Online repository
Online repository
Embedded Files
Along with the publications derived from the project, which are all available under OpenAccess policy (see Publications), we collect on this page all the links to useful material developed during the corresponding research works:
M. Carbonera, M. Ciavotta, E. Messina (2025). Generative AI for traffic scenarios: a GCN-VAE model. In: Proceedings of IES2025 conference. Material
X. Chou, E. Messina, S.W. Wallace (2025). Solving Two-Stage Stochastic Programming Problems via Machine Learning. In: Nicosia, G., Ojha, V., Giesselbach, S., Pardalos, M.P., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD ACAIN 2024. Lecture Notes in Computer Science, vol 15508, 1-12. Material
X. Chou, L. Di Marco, E. Messina (2025). Overcoming Computational Challenges in Two-Stage Multi-path Traveling Salesman Problem via Neural Networks. In: Proceedings of the IES2025 Conference. Material
F. Maggioni, A. Spinelli (2025). A Novel Robust Optimization Model for Nonlinear Support Vector Machine. EJOR 322(1), 237-253. Material
L. Bertazzi, G. O. Chagas, L. C. Coelho, D. Laganà, F. Vocaturo (2025). Online algorithms for the multi-vehicle inventory-routing problem with real-time demands. TR:C 170, 104892. Material
A. Spinelli, F. Maggioni, T. R. P. Ramos, A. P. Barbosa-Póvoa, and D. Vigo (2024). A rolling horizon heuristic approach for a multi-stage stochastic waste collection problem. EJOR 323(1), 276-296. Material
A. Candelieri, X. Chou, F.A. Archetti, E. Messina (2024). Generating Informative Scenarios via Active Learning. In: M. Bruglieri et al. (eds.), Optimization in Green Sustainability and Ecological Transition, AIRO Springer Series, vol. 12, 299-310. Material
M. Carbonera, M. Ciavotta, E. Messina (2024). Variational Autoencoders and Generative Adversarial Networks for Multivariate Scenario Generation. DATA SCIENCE FOR TRANSPORTATION, 6(3). Material
M. Carbonera, M. Ciavotta, E. Messina (2023). Driving into Uncertainty: An Adversarial Generative Approach for Multivariate Scenario Generation. In: Proceedings - 2023 IEEE International Conference on Big Data. Material
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