Conférences  >  Mathématiques  >  Optimisation, recherche opérationnelle

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

The seminar seeks to identify the most pressing needs for quantifying uncertainty in MO problems that arise in industry, along with the most promising theoretical and methodological developments that will lead to efficient algorithms for addressing the identified needs. Toward this end, the seminar aims to bring together academic and industry experts as well as early-career researchers from different research disciplines, including Evolutionary Multiobjective Optimization, Multiple Criteria Decision Making, and Stochastic/Simulation Optimization, to discuss and propose novel ideas related to modeling, theory, and novel applications of MO under uncertainty.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

The purpose of this Dagstuhl Seminar is to audit the current experience that exists in the field, pool it, and start to describe that experience in a way that goes beyond anecdote (although anecdotes can be useful) and moves us toward advice or recipes that actually work in practice.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

In addition to exploring new methodologies, the workshop will address key challenges such as the explainability and interpretability of decisions, data privacy, and the practical deployment of models using contextual information. While these challenges are well-recognized in the ML community, particularly with the rise of large language models, they remain underexplored in the Operations Research community. By fostering collaboration and knowledge exchange, the workshop aims to develop scalable models, promote better benchmarking practices, and expand the applicability of contextual optimization to a wider range of real-world problems.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Cost-effective monitoring of large transportation networks, combined with the collection of accurate and relevant data on transport, vehicle movements, and individual mobility through various technologies, is crucial for enhancing transportation service efficiency. As we have seen in the past decade, recent technological advancements have produced an enormous volume of data, which is invaluable for understanding, planning, and managing transport networks in large cities, while also addressing the limitations imposed by road network growth. The challenge of managing, analyzing, and interpreting the information collected is considerable. The objective of this workshop is to bring together statisticians and engineers, with the specific goal to share recent advances, identify pressing problems, spark productive collaborations, and ultimately advance the state of the art by developing methods for the statistical modeling of data in the field of transportation. We will foster these interactions through four theme-focused daily workshops on the following topics: network modeling, travel behavior and choice modeling, traffic flow modeling and accident and safety analysis.

Mathématiques appliquées (en général),    Transport et logistique

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Recent breakthroughs in autonomy, electrification, and shared mobility are transforming transportation by opening new avenues for integrated, multi-modal transit systems with potential to benefit society as a whole. This five-day Dagstuhl Seminar aims to bring together experts from computer science, operations research, engineering, social sciences, and public policy to tackle both the theoretical and practical challenges at the intersection of data-driven transit planning, algorithm design, and real-world implementation.

Isaac Newton Institute for Mathematical Sciences, Cambridge

Isaac Newton Institute for Mathematical Sciences, Cambridge

Isaac Newton Institute for Mathematical Sciences, Cambridge

Statistique, Théorie des jeux,    Mathématiques appliquées (en général)

Isaac Newton Institute for Mathematical Sciences, Cambridge