Plenaries and keynotes

There is a long history of applying operations research techniques to problems in airline and air transportation system planning and operations. Over time, these techniques have become more sophisticated, with models and algorithms enhanced to account for multiple sources of uncertainty, competitive effects, passenger choice, and dynamic decision making, to name a few. The impacts have been significant, as demonstrated through numerous applications to air transportation problems across the globe. In this talk, I will briefly review this history, provide examples that illustrate the evolution of modeling and solution approaches, quantify some of the impacts, and highlight research opportunities in the field.

Districting or territory design problems involve essentially partitioning decisions.  Given a set of basic geographical areas, the idea is to find a partition of these into clusters or districts in such a way that specific planning requirements are met.  These requirements depend naturally on the specific application.  Districting problems are motivated by very different applications, such as political districting, sales and service territory design, school districting, the design of territories for waste collection, to name a few.  Through the years many ideas for tackling these very difficult discrete optimization problems have been successfully developed.

In this talk, we will go through some of the most representative models and features, highlighting particular properties and successful ideas for efficient algorithmic development.  Special emphasis will be given to the computational issues and how particular problem structures and properties can be adequately exploited for efficient algorithmic design.  The last part of the talk will include a discussion of current research trends on this fascinating class of discrete optimization problems.

Roger Ríos is a Professor of Operations Research in the Graduate Program in Systems Engineering at Universidad Autonoma de Nuevo Leon, Mexico. He has held Visiting Scholar positions at the University of Texas at Austin (OR/IE Program), Barcelona Tech (Department of Statistics and Operations Research), University of Colorado (Leeds School of Business), and University of Houston (High Performance Computing Center). He holds a PhD in OR/IE from the University of Texas at Austin. His research interests are mainly in designing and developing efficient solution methods to hard discrete optimization problems. He has addressed applied decision-making problems on districting, location, healthcare, natural gas transportation systems, and scheduling. His research has been published in leading journals in the field. He is a member of the Editorial Board of Computers & Operations Research and Operations Research Perspectives. He is a Fellow of the Mexican System of Research Scientists, the Mexican Academy of Sciences, and the Mexican Academy of Computer Sciences. He is former President and a founding member of the Mexican Society of Operations Research. More about his work can be found at http://yalma.fime.uanl.mx/~roger/

We sometimes get stuck while trying to model computational problems in a certain framework, or while trying to design fast algorithmic solutions for certain subproblems. The talk discusses some concrete situations, and convincingly explains (with the help of computational complexity theory) the various reasons for our failures.

Gerhard Woeginger is a professor at RWTH Aachen where he chairs the algorithms and complexity group. His research interests lie in the intersection area of Operational Research, Foundations of Computer Science, and Discrete Mathematics.
Concrete topics are approximation, scheduling, competitive analysis of online algorithms, parameterized complexity, graph theory; recently also algorithmic game theory and computational social choice.

Woeginger served as program chair of the European Conference on Operational Research (EURO-2009), the International Computer Science Symposium in Russia (CSR-2016), and of several other conferences. He received a Humboldt Research Award in 2011, and he was elected to the Academia Europaea in 2014.

Since trading volatility began more than 20 years ago it has grown in depth and scope to the point of major regulatory concern. Not only is manipulation rife, many investment banks now have considerable exposure to a market crash caused by their large-scale issuance of variance swaps and volatility exchange-traded products. It is possible that volatility could become the new ‘credit’ in the next global banking crisis. It is therefore imperative to really understand the variance risk premium – what drives it, and how it behaves in different market circumstances. However, previous estimates of this premium are subject to numerous biases and are based on low frequency data over a relatively short time period.

This talk starts by surveying the potential hazards of trading volatility and the growth in complexity of products which have become the target of manipulative practices, just as they are being pushed out to the ordinary investor. Then we focus on a new and general aggregation property which – for the first time – allows not only variance, but higher moment risk premia to be estimated unbiasedly and efficiently over a long period of time. Finally, we use daily data to examine the determinants of S\&P 500 variance, third and fourth moment risk premia, showing that they depend on the market regime. They have also evolved over time as the market for trading volatility continues to expand.

From 1985 – 1998 Carol was lecturer in Mathematics and Economics at the University of Sussex. From 1999 – 2012 she was Chair of Risk Management at the ICMA Centre in the Henley Business School at Reading. From 2010 – 2012 Carol was Chair of the Board of PRMIA (Professional Risk Manager’s International Association). From 1995 – 2004 she worked half-time as an academic and half-time in the finance industry.

