Bernazzani, S. Carosi, A. Frangioni, A. Gaffi, L. Girardi, “Miglioramenti Algoritmici nella Soluzione di Problemi Reali di Schedulazione di Veicoli e Personale”, Chapter 30 in “Scienza delle decisioni in Italia: applicazioni della ricerca operativa a problemi aziendali,” (IT). G. Felici and A. Sciomachen eds., EGIC Genova, pp 429–442, 2008; and A. Davini, A. Frangioni, “L'Ottimizzazione della Pianificazione Turni per il Trasporto Pubblico” (IT), Matematica e Impresa 1, pp35, 2008.

Models for simulation with tariff zones

The University of Florence and MAIOR worked in the evolution of MAIOR’s solutions for transit scheduling planning. The research's topic is the "Evolution of models for the simulation of urban public transport applied to fare zone policies. Re-formulation of the assignment model and modification of the algorithm". Publication: Arturo de Santis, “Modelli e algoritmi per i sistemi di trasporto collettivo in presenza di zone tariffarie” (IT), G. Thesis. Relators: Fabio Schoen, Paola Cappanera, Lorenzo Sassolini, 2005.

Optimization Algorithms for rail operators

The University of Bologna and MAIOR worked in the enhancement of MAIOR’s railway scheduling solutions for rail agencies. The research’s topic is the "Research activity finalized to the development of Optimal Vehicle Block Scheduling Algorithms for Railway Companies, allowing the multi-coverage of the trips in order to satisfy the required demand along the line and day". The findings in this research are present in MAIOR’s scheduling solutions.

Airline Crew Scheduling

MAIOR and the University of Pisa collaborate to solve the airline crew scheduling problem based on state-of-the-art methods. This research's topic is the "Research activity finalized to the development of algorithms for the assignment of the airline crews to the pairing, reserves, trainings, days off and vacations while respecting the rule set constraints and at the same time equally distributing the work load and satisfying the requests and preferences of the personnel". Publication: P. Cappanera, G. Gallo, “The Airline Crew Rostering Problem at Alitalia Express”, Technical Report, University of Pisa, 2001.

New functionality for traceability

Trace Manager is the new functionality of the MAIOR Suite that automatically records in background information relating to the creation, modification, and deletion of data, such as versions, scenarios, running times levels, lines, stops, timetables, blocks, driver duties, and many other elements. The Trace Manager allows your dispatchers to reconstruct the story of every single piece of data present on the system. For example, it is possible to understand when a certain object has been created or modified and by who.

New timetable design algorithm

The MAIOR Transit Scheduling Suite's new release version will bring available the new algorithm for timetable design. Thanks to this innovative tool, public transportation agencies and operators will benefit from optimized timetables for single or multiple lines using these inputs: Desired Service Headway Available Vehicle Fleet Targeted Passengers or Demand Number of Trips to be Performed

New vehicle and driver scheduling algorithm

The new embedded algorithm available in the latest MAIOR Transit Scheduling Suite version performs simultaneous vehicle blocking and run-cutting optimization. This new feature will allow transit agencies and operators to reduce costs associated with service operations, by generating completely optimized service scenarios, with vehicle blocks that better support driver duties, and vice-versa. This new integrated algorithm can be used by urban and regional service providers and has even better capabilities for hybrid or sub-urban services.

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