Plenary & Keynote Speakers

Abstract: This presentation will highlight how decision science can power decision making on the journey from designing a global ocean transportation network serving 100.000+ customers across 130 countries to the intricate orchestration of schedules for 700+ vessels to transport our customers goods and ensure availability of equipment to enhance our customers supply chains all the way. At every phase decision science can play a pivotal role in increasing consistency and quality of decision making. We will also delve into the orchestration of problem solving between the different phases.
The presentation will emphasize how the implementation of models and algorithms is really just ten cents a penny – successful implementation of decision science necessitates harmonizing with organizational dynamics, processes, and technological frameworks. We'll explore the evolution of decision science products at Maersk emphasizing the criticality of obsessing about customer and business outcomes through performance and process metrics needed to sustain the vitality and health of decision science products.

Bio: Berit Dangaard Brouer has a long academic and practical experience in the design and implementation of solutions to large scale optimization problem for the maritime industry. After working for IBM and Ange Optimization on optimization algorithms for container liner shipping, she successfully completed a PhD in operations research at the Technical University of Denmark (DTU), where she has been studying algorithms to solve container liner network problems. Within the maritime OR community, she is especially known for her work on the development of the LinerLib benchmark and related solution methods. The benchmark has been the inspiration for much other maritime related research.

Berit Dangaard Brouer continued her carrier as a practitioner of OR within the maritime optimization, first at Optivation and now at Maersk. Based on her extensive knowledge of the maritime sector and of optimization research, I personally look very much forward hearing her talk.

Abstract: What does well-being have to do with operations? Well-being encompasses a lot: Are we happy as individuals? Are groups treated fairly? Is society sustainable? Operations management has many impacts on well-being at each of these levels, some more obvious than others. This talk will offer a wide-ranging exploration of linkages between operations and well-being. It organizes operations into five broad areas: pace and productivity, predictability and probability, process and prevention, performance and payment, and pollution and protection. For each of those, it explores what makes individuals (un)happy, what is fair, and what is sustainable. Operations cannot solve all societal problems but the links between quality of operations and quality of life are more numerous and nuanced than we usually realize.

Bio: Charles Corbett, Ph.D., is the IBM Chair in Management and Professor of Operations Management and Sustainability at the UCLA Anderson School of Management; he holds a joint appointment at the UCLA Institute of the Environment and Sustainability. He served as Chairman and Deputy Dean of Academic Affairs from 2009‐2012, and previously as Associate Dean of the MBA program. He has received the Neidorf “Decade” Teaching Award, Citibank Teaching Award, the Executive MBA Class of 2006 Outstanding Teaching Award and the Robbins Assistant Professor teaching award, in addition to the UCLA Staff Assembly’s Faculty/Staff Partnership Award and the Anderson School’s J. Clayburn LaForce Faculty Leadership Award. He was an AT&T Faculty Fellow in Industrial Ecology. He founded and co‐directed the award‐winning UCLA Leaders in Sustainability graduate certificate program and the Easton Technology Leadership Program.

His areas of teaching include sustainable operations and supply chains, time management and well‐being, and operations of entrepreneurs and small business,. He has given (semi‐)plenary and keynote lectures at conferences in Amsterdam, Bali, Istanbul, Lima, Mexico City, Montreal, Paris, Sao Paulo, Salvador (Brazil), Shanghai, Tainan (Taiwan), and Wrocław (Poland).

His current research focuses on sustainable operations and on time management and well‐being. He has published in leading academic and business journals, including Sloan Management Review, California Management Review, Operations Research, Management Science, Manufacturing & Service Operations Management, European Journal of Operational Research, the Journal of the Operational Research Society, Production and Operations Management, Energy, Environmental and Resource Economics, Journal of Industrial Ecology, and others. Dr. Corbett served as Editor‐in‐Chief of Foundations and Trends in Technology, Information and Operations Management; he has been guest editor of three special issues of Production and Operations Management on Environmental Management & Operations and has held various editorial positions at other leading journals including Management Science, POM and the Journal of Industrial Ecology. His 2006 study on sustainability in the motion picture industry was featured in media outlets worldwide, including CNN, the Los Angeles Times, the New York Times, The Guardian, La Opinion, and various radio and TV stations. He was elected a lifetime Fellow of the Production and Operations Management Society in 2013 and of the Manufacturing and Service Operations Management Society in 2019.

