Sessions Plénières :
Les conférences plénières seront données par les orateurs suivants :
Multiple Objective Applications in Safety, Sustainability and Renewable Energy Transition
This seminar presents recent and ongoing multiple objective applications arising from the fields of strategic and operational safety planning, sustainability and the strategic management of the renewable energy transition. These applications originate from, or are inspired by, collaborative work on projects including those funded under EU Horizon and UKRI programmes.
The first application details a renewable energy case study dealing with the renewable energy transition in China. A desired energy configuration in 2050 under different national scenarios is generated using extended network goal programming (ENGP). The balance between national level, different regions and a set of sustainability criteria is optimised. A multi-period stochastic generation expansion planning model is then formulated and solved in order to give the optimal temporal decisions in each region in order to achieve the 2050 optimal configuration given by the ENGP model.
The second case study involves the selection of a priority set of Arctic Innovation Needs for safety and security in the Arctic and North Atlantic. Collaboration with search and research (SAR) and Marine Environmental Response (MER) practitioners is first described. The resulting need classification scheme and its assessment by importance and difficulty is then detailed. A balanced knapsack goal programming model and its usage in deriving a priority set of innovation needs are shown. Some results in terms of SAR and MER policy and practice are detailed.
A third application involves the management of decisions to open or close gas stations at a time of heightened risk. Lives lost and economic, environmental and social sustainability impacts of an incident, as well as population inconvenience incurred when closing gas station(s) are considered as criteria, with the latter modelled by queuing theory. A lexicographic extended goal programming model is built to investigate strategies for gas station closures. The results on a case study from the city of Portsmouth, UK are given. Finally, an overview of ongoing projects is given and conclusions as to future theory and application of multiple criteria optimisation techniques are drawn.
Pr. Dylan F Jones
Centre for Operational Research and Decision Analytics, University of Portsmouth, UK
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Biographie. Pr. Dylan Jones is a Professor of Operational Research in the School of Mathematics and Physics, and was Director of the Centre for Operational Research and Logistics for the period 2014-2024. He holds a BSc (Hons) in Mathematics with Operational Research from the University of Southampton and a PhD in Operational Research from the University of Portsmouth. He has been involved in research, teaching, and course development in the field of Logistics and Operational Research for over twenty-five years. His main research area is the theory of decision-making in the presence of multiple, conflicting objectives.
Pr. Dylan Jones has a wide range of research interests, with specific applications in supply chain management and logistics, maritime logistics, healthcare, renewable energy, safety and sustainability. He regularly attends international conferences in both general Operational Research and specialised Multi-Criteria Decision Making. His research research has been or is supported by a range of funders including the European Union, UKRI, Lloyd's Register Foundation and the UK Royal Society. He has particular research connections in France, Spain, and Brazil. He's the author of over 80 publications in international journals and a Springer research book on goal programming.
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Modeling Fairness in Facility Location and Routing
Most combinatorial optimization problems evaluate solution quality by aggregating individual performance measures into a single metric. In facility location, for instance, allocation costs from clients to their nearest open facility are typically summarized as the average allocation cost. Similarly, in routing problems, optimization focuses on minimizing the total cost across all routes, often disregarding the individual quality of routes for each driver. Even in machine learning—such as supervised classification—the objective is to minimize the average misclassification error across all observations.
In this talk, we explore alternative quality measures that account for fairness or equity (e.g., min-max, range, Gini deviation, Hurwicz criterion), as well as robustness (e.g., conditional value at risk, k-sum). To address these objectives, we apply the discrete ordered operator, which provides a unified modeling framework capable of capturing all these quality measures and more. This stands in contrast to much of the existing literature, which typically develops specialized models and solution techniques tailored to each specific measure.
We provide a generic MIP approach to model these diverse objectives and solve them within a common optimization framework. For facility location problems, we show how the discrete ordered objective can be efficiently handled using Benders decomposition, significantly improving the performance of state-of-the-art MIP approaches. For routing problems, we leverage connections to bilevel optimization to incorporate fairness directly into route planning.
The talk is based on a joint work with M. Pozo, J. Puerto Albandoz and A. Torrejón
Pr. Ivana Ljubić
ESSEC Business School of Paris |
Biographie. Ivana Ljubić is Professor of Operations Research at the ESSEC Business School of Paris. She received her habilitation in Operations Research at the University of Vienna (2013), and she holds PhD degree in computer science from the Vienna University of Technology (2004). She worked for two years in a company dealing with portfolio optimization before continuing her university career at the University of Vienna where she was appointed until 2015. She was also Visiting Researcher/Professor at the following institutions: La Sapienza in Rome, University of Maryland, TU Berlin, TU Dortmund, University of Paris Dauphine, etc. As of September 2015, she is appointed at the ESSEC Business School of Paris.