Carol has held the following positions in financial institutions: Fixed Income Trader at UBS/Phillips and Drew (UK); Academic Director of Algorithmics (Canada); Director of Nikko Global Holdings and Head of Market Risk Modeling (UK); Risk Research Advisor, SAS (USA). She also acts as an expert witness and consultant in financial modelling.

She publishes widely on a broad range of topics, including: volatility theory; option pricing and hedging; trading volatility; hedging with futures; alternative investments; random orthogonal matrix simulation; game theory and real options. She has written and edited numerous books in mathematics and finance and published extensively in top-ranked international journals.   Her four volume textbook on Market Risk Analysis (Wileys, 2008) is the definitive guide to the subject.

The stream on Teaching OR/MS is part of the EURO and IFORS conferences since 2010 in Lisbon. The contributions of our community for this stream show that skilled teachers with their enthusiasm and experience are able to create teaching experiments that, as we once heard, are condemned to be successful.

In this talk, we will go through several examples of teaching and learning practices in OR, selected from the ones presented in the Teaching OR/MS stream, from the literature, and from direct contributions. These reported practices may be based on case studies, specific software packages, classroom games and also projects which engage students and help to develop the competencies that are needed to apply the OR/MS methods in practice. There are also some experiments on assessment for large groups of students and on gamification. We believe that experiments like these ones, adapted to the specific program, and to the number and concrete characteristics of the students, may form the building blocks for rather successful OR/MS courses.

Maria Antónia Carravilla teaches at the Faculty of Engineering of the University of Porto (FEUP) since 1985, and at the Porto Business School in the Executive and Magellan MBA’s, both in Portugal. She has also been a visiting professor at Universidade de São Paulo, Brazil (USP). Maria Antónia is currently the Director of the Doctoral Program in Engineering and Industrial Management at FEUP. Operations Research is her scientific area, and within Operations Research she has worked on Lot-Sizing, Scheduling, Nesting, Retail Shelf Planning, Logistics Platform Operations, Supply Chain Management, and Fleet Management and Pricing problems. From the point of view of the techniques, her focus is on the development of optimisation models using Mathematical Programming and Constraint Programming and on Matheuristics that hybridise the optimisation models with Metaheuristics, aiming to address real-world problems and provide good or even optimal solutions in a reasonable timeframe for decision support. During her career, she has always been involved in R&D projects with industry, services and public administration and projects funded by the Portuguese Science Foundation. The outputs of these projects led to long-lasting collaborations with these organisations and were the basis for the theses of most of her Ph.D. students. As a teacher, she has been responsible for several courses on Operations Research, Operations Management and Logistics, taught at the BSc, MSc and Ph.D. levels. She has also supervised MSc students whose theses were developed in academia as well as in industry. Maria Antónia is very interested in topics related to teaching Operations Research in particular and made several presentations on teaching experiences in OR conferences and, since 2010, she organises a stream on Teaching OR/MS in EURO-k and IFORS conferences.
Maria Antónia Carravilla received in 2009, the first time it has been awarded, the Award for Pedagogical Excellence of the Faculty of Engineering.

The explosion in digital music information has spurred the developing of mathematical models and computational algorithms for accurate, efficient, and scalable processing of music information. According to the 2017 IFPI Industry Global Music Report, the total global recorded music revenue was US$15.7b in 2016, 50% of which were digital. Industrial scale applications linking recorded content to listeners include Last.fm, Pandora, Shazam, and Spotify. Shazam has over 120 million active users monthly and Spotify over 140 million. Since the launch of Shazam, users have issued 30 billion song identification requests, growing by 20 million each day. With such widespread access to large digital music collections, there is substantial interest in scalable models for music processing. Optimization concepts and methods thus play an important role in machine models of music engagement, music experience, music analysis, and music generation.

In this talk, we will show how optimization ideas and techniques have been integrated into computer models of music representation and expressivity, and into computational solutions to music generation and structure analysis. More specifically, we will report on research and outcomes on an interior-point approach to modeling tonal perception (inferring the keys and chords from note information), the idea of duality in reverse-engineering music structure analyses, constraint-based music generation to instill long-term structure, an optimization heuristic for stream segregation  separating out voices from a polyphonic texture), statistical and optimization-based approaches to music segmentation, and rhythm transcription that minimizes quantization error for music and arrhythmia sequences. The talk will contain numerous music illustrations and, where appropriate, live performances of music and demonstrations of interactive visualization software on a piano or keyboard.