Professor Corbett holds a Ph.D. in Production and Operations Management from INSEAD in Fontainebleau, France, and a Drs. in Operations Research from Erasmus University Rotterdam (Netherlands).

Abstract: The fusion of machine learning and optimization has the potential to deliver outcomes for engineering applications that the two technologies cannot achieve independently. This talk illustrates this potential with the concept of optimization proxy that can produce, in real time, feasible and near-optimal solutions to classes of optimization problems. The talk reviews the theoretical foundations underlying optimization proxies and demonstrates its practical practical on applications in power systems, supply chains, and mobility.

Bio: Pascal Van Hentenryck is an A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Prior to this appointment, he was a professor of Computer Science at Brown University for about 20 years, he led the optimization research group (about 70 people) at National ICT Australia (NICTA) (until its merger with CSIRO), and was the Seth Bonder Collegiate Professor of Engineering at the University of Michigan. Van Hentenryck is also an Honorary Professor at the Australian National University.

Van Hentenryck is a Fellow of AAAI (the Association for the Advancement of Artificial Intelligence) and INFORMS (the Institute for Operations Research and Management Science). He has been awarded two honorary doctoral degrees from the University of Louvain and the university of Nantes, the IFORS Distinguished Lecturer Award, the Philip J. Bray Award for teaching excellence in the physical sciences at Brown University, the ACP Award for Research Excellence in Constraint Programming, the ICS INFORMS Prize for Research Excellence at the Intersection of Computer Science and Operations Research, and an NSF National Young Investigator Award. He received a Test of Time Award (20 years) from the Association of Logic Programming and numerous best paper awards, including at IJCAI and AAAI. Van Hentenryck has given plenary/semi-plenary talks at the International Joint Conference on Artificial Intelligence (twice), the International Symposium on Mathematical Programming, the SIAM Optimization Conference, the Annual INFORMS Conference, NIPS, and many other conferences. Van Hentenryck is program co-chair of the AAAI’19 conference, a premier conference in Artificial Intelligence.

Van Hentenryck’s research focuses in Artificial Intelligence, Data Science, and Operations Research. His current focus is to develop methodologies, algorithms, and systems for addressing challenging problems in mobility, energy systems, resilience, and privacy. In the past, his research focused on optimization and the design and implementation of innovative optimization systems, including the CHIP programming system (a Cosytec product), the foundation of all modern constraint programming systems and the optimization programming language OPL (now an IBM Product). Van Hentenryck has also worked on computational biology, numerical analysis, and programming languages, publishing in premier journals in these areas.

Van Hentenryck runs the Seth Bonder summer Camp in Computational and Data Science for middle- and high-school students every summer.

Abstract: We move towards an era of smart cities and factories, where autonomous vehicles will provide on-demand transportation while making our streets safer, and mobile robots will automate processes in coexistence with humans. These applications require novel methods for real-time large-scale routing of thousands of vehicles and multi-objective task assignment. In this talk and building upon our seminal work on on-demand high-capacity ridesharing via dynamic trip-vehicle assignment, I will give an overview of our recent work in this field. I will first analyze some of the sources of uncertainty present in ride pooling systems, followed by a discussion of methods for predictive routing that utilize a model of future demand, how these systems could be combined with public transit, and how an equilibrium can be achieved. Secondly, I will discuss an extension of these methods for flash deliveries and a multi-objective optimization that enables us to design the required vehicle fleet accounting for the trade-off between quality of service and operation cost. Finally, and looking into a multi-robot task pickup and delivery problem, I will discuss how we can compute statistically distinct plans for multi-objective task assignment, which could later be employed by a user to select the desired operational point of the system.