She is member of the Editorial Advisory Board for the journals European Journal of Operational Research, Computers and Operations Research, and she is Associate Editor for the journals Operations Research, Transportation Science and Networks. She also served as guest-editor of journals: European Journal of Operational Research and Annals of Operations Research and she is a former chair of the INFORMS Telecommunication Section.
Research interests of Ivana Ljubić include network design problems, combinatorial optimization, optimization under uncertainty, bilevel optimization. She uses tools and methods of mixed integer (non)-linear programming for solving optimization problems with applications in telecommunications, transportation, logistics, network design, design of data and distribution networks, social networks or bioinformatics. She received PhD Fellowship of the Austrian Academy of Sciences (DOC Fellowship, 2003-2004), PhD award of the Austrian Society for Operations Research (2005), Hertha-Firnberg Post-Doc Fellowship of the Austrian Science Fund (2007-2010) and APART Fellowship of the Austrian Academy of Sciences (2011-2013).
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Quand la programmation par contraintes danse avec les flots
Dans cette présentation, nous examinerons comment la programmation par contraintes se base sur la résolution de problèmes de flots pour résoudre certains problèmes d'optimisation combinatoire comme le bin packing.
Nous commencerons par rappeler les principes de la programmation par contraintes (CP). Puis, nous expliquerons comment les flots sont utilisés dans les contraintes globales qui sont un des aspects les plus importants de la CP. Nous expliquerons ensuite comment le bin packing est efficament modéliser en CP à l'aide de problèmes d'affectation classiques entre élements et containeurs. Nous donnerons quelques modèles complexes conduisant à des résultats intéressants dans la pratique, notamment pour le problème du programme scolaire équilibré (BACP) et le problème d'allocation de réservoirs (TAP).
Pr. Jean-Charles Régin
Decision Intelligence chair, 3IA center, Université Côte d'Azur
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Biographie. Pr. Jean-Charles Réginis is an internationally recognized expert of Constraint Programming (CP). Innovation and ground-breaking research are a constancy in his career and his contributions are recognized internationally in the academic and non-academic worlds. He is one of the inventors of the global constraints in CP (i.e. algorithms to quickly eliminate incoherent values). His paper describing the All different global constraint is highly cited (> 1000 cit.). This constraint is now implementing in almost all CP solvers and routinely used in many applications by companies such as IBM, Oracle, Microsoft, Google or SAS. The cited article, published in 1994, received the "Classical Paper Award" in 2013 from the American Association on Artificial Intelligence: ''For ground-breaking contributions to constraint programming via the development of one of the first propagators for global constraints.''. In 2013, he received the Association for Constraint Programming Research Excellence Award ''in recognition of a program of seminal and outstanding scientific contributions to both the theory and practice of constraint programming''.
Pr. Jean-Charles Régin introduced operations research-based filtering algorithms in CP by integrating matching and flow algorithms in propagator algorithms. A relevant example is his paper about the Generalized Cardinality Constraints (~ 500 citations). He pioneered soft global constraints for dealing with over-constrained problems and proposed an efficient way for modelling such problems. He established statistics-based constraints. He also invented a new parallelism method for CP, the Embarrassingly Parallel Search, a nonintrusive and successful method that integrated into solvers as the new default method of parallelism.
He proposed several filtering algorithms for enforcing arc consistency for generic constraints (also called table constraint). In 2022, one of his papers on this subject received the Artificial Intelligence Journal classic paper award: "The hallmarks of this work are its elegance, simplicity, efficiency and impact". He has also developed new algorithms for performing operations on Multi-valued Decision Diagrams based on their graph structure and thereby gained several orders of magnitude in time. These algorithms open up new possibilities for research and enable certain problems to be modelled very differently. Interesting successes, notably with F. Pachet (Sony), have been achieved in automatic music generation. Recently, he has taken an interest in constrained text generation and has proposed an original method of neurosymbolic AI combining CP and LLMs.
Pr. Jean-Charles Régin obtained the "Decision Intelligence" chair at The 3IA center in Nice in 2019. This chair aims at designing explainable decision-making processes satisfying real world constraints in a multi-objective environment including incomplete, fuzzy or stochastic data.
His work nourished from the academic environment as well as from the several cooperation projects that he has been involved in and from the collaborations with the private sector (Microsoft, Google, Amadeus, Sony…). For twelve years, he managed a worldwide recognized R&D team that designed of one the best CP based solvers for solving combinatorial optimization problems. Beyond the public sector, his career is strongly linked to the private sector as he worked on many real-world applications for a variety of companies. During his career, he has been strongly involved in dissemination activities related to his field of research. In 2004, he created, organized and chaired the first international CP-AI-OR conference. In 2023, for the conference's 20th anniversary, he organized it again in Nice.
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