The main causes of overcrowding of Emergency Operating Rooms (EORs) are the growing demand for non-elective surgeries and the unprecedented medical staff shortage. The effects of overcrowding can unfortunately ripple across the hospital, resulting in increasing in-hospital mortality, longer length of stays and higher costs. DOSE project is motivated by this context, where it becomes crucial to rethink the operations engineering and optimization of EORs. The objective of DOSE is to develop a multidisciplinary data-driven approach for optimizing EOR patient care pathways. More specifically, we aim at 1) developing data-driven approaches to provide accurate and comprehensive modeling of EOR patient care pathways, including predictions for resource requirements and activity delays; 2) developing health outcome Key Performance Indicators (KPIs) with machine learning techniques and medical expertise, a Quality of Working Life model for the EOR staff with human and social sciences, and a health-economic approach for cost-effectiveness analyses; 3) deriving optimal scheduling policies for care pathway activities by accounting for the above KPIs, and integrating the results into a digital twin to support real-time decision making. DOSE will improve EOR-related care delivery by exploiting tools from Industry 4.0 such as big data, data-driven optimization, and digital twins. It will be carried out in close collaboration with the hospital La Pitié Salpêtrière, and using detailed data on EOR patients from Entrepôt de Données de Santé. The project is challenging given the high dimension and the inherent stochastic and dynamic nature of the related problems, and our ambition to center on benefits and impacts for patients and medical staff. DOSE aims at bringing multidisciplinary contributions to the literature of data analytics, stochastic optimization, simulation, medicine, health economy, and human and social sciences.

The proposed project aims to create an innovative suite of open-source model tools that contributes to harmonizing the European Union’s (EU) hydrogen (H2) strategy with international development plans. By integrating analytical economics approaches with techno-economic numerical modeling and cutting-edge machine learning methods, we will provide a dynamic, data-based set of tools that can be adapted to capture changing regulatory and market environments. The models will empower industry stakeholders and policymakers to navigate the complexities of the emerging H2 market and supply structures, providing a robust state-of-the-art platform for scenario analyses and efficient planning. By informing the decision-making at all levels, our models can play a crucial role in shaping the future of the H2 economy, driving sustainable growth, and mitigating economic, climate, and political risks faced by the participants of the H2 economy. The project is designed to accomplish three interconnected goals. First, it will help to align the views on the long-term dynamics of the emerging international H2 market, incorporating the role of carbon regulations for H2 imposed by some lead economies. Examining the future market interactions, the study will focus on H2-importing countries, including but not limited to Germany, France, Japan, China, and the U.S., and the emerging H2 exporters, such as the U.S., Australia, Canada, Chile, Brazil, Saudi Arabia, and African countries. Developing a coherent view on the long-term hydrogen trade is crucial for addressing security of supply concerns and mitigating physical and market risks in the EU’s hydrogen economy. Second, we propose a multi-faceted approach that combines the long-term perspective on intra-EU coordination of import diversification with stochastic modeling of short-term market dynamics. The research results will provide input to consistent infrastructure development planning which is required to enable growth of hydrogen demand, including the capacity of import ports and intra-EU pipeline capacity. It also will provide the information necessary to support the EU’s H2 economy participants in their H2 contract negotiations, pricing strategies, and infrastructure cost-sharing. Third, leveraging prior expertise of the project team in natural gas and power markets, MINDSET Clean H2 will become a platform for the efficient use of novel tools, such as AI algorithms, to navigate the complexities of the energy sector. The increasing demand for machine learning capabilities among companies underscores the need for a platform that can provide accurate data and projections to support business decisions. Collaborating with industrial partners, the project will develop a suite of tools that addresses relevant real-world challenges and goes beyond traditional academic research approaches. The proposed collaboration will bring together CentraleSupélec’s (CS) agility, DIW Berlin’s modeling capacities and TU Munich’s (TUM) strategic expertise to innovate in the hot topic of hydrogen market development. This fusion of approaches enriches the partnership and enables the development of innovative solutions that benefit from both the broad, analytical frameworks of the German side and the agile, targeted problem-solving techniques of the French. The shared common framework will facilitate seamless integration and constant communication, allowing all teams to participate with an equal share to the project. The cooperation will also benefit the education of young researchers that will be able to engage with stakeholders, to spend time with the project partners, and for whom topic-specific summer schools will be organized.

The space industry is a key sector for the functioning of crucial industrial systems as it provides communication, navigation, and Earth observation services. Today, the space industry is undergoing a fundamental transformation with the access to space becoming easier and cheaper and the cost of developing and deploying space systems significantly decreasing. New industrial actors are rapidly developing the future space production systems: building mega-constellations (tens of thousands) of satellites to provide global services such as Internet-of-Things (IoT), and developing in-orbit manufacturing facilities to produce new mechanical and pharmaceutical products. Together, these satellites and in-orbit manufacturing facilities constitute the future space production infrastructure. Today, this space infrastructure is operating in one of the harshest environments, subject to a myriad of uncertainties and is largely inaccessible for inspection, maintenance, refuelling, upgrade, or end-of-life disposal. The current space industry operates within a “one-off” paradigm, where the only way to recover a failed space system or to update its existing capability is to replace the faulty system with a new one, e.g., construct and launch a new replacement satellite. This results in extremely low flexibility, high costs and unsustainable space environment where disposed satellites become space debris . To address this problem, an alternative paradigm has recently emerged based on On-Orbit Servicing (OOS), which consists of the deployment of robotic On-orbit Servicing Vehicles (OSVs) in space that can provide maintenance, inspection, repair, refuelling, recovery and upgrade services to those satellites, significantly reducing their cost of operation, improving their flexibility to new demands and their resilience towards failure. The objective of the ReSuSpace project is to develop quantitative decision-aid tools grounded in advanced mathematical modelling, operations research, systems engineering, resilience and sustainability assessment, and artificial intelligence for the optimal planning and management of the lifecycle of the space industry of the future. Particularly, the aim is to develop modelling and solution methods for the optimal planning of robotic On-Orbit Servicing (OOS) systems to: (1) extend the lifecycle of space production systems (i.e. satellites) by providing life-extension services such as predictive maintenance; refuelling; upgrade and inspection that increases their resilience towards failure; and (2) ensure the industry’s sustainability by assisting with active debris removal; satellites collision avoidance to prevent the creation of new debris; and end-of-life de-orbiting of inactive satellites.

The objective is to design a biodegradable packaging to preserve fruits and vegetables along the postharvest chain until consumption. Several issues related to this technology will be studied: control of heat/moisture transfer and airflow within the pallet, which exchanges with the modified atmosphere of packaging, consumer acceptance, logistic cost and environmental impact. A multidisciplinary approach including field practices, consumer behaviour, packaging technology and packing, product quality assessment and shelf life determination, fluid-dynamic, heat and mass transfer, refrigeration equipment, logistics organisation cost and life cycle assessment will be implemented to optimize the post-harvest chain in terms of quality, cost and environmental impact. The approach developed in this project will be validated for salad (local food) and for strawberry (long distance food) and will be generalizable to the whole fruits and vegetables sector.