Bio: Javier Alonso-Mora is an Associate Professor at the Cognitive Robotics department of the Delft University of Technology, where he leads the Autonomous Multi-robots Laboratory. He is a Principal Investigator at the Amsterdam Institute for Advanced Metropolitan Solutions and co-founder of The Routing Company. He is actively involved in the Delft robotics ecosystem, including the Robotics Institute, the Transportation Institute and Robovalley.
Before joining TU Delft, Dr. Alonso-Mora was a Postdoctoral Associate at the Computer Science and Artificial Intelligence Lab of the Massachusetts Institute of Technology. He received his Ph.D. degree in robotics from ETH Zurich, where he worked in the Autonomous Systems Lab, and in partnership with Disney Research Zurich. Dr. Alonso-Mora holds a Diploma in Engineering and a Diploma in Mathematics from the Technical University of Barcelona, where he was part of the Interdisciplinary Higher Education Centre and the Robotics Institute.
His main research interest is in navigation, motion planning and control of autonomous mobile robots, with a special emphasis on multi-robot systems, on-demand transportation and robots that interact with other robots and humans in dynamic and uncertain environments. He is the recipient of multiple prizes and grants, including the ICRA Best Paper Award on Multi-robot Systems (2019), an Amazon Research Award (2019) and a talent scheme VENI award from the Netherlands Organisation for Scientific Research (2017).

Abstract: Retail is often the last supply chain node a product/service ‘visits’ before reaching the end customer. As a result, customers’ behavior towards different incentives is paramount to managing retail operations, which combine elements of customer relationship management, supply chain and inventory planning, product distribution and logistics, pricing, and store management. Consequently, we must acknowledge that it may be hard to adhere to the common assumptions of operations research in this context -- that all relevant variables and constraints can be identified and accounted for to achieve the ideal outcome. In this talk, we’ll give a framework and examples for conducting empirically driven operations research to improve retail operations. The “empirically driven” approach has two main components: find an empirical justification of model assumptions and parameters and perform an empirical assessment of model results and insights. .

Bio: Master of Science and Doctor of Philosophy in Industrial Engineering and Management by FEUP.

Head of the Research Center for Industrial Engineering and Management from INESC TEC Laboratório Associado.

Co Founder of LTPlabs - consultancy company that applies advanced analytical methods to help make better complex decisions.

Specialist in supply chain planning with an emphasis on food products. He was Supply Chain Analyst at Total Raffinage Marketing (França). Researcher/Consultant in several projects related to Operations Management and supported by different types of entities.

Author of several publications in international journals in the field of Operations Research (for example, International Journal of Production Economics, Industrial Engineering and Chemistry Research, Computers and Chemical Engineering, Interfaces).

Abstract: In the last decades, the impact of natural and manmade disasters has been increasing and so does the research in the field of humanitarian logistics. But what are the challenges that humanitarian logistics face in practice and where is the research going? Is there an alignment between research and practice or a gap?
By reviewing papers on humanitarian operations, problems raised by practitioner conferences and webinars, and conducting a survey of the author community on the topic, we link these problems with the literature developed in our field. We also identify characteristics of papers that close the gap between practice and research. We then go deeper into some research projects that have affected practice helping it to become efficient, effective, and sustainable.

Bio: Maria Besiou is Dean of Research and Professor of Humanitarian Logistics at Kühne Logistics University (KLU), Hamburg, Germany. She received her Ph.D. in Mechanical Engineering and Operations Management from Aristotle University of Thessaloniki (AUTH) in Greece. Before joining KLU she worked as a Postdoctoral Research Fellow at INSEAD, France.

Besiou’s specialization is on the field of sustainable supply chain management. Her main research interests are in humanitarian logistics, and closed-loop supply chains. Her research focuses on real problems that commercial companies and humanitarian organizations face and their decision-making, while managing their supply chains.

Prof. Besiou serves as the Academic Director of the Center of Humanitarian Logistics and Regional Development (CHORD). Her research appears in several case studies, award-winning papers and peer-review international journals. Prof. Besiou serves as an Associate Editor in the Department of Humanitarian Operations in Decision Sciences Journal and the Journal of Humanitarian Logistics and Supply Chain Management (JHLSCM), as a Senior Editor of the Disaster Management Department (DMD) of Production and Operations Management (POM), and on the editorial review boards of Journal of Operations Management (JOM) and Production and Operations Management (POM).

She has served as a Coordinator of EURO Hope (European Working Group on Humanitarian Operations) and as the President of the College of Humanitarian Operations and Crisis Management (HOCM) at the Production and Operations Management Society (POMS). In 2022 she received the American Logistics and Aid Network's (ALAN) Research and Academic Contribution Award for her work researching and teaching humanitarian logistics.