Elaine Chew is Professor of Digital Media in the School of Electronic Engineering and Computer Science at Queen Mary University of London, where she is affiliated with the Centre for Digital Music, since 2011. Prior to that, she was a tenured Associate Professor at the University of Southern California, where she held joint appointments in the Viterbi School of Engineering and the Thornton School of Music (courtesy). Her research centers on the mathematical modeling and and computational analysis of music structures, with recent applications to cardiac signal analysis. She is recipient of a Presidential Early Career Award for Scientists and Engineers in the US and the Edward, Frances, and Shirley B. Daniels Fellowship at the Radcliffe Institute for Advanced Study at Harvard. She is author of numerous research papers and a recent monograph on Mathematical and Computational Modeling of Tonality, the first volume on music in the Springer International Series on Operations Research and Management Science. Prof. Chew received PhD and SM degrees in Operations Research from MIT, a BAS in Mathematical and Computational Sciences (honors) and Music Performance (distinction) from Stanford, and Fellowship and Licentiate diplomas in piano performance from Trinity College, London.

Complex systems typically display a modular structure, as modules are easier to assemble than the individual units of the system, and more resilient to failures. In the network representation of complex systems, modules, or communities, appear as subgraphs whose nodes have an appreciably larger probability to get connected to each other than to other nodes of the network. In this talk I will address three fundamental questions: How is community structure generated? How to detect it? How to test the performance of community detection algorithms? I will show that communities emerge naturally in growing network models favoring triadic closure, a mechanism necessary to implement for the generation of large classes of systems, like e.g. social networks. I will discuss the limits of the most popular class of clustering algorithms, those based on the optimization of a global quality function, like modularity maximization. Testing algorithms is probably the single most important issue of network community detection, as it implicitly involves the concept of community, which is still controversial. I will discuss the importance of using realistic benchmark graphs with built-in community structure.

Santo Fortunato is the Director of the Center for Complex Networks and Systems Research (CNetS) at Indiana University and a Scientific Director of Indiana University Network Science Institute (IUNI). Previously he was professor of complex systems at the Department of Computer Science of Aalto University, Finland. Prof. Fortunato got his PhD in Theoretical Particle Physics at the University of Bielefeld In Germany. He then moved to the field of complex systems, via a postdoctoral appointment at the School of Informatics and Computing of Indiana University. His current focus areas are network science, especially community detection in graphs, computational social science and science of science. His research has been published in leading journals, including Nature, Science, PNAS, Physical Review Letters, Reviews of Modern Physics, Physics Reports and has collected over 22,000 citations (Google Scholar). His review article Community detection in graphs (Physics Reports 486, 75-174, 2010) is one of the best known and most cited papers in network science. He received the Young Scientist Award for Socio- and Econophysics 2011, a prize given by the German Physical Society, for his outstanding contributions to the physics of social systems.

There are many situations in which present actions must be made and resources allocated with incomplete knowledge of the future. It is not clear in this setting how to measure the quality of a proposed decision strategy. Online optimization compares the performance of a strategy that operates with no knowledge of the future (on-line) with the performance of an optimal strategy that has complete knowledge of the future (off-line). In some cases, some probabilistic information about the future may be available. In this talk, we provide an overview of results obtained from that perspective on problems arising from dynamic matching markets such as (i) online auctions, (ii) display advertisements, (iii) kidney exchange programs, and (iv) ride-hailing platforms.

Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science and a member of the Laboratory for Information and Decision Systems at MIT. He is also co-Director of the MIT Operations Research Center and the Faculty Director of the MIT-France Program.

Dr. Jaillet’s research interests include online optimization and learning; real-time, dynamic, and data-driven problems; and networks.

He is a Fellow of the Institute for Operations Research and Management Science Society (INFORMS) and a member of the Society for Industrial and Applied Mathematics (SIAM).

We will discuss a few examples from healthcare, where quantitative models embedded in decision-support tools can improve the quality of decision-making (for patients, physicians, or caregivers) and patient outcomes. Examples will come from a variety of applications, including (i) organ transplant decisions for patients and physicians (considering the survival curve estimates of accepting an organ and undergoing transplant, versus remaining on the waiting list hoping to receive a better quality organ in the future), (ii) catch-up scheduling for vaccinations, (iii) prenatal screening for Down Syndrome, (iv) scheduling of patients to receive a combination of services.

Pinar Keskinocak is the William W. George Chair and Professor, and co-founder and Director of the Center for Health and Humanitarian Systems at Georgia Tech. She also serves as the College of Engineering ADVANCE Professor.

