{"id":39472,"date":"2025-07-31T00:00:00","date_gmt":"2025-07-30T22:00:00","guid":{"rendered":"https:\/\/optit.sviluppo.host\/2025\/07\/31\/hybrid-ai-in-action-mila-project-nears-completion-with-promising-results\/"},"modified":"2026-06-16T08:53:14","modified_gmt":"2026-06-16T06:53:14","slug":"hybrid-ai-in-action-mila-project-nears-completion-with-promising-results","status":"publish","type":"post","link":"https:\/\/optit.sviluppo.host\/en\/news\/hybrid-ai-in-action-mila-project-nears-completion-with-promising-results\/","title":{"rendered":"Hybrid AI in action: MILA project nears completion with promising results"},"content":{"rendered":"<p>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 <strong>the project is delivering on its ambitious goals<\/strong>.<\/p>\n<p>Coordinated by Optit under the <a href=\"https:\/\/fondazione-fair.it\/en\/project\/\">FAIR programme<\/a> \u2013 Spoke 8 \u201cPervasive AI\u201d \u2013 the project focuses on <strong>hybrid decision-support models<\/strong> in the <strong>Logistics<\/strong> and <strong>Environmental <\/strong>sectors.<\/p>\n<p>The positive feedback received validates the quality and consistency of the work carried out, highlighting the project\u2019s contribution to both <strong>scientific advancement <\/strong>and <strong>real-world applications<\/strong>.<\/p>\n<p>Since the beginning of 2025, the project has moved from feasibility analysis to the <strong>design<\/strong>, <strong>prototyping<\/strong>, and <strong>partial integration<\/strong> of hybrid models that c<strong>ombine symbolic methods with machine learning<\/strong> techniques.<\/p>\n<p>Notable developments include a <strong>prototype<\/strong> for <strong>correcting and inferring master data<\/strong> for routing algorithms, a <strong>demonstrator for GPS-based road speed estimation<\/strong>, and <strong>forecasting modules<\/strong> capable of handling anomalies and missing values in time series. These efforts have already led to the integration of new features in <a href=\"\/?page_id=27875\">Optit\u2019s dispatching platform<\/a> and the creation of demonstrators for workforce planning and load optimization.<\/p>\n<p>We also presented a joint scientific paper with the <a href=\"https:\/\/www.unimi.it\/en\">University of Milan<\/a> at <a href=\"https:\/\/isc-hpc.com\/\">ISC 2025<\/a>, one of the leading international conferences on high-performance computing and its applications.<\/p>\n<p>The paper focuses on the use of <strong>LSTM models<\/strong> to learn decision-makers\u2019 preferences in routing, bringing hybrid AI closer to real operational needs.<\/p>\n<h4><b><\/b><\/h4>\n<h4><b>Real-world impact: use cases in Logistics and Waste management<\/b><\/h4>\n<p>Concrete use cases explored during the project underscore the potential of <strong>hybrid AI<\/strong> in <strong>improving operational decisions<\/strong>.<\/p>\n<p>In Retail Logistics, we developed <strong>machine learning models<\/strong> to automatically <strong>infer delivery time windows and vehicle compatibility<\/strong> for new customer locations. In the load building domain, a <strong>smart parameter tuning method<\/strong> was applied to <strong>metaheuristic algorithms<\/strong>, resulting in a <strong>3.5x speed-up<\/strong> and better optimization results within fixed time budgets. In the Waste sector, we tested <strong>hybrid clustering approaches<\/strong> to divide urban areas into efficient collection zones, and we designed <strong>solutions to enhance road network modelling<\/strong> based on GPS data.<\/p>\n<p>These case studies demonstrate that <strong>hybrid AI<\/strong> is not just a promising research direction, but a <strong>powerful enabler of smarter, more efficient, and more sustainable operations<\/strong>.<\/p>\n<p>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.<\/p>\n<p>&nbsp;<\/p>\n<p><i>The MILA project has been funded under the FAIR programme (code PE00000013), as part of Italy\u2019s National Recovery and Resilience Plan (PNRR), with funding from the European Union \u2013 NextGenerationEU. <\/i><\/p>\n","protected":false},"excerpt":{"rendered":"<p>From concept to prototype: what we\u2019ve built so far<\/p>\n","protected":false},"author":2,"featured_media":39473,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[862],"tags":[],"class_list":["post-39472","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Hybrid AI in action: MILA project nears completion with promising results | OPTIT<\/title>\n<meta name=\"description\" content=\"From concept to prototype: what we\u2019ve built so far\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta 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