Abstract: In this talk we survey some recent developments on the OR aspects of kidney exchange programmes (KEPs). These programmes have been established in most of the Western countries in the last two decades to facilitate the exchange of living kidney donors for those recipients who have willing but immunologically incompatible donors. In the first part, we describe the European practices including the hierarchic optimisation used in the matching runs of the KEPs for computing optimal solutions. These IP-based methods have been implemented in the simulator software developed in the ENCKEP COST Action (2016-2021) and subsequently incorporated in the KEPSOFT software that provides a common IT-platform for European applications. In the second part, we describe an alternative solution concept based on the individual fairness notion of stability. We explain how this classical cooperative game theoretical concept can incentivise the recipients to register valuable donors or multiple donors in KEPs. Finally, in the third part, we present new results on international KEPs, where credit-based compensation schemes have been proposed to balance out the benefits of the countries when merging their national pools. .

Bio: Péter Biró graduated with an MSc in Mathematics at Budapest University of Technology and Economics in 2003, and then received his PhD in Mathematics and Computer Science at the same institute in 2007. During this period, he also studied economics for five years at Corvinus University of Budapest, graduating with MSc in 2007. Afterwards, he was a postdoctoral fellow at the Computer Science Department, University of Glasgow for three and a half years. In 2010 he moved back to Budapest, and since then he has a research position at the Institute of Economics, KRTK, Hungarian Academy of Sciences, which was later renamed to Eötvös Loránd Research Network. From 2010 to 2016 he worked in the Game Theory research group, and in 2016 he established his own research group on Mechanism Design, funded by the Momentum Grant of the Hungarian Academy of Sciences. He also teaches part time at the OR Department, Corvinus University of Budapest since 2013. He was on sabbatical from the Budapest positions twice, first in 2014 as a visiting professor at the Economics Department, Stanford University invited be Al Roth for a year, and then in 2023, when he co-organised and participated in a 4-month program at Simons Laufer Mathematical Sciences Institute, Berkeley, USA. His research interest lies in the interdisciplinary fields of Market Design, Engineering Economics, and in particular matching problems under preferences. In his research he uses multiple approaches including game theory, algorithm theory, graph theory, and optimisation. Besides the theoretical studies, he has been involved in several practical applications, such as the UK kidney exchange programme, the Scottish resident allocation scheme, and the Hungarian higher education admission scheme.

Abstract:

In some ML communities, researchers claim that obtaining local solutions of optimality criteria is often sufficient to provide a meaningful and accurate data model in real-world analytics. However, this is simply incorrect and sometimes dangerously misleading, particularly when it comes to highly structured problems involving non-convexity such as discrete decisions (binary variables). This talk will advocate the necessity of research efforts in the quest for global solutions and strong rigorous bounds for quality guarantees, showcased on one of the nowadays most popular domains -- cardinality-constrained models. These models try to achieve fairness, transparency and explainability in AI applications, ranging from Math.Finance/Economics to social and life sciences.

From a computational viewpoint, it may be tempting to replace the zero-norm (number of nonzero variables) with surrogates, for the benefit of tractability. We argue that these relaxations come too early. Instead, we propose to incorporate the true zero-norm into the base model and treat this either by MILP relaxations or else by lifting to tractable conic optimization models. Both in practice and in theory, these have proved to achieve much stronger bounds than the usual LP-based ones, and therefore they may, more reliably and based upon exact arguments, assess the quality of proposals coming from other techniques in a more precise way. With some effort invested in the theory (aka later relaxations), the resulting models are still scalable and would guarantee computational performance closer to reality and/or optimality.

Bio: Immanuel M. Bomze was born in Vienna, Austria, in 1958. He received the degree Magister rerum naturalium in Mathematics at the University of Vienna in 1981. After a postgraduate scholarship at the Institute for Advanced Studies, Vienna from 1981 to 1982, he received the degree Doctor rerum naturalium (Ph.D.) in Mathematics at the University of Vienna. After his Habilitation 1987, he held several visiting research positions at various research institutions across Europe, the Americas, Asia and Australia. He also gained some practical Operations Research experience during his work as a research mathematician in the Business & Marketing Research/Operations Research group of the national incumbent telecommunication operator Telekom Austria 2002-2004. Since 2004, he holds a chair (full professor) of Applied Mathematics and Statistics at the University of Vienna.