Dr. Keskinocak’s research focuses on the applications of quantitative methods and analytics to have a positive impact in society, particularly in healthcare and humanitarian systems. Her recent work has addressed a broad range of topics such as infectious disease modeling, evaluating intervention strategies, and resource allocation; catch-up scheduling for vaccinations; decision-support for organ transplant; hospital operations management; and disaster preparedness and response. She has worked on projects with a variety of governmental and non-governmental organizations, and healthcare providers, including American Red Cross, CARE, Carter Center, CDC, Children’s Healthcare of Atlanta, Emory Healthcare, Grady Hospital, and Task Force for Global Health.

She has served her professional community in various roles, including Department Editor for Operations Research, INFORMS Secretary, INFORMS Vice President for Membership and Professional Recognition, co-founder and President of the Junior Faculty Interaction Group Forum, and President of the Women in OR/MS Forum. In addition to research and educational activities, she also spends a significant amount of her time and efforts on promoting diversity and inclusion among faculty, students, and staff in higher education.

Health care is a prototypical example of a complex system.  System modeling provides an opportunity to capture this complexity and to evaluate the potential positive and negative impacts of policy options and to gain insight into the factors that promote or inhibit success.  Our experience in the health policy context has highlighted the crucial importance of engaging stakeholders from the outset so as to encourage a sense of ownership of the process; the idea is that owning the process encourages support of any resulting policy decisions and actions. In this talk, I will discuss the use of Group Model Building as the mechanism of stakeholder engagement, and describe several practical examples, focusing on current work in Cambodia to disseminate and evaluate services for chronic non-communicable diseases (NCDs).   This effort is part of a 4-stage project with the ultimate goal of broadly implementing basic, efficient, and well-accepted NCD services throughout Cambodia.

Dr. Matchar is the Director of the Program in Health Services and Systems Research, one of five Signature Research Programs within the Duke-NUS Medical School, where he is also a Professor. He is also a Professor of Medicine at Duke University in the United States. His research is focused on the evaluation of clinical practice based on “best evidence,” and the implementation and evaluation of innovative strategies to promote practice change. He has worked in a wide range of clinical content areas, including cerebrovascular disease, stroke prevention and rehabilitation, women’s health issues, renal disease, Alzheimer’s disease, multiple sclerosis, migraine, and antithrombotic strategies. His methodological work addresses the design of effectiveness studies, strategies for bridging the gap between analysts and policy makers, utilizing simulation models to promote communication regarding complex clinical policy questions, and application of decision support systems to link evidence and community practice.

Data Science aims to develop models that extract knowledge from complex data and represent it to aid Data-Driven Decision Making. Mathematical Optimization has played a crucial role in the three main pillars of Data Science, namely Supervised Learning, Unsupervised Learning and Information Visualization. For instance, Quadratic Programming is used in Support Vector Machines, a Supervised Learning tool. Mixed-Integer Programming is used in Clustering, an Unsupervised Learning task. Global Optimization is used in MultiDimensional Scaling, an Information Visualization tool. Data Science models should strike a balance between accuracy and interpretability, enabling easy communication with the user who needs to interact with the models. In this lecture, we will navigate through several Mathematical-Optimization based models in Data Science that illustrate the important role of Mixed-Integer Nonlinear Programming in achieving such a balance.

Dolores Romero Morales is a Professor in Operations Research at Copenhagen Business School. Her areas of expertise include Supply Chain Optimization, Data Mining and Revenue Management. In Supply Chain Optimization she works on environmental issues and robustness. In Data Mining she investigates interpretability and visualization. In Revenue Management she works on large-scale network models. Her work has appeared in a variety of leading scholarly journals, including European Journal of Operational Research, Management Science, Mathematical Programming and Operations Research, and has received various distinctions.

She has worked with and advised various companies on these topics, including IBM, SAS, KLM and Radisson Edwardian Hotels, as a result of which these companies managed to improve some of their practices. SAS has named her an Honorary SAS Fellow and member of the SAS Academic Advisory Board.

She currently sits on the Editorial Review Board of Productions and Operations Management, is an Associate Editor of Omega, Journal of Global Optimization, and TOP.

Dolores joined Copenhagen Business School in 2014. Prior to coming to Copenhagen Business School she was a Full Professor at University of Oxford (2003-2014) and an Assistant Professor at Maastricht University (2000-2003). She has a BSc and an MSc in Mathematics from Universidad de Sevilla and a PhD in Operations Research from Erasmus University Rotterdam.