Bomze’s research interests are in the areas of nonlinear optimization, qualitative theory of dynamical systems, game theory, mathematical modelling and statistics, where he has edited one and published four books, as well as over 120 peer-reviewed articles in scientific journals and monographs. The list of his co-authors comprises over ninety scientists from more than a dozen countries in four continents. In 2014 he was elected Fellow of EurOpt, the Continuous Optimization Working Group of EURO, the Association of European Operational Research Societies.

As a member of program and/or organizing committees, he co-organized various scientific events and he is an Associate Editor for five international journals. For several science foundations and councils (based in Canada, the Czech Republic, Germany, Great Britain, Hong Kong, Israel, Italy, the Netherlands, Norway, Portugal, Singapore, Spain, USA), and for almost 50 scientific journals he acted as a reporting referee. 2011–2017 he served as an Editor (Co-EiC) of the European Journal of Operational Research, and since 2021 he serves as sole EiC of the EURO Journal of Computational Optimization. Bomze co-founded the Vienna Center of Operations Research (VCOR) and serves as its co-director. As elected president of EURO, he served 2018-2020 and, as Past President, in the EURO Executive Committee until end of 2021.

Abstract: Decision makers are often faced with problems that are subject to uncertainty. Consider the problem of planning transport services for an upcoming season, determining optimal locations of new infrastructure, or establishing production plans and pricing strategies for a product. In this context, demand uncertainty is challenging to deal with, notably because it is decision-dependent. In this talk, we discuss data and modeling challenges associated with understanding and predicting demand. Focusing on the competitive facility location problem, we describe a methodology to deal with decision-dependent demand uncertainty without making strong distributional assumptions. We also provide a high-level overview of contextual stochastic optimization. Studied in the literature under a variety of names, contextual optimization refers to data-driven approaches to prescribe decisions by exploiting relevant side information. We position demand modeling for decision making in this context and outline future research directions on integrated learning and optimization.

Bio: Emma Frejinger is a professor in the Department of Computer Science and Operations Research at Université de Montréal where she holds a Canada Research Chair and an industrial chair funded by the Canadian National Railway Company. Her research is application-driven and focuses on innovative combinations of methodologies from machine learning and operations research to solve large-scale decision-making problems. Emma has extensive experience working with industry, particularly within the transportation sector, where she has led collaborative research projects. Since 2018, she also works as a scientific advisor for IVADO Labs developing AI solutions for the supply chain industry. Before joining Université de Montréal in 2013, Emma was a faculty member at KTH Royal Institute of Technology in Sweden. She holds a Ph.D. in mathematics from EPFL (Switzerland).

Abstract: There have been decades of research in the field of humanitarian logistics, and academics in the field of logistics are becoming more and more interested in it. That being said, we still acquire insight and identify new problems with each disaster. We have also observed various humanitarian logistics applications during Covid-19, e.g. for PCR test sites and vaccination centers. Unfortunately, the recent earthquake in Turkey has led us to re-evaluate the response cycle of disaster management. Close inspection reveals that this response phase actually leads to a variety of new applications of distribution logistics problems. We have conducted many meetings and workshops with municipalities that were very active during the response of the Maraş Earthquakes. Many municipalities aim to have “earthquake resistant cities” with correct action plans and being ready for potential disasters. Based on our discussions with these municipalities, we have developed an “ideal action plan”. We also investigated the potential decision problems and linked these problems with OR literature. .

Bio: Bahar Yetis Kara is a Professor in the Department of Industrial Engineering at Bilkent University where she has been a faculty member since 2001. Dr. Kara holds an M.S. and Ph.D. degree from Bilkent University Industrial Engi- neering Department, and she worked as a Post- doctoral Researcher at McGill University in Canada.
Dr Kara’s current research interests include dis- tribution logistics, humanitarian logistics, hub location and hub network design. Dr. Kara is one of the founders and a member of the executive board of the EURO Working Group on Humani- tarian Operations (HOpe). She is also a member of the executive board of Bilkent University and
of Turkish OR Society. Dr. Kara editored 4 books and authored/co-authored more than 80 journal and conference papers.
Dr. Kara holds “Best Dissertation Award” given by INFORMS-UPS-SOLA (2001), TUBA-GEBIP (National Young Researchers Career Development Grant) reward (2008), IAP Young Researchers Award (2009), and TÜBİTAK Young Scientist Incentive Award (2010). She is currently working as an as- sociate editor of Transportation Research- Part B, Journal of Operational Re- search Society, IIE Transactions, and Socio Economic Planning and Science.

Abstract: Stochastic dominance is a statistical tool developed for comparing the random variables among each other. In financial applications, these random variables usually represent random returns of the considered assets or portfolios. The paper focuses on portfolio selection problems with stochastic dominance constraints for various orders of stochastic dominance relations. Firstly, the tractable necessary and sufficient conditions for particular probability distributions are discussed.
Secondly, these conditions are employed in the static and dynamic portfolio selection problems. Finally, the extensions for the case of vector comparisons are presented. The theoretical results are accompanied by empirical examples.

Bio: Miloš Kopa is an associate professor at Charles University in Prague, chair of Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics and Director of Financial Mathematics study programme. He received Ph.D. degree in Mathematics (specialization: Econometrics and Operations research) in 2006.

He is the secretary (and former chair) of EURO working group on stochastic optimization. He is an active member of several other scientific international societies: Stochastic Programming Community, EURO working group on commodities and financial modelling, EUROPT.

In Czech Republic, he is the president of the Czech Society for Operations Research, a member of expert group for Mathematics and Economics of the Czech National Accreditation Authority and the former chair of expert committee for Finance and Operations research (panel 403) of the Czech Science Foundation

The research of Miloš Kopa is focused on: stochastic programming theory and applications, especially financial applications. In recent years he has published several papers dealing with portfolio efficiency; risk measures; stochastic dominance criteria; data envelopment analysis and its relation to stochastic dominance; robustness (contamination) in stochastic programs; ALM management; Decision dependent randomness problems, etc.

Abstract: This presentation highlights the interplay between optimization and machine learning, examining two pivotal examples.
First, we will explore how mathematical optimization can significantly enhance the interpretability of machine learning models, with a focus on classification problems. Interpretability is a crucial requirement for the practical deployment of machine learning models across diverse fields.
Optimal decision trees are fundamental tools for building robust and interpretable classification models. However, the challenge lies in their inability to ensure optimality for large datasets. Recently some efficient approaches have been introduced for building optimal decision trees that require all the features to be binary. We will describe an efficient optimization-based approach for deriving a supervised discretization of the original dataset. Thanks to the use of optimization, the proposed technique allows to control the importance of the features extracted, and hence the granularity of the discretization. This novel technique not only facilitates the training of a robust and interpretable optimal decision tree but also allows to address the complexities associated with larger datasets.
Finally, we'll transition to a pragmatic application in process engineering, showcasing the successful integration of optimization and machine learning to solve an important real-world problem.

Bio: Veronica Piccialli is Full Professor in Operations Research at DIAG Sapienza University of Rome since September 2021. Before she was first Assistant Professor and then Associate Professor at University of Rome Tor Vergata. Since 2019 she is Associate Editor in the area "Design and Analysis of Algorithms: Continuous" for INFORMS Journal on Computing, and since 2021 she is Associate Editor of EURO Journal on Computational Optimization.
Her research interests are: Semidefinite Programming, Mixed Integer Nonlinear Programming, Machine Learning and Optimization, Interpretability of Machine Learning Algorithms, Global Optimization. Since 2015 she is collaborating with LRGP and LORIA at Universitè de Lorraine on Process Engineering Optimization. . 

Abstract: The surprising fact that EURO24, the 33rd EURO conference, is the first hosted by Denmark, provides the opportunity to give a personal reflection on the Danish contribution to OR history. There will be an emphasis on four individuals who have played a significant role in the development of OR theory and professional infrastructure. Three of these individuals will be revealed at the lecture. For now, it is sufficient to say that the lecture is in honour of my dear friend Jakob Krarup, who passed away last year. He was greatly looking forward to the conference being held in his own city. 

From this description it will be obvious that this will not be a standard keynote address. However, there may be one, or three, equations included. That is a clue to one of the other three individuals whose contribution will be noted.

Bio: Graham Rand retired in 2016 after 43 years in Operational Research at Lancaster University, UK. The Operational Research Society made him a Companion of Operational Research in 2006, and he became a Fellow of the International Federation of Operational Research Societies in 2021.

He served a term as Council Member and was twice Conference Chairman of the British Operational Research Society. He edited the Journal of the Operational Research Society from 1991-1996, and from 2014-2022 the Society’s magazine, Impact. For the International Federation of Operational Research Societies (IFORS) he was Vice-President (1998-2000) and was editor of both International Abstracts in Operations Research (1980-1991) and International Transactions in Operational Research (2000-05). He was editor of the proceedings of the IFORS Conference held in Buenos Aires in 1987, and Chairman of the Program Committee for the IFORS Conference held in Athens in 1990. For the European Association of Operational Research Societies he was chairman of the Gold Medal jury in 1995. He was a founder member of both the International Society for Inventory Research (1983) and the Operations Management Association (1985). He was President of Omega Rho, the international honor society for Operational Research in 2012-2014.

Abstract: A significant portion of the effort within the field of Operations Research is dedicated to the development of intricate ad-hoc algorithms and models tailored for addressing diverse optimization problems. Frequently, the research community exhibits a preference for complexity in the proposed methodologies. Notably, an esteemed characteristic sought in these methods is the incorporation of problem-specific knowledge, enabling the attainment of results that get as close as possible to optimality.

In academic publishing, the pursuit of marginal gains in optimality gaps is a common goal, even at the expense of introducing additional complexity to models or algorithms. While there is consensus that such endeavors contribute to advancements in algorithms and propel the field of Operations Research forward, a frequently underestimated dimension is the practical applicability of these advancements in industrial settings.

Real industrial problems are often messy with imprecise (or numerous) objectives, soft constraints, preferences and a myriad of situations that demand pragmatic approaches. Additionally, these problems undergo rapid evolution with frequent model refinements, occurring often fortnightly.
Here, the ongoing painful adaptation of intricate code bases stemming from ad-hoc methods is not the favored choice. In contrast, solvers offer greater flexibility, allowing faster implementation of new constraints through their APIs than altering tailored algorithms. In industrial contexts, the preference lies in flexibility, maintainability, robustness, and reduced operational load, where a modest optimality gap is deemed a minor trade-off.

Moreover, in the realm of large-scale real-world problems, mathematical solvers often prove impractical. It is commonplace to resort to simplifying the problem for solvability. This practice raises a critical question: Is it logical to insist on chasing optimality in solving a simplified problem with a reduced real-world fidelity? Furthermore, real problems entail vast datasets, often including forecasted or approximated input data. Does it make sense to go great lengths to optimally solve a problem when a portion of the input data involves approximations?

In this presentation, I advocate for relinquishing the pursuit of optimality and, instead, embracing heuristic solvers and simplified modeling approaches. Such strategies yield expedient, adaptable, maintainable, and easily implementable models. The discourse will draw upon various examples, spanning classical routing and scheduling problems, culminating in intricate real-world virtual machine placement bin packing problems encountered at Amazon Elastic Compute Cloud (EC2).

Bio: Ruben Ruiz is Principal Applied Scientist at Amazon Web Services (AWS) and Full Professor of Statistics and Operations Research on leave of absence at the Polytechnic University of Valencia, Spain. He is co-author of more than 100 papers in International Journals and has participated in presentations of more than two hundred papers in national and international conferences. He is editor of the Elsevier’s JCR-listed journal Operations Research Perspectives (ORP) and co-editor of the JCR-listed journal European Journal of Industrial Engineering (EJIE). He is also associate editor of other journals like TOP and member of the editorial boards of several journals most notably European Journal of Operational Research and Computers and Operations Research. His research interests include scheduling and routing in real life scenarios as well as cloud computing scheduling.

Abstract: Decision Support Systems (DSS) were born in the 70's. Since this decade, we can find in the literature several tracks of evolution for these systems. We will draw the evolutions of DSS: describing the architecture, the usability, the functionalities, the programming technics as well as the way to use these systems. We will show how Artificial Intelligence and Machine Learning influenced the development of DSSs.
Different projects will be presented related to DSSs.

Bio: Pascale Zaraté is a Professor at Toulouse Capitole University. She conducts her research at the IRIT laboratory. She holds a Ph.D. in Computer Sciences / Decision Support from the LAMSADE laboratory at the Paris Dauphine University, Paris (1991). Pascale Zaraté’s current research interests include Decision Support Systems, Group Decision Support Systems, Recommender systems She published several manuscripts: 3 books, edited 6 books, edited 18 special issues in several international journals, 11 proceedings of international conferences, 30 papers in international journals, 2 papers in national journals, 7 chapters in collective books, 52 papers in international conferences.