Attractive and efficient public transportation is needed for satisfying the increasing mobility demand in an environmental-friendly way. In view of
growing emissions, research on optimizing public transport is more relevant than ever.

The classical approach in public transportation planning is the following: After the network design, the lines and their
frequencies are planned. Based on these, the timetable is determined, and later on the vehicles’ and drivers’ schedules. In order to account for the passengers, their routes are estimated after each of these stages and then used as input for the next stage.
These single planning stages are well understood and many of them can algorithmically be treated. However, following the above sketched sequential approach may be far away from finding an optimal solution for the whole system. This calls for integrated optimization.

In this talk we present approaches for integrated optimization in public transportation, apply them to benchmark examples and discuss
how useful they are. While we focus on public transportation, many of the underlying ideas can also be used in other application areas.

Integrated versus sequential optimization.
The sequential procedure sketched above can be regarded as a Greedy approach: in each planning stage one aims at the best one can
do. This usually leads to suboptimal solutions. On the other hand, many of these single steps are already NP hard such that solving the
integrated problem to optimality seems to be out of scope. Nevertheless, we show how improvements can be made using the
Eigenmodel as a framework for (heuristic) integrated optimization. We furthermore introduce the price of sequentiality as a measure how much can be
gained by integrated approaches.

Integrating passengers’ routes.
While many models in public transportation aim to minimize the traveling time of the passengers, the behavior of the passengers is
not reflected realistically in most approaches. In many models, passengers are routed before the optimization. These routes are then
fixed and are the basis for finding good line plans and timetables. We show that such a first routing has an immense impact on the resulting
line plan, the timetable, the travel time and the costs. Better results are obtained if the routes of the passengers are variables which
are determined within the optimization. However, these models are even harder to solve. We show two tricks to make such models tractable.

Finally, both aspects are combined, again in the framework of the Eigenmodel.
We also show how more realistic models such as taking the vehicles’ capacities into account, or using logit models can be heuristically treated within this framework. In an outlook, we also sketch questions and ideas which may be relevant for integrated public transportation in the future.

After receiving her PhD in 1998 at the Technical University of Kaiserslautern, she worked as postdoc at the Fraunhofer Institute for Industrial Mathematics for two years before she went back to university to receive her Habilitation in 2003. She received a position as associate professor in Göttingen in 2004 and has been full professor since 2007.

Her research interests focus on discrete optimization in public transportation, multi-objective robust optimization, and several topics related to facility location. She develops approaches based on integer programming, graph-based algorithms, and on simulation.

She has been involved in many industrial and research projects, among them the European projects ARRIVAL and OptALI and cooperations with India. She also is the coordinator of the research unit on Integrated Transportation funded by the German Research Foundation (DFG).

Anita Schöbel is in the managing board of the German Society of Operations Research (GOR) and of the centre of Simulation Studies Clausthal-Göttingen (SWZ).

The basic concepts of Interior Point Methods (IPMs) were introduced by Frish in 1950’s, and further developed in the 1960’s, among others by Fiacco-McCormick (SUMT) and Dikin (Affince scaling). By the early 70’s it was concluded that, mostly due to numerical instability, IPMs most probably will not be viable algorithms for solving large scale optimization problems.

Karmarkar’s 1984 paper and the subsequent “Interior Point Revolution” fundamentally changed the landscape of optimization. IPMs become the method of choice to solve large-scale linear optimization problems, new classes of conic and convex optimization problems become efficiently solvable. The new powerful algorithmic and software tools opened new areas of applications. In this talk we walk through the history of IPMs, highlight the scientific and computer technology advances that make the Interior Point revolution possible.

Prior to his appointment at Lehigh U., where he served as the Chair of ISE 2008-2017, Prof. Terlaky has taught at Eötvös U., Budapest, Hungary; Delft University of Technology, Delft, Netherlands; McMaster U., ON, Canada. At McMaster he also served as the founding Director of the School of Computational Engineering and Science.
Prof. Terlaky has published four books, edited over ten books and journal special issues and published over 180 research papers. Topics include theoretical and algorithmic foundations of mathematical optimization (e.g., invention of the criss-cross method, oriented matroid programming), design and analysis of large classes of interior point methods for linear and conic linear optimization, disjunctive conic cuts for MISOCO, computational optimization, worst case examples of the central path, nuclear reactor core reloading optimization, oil refinery and VLSI design and robust radiation therapy treatment optimization, and inmate assignment optimization.
Prof. Terlaky is Founding Honorary Editor-in-Chief of the journal, Optimization and Engineering. He has served as associate editor of ten journals and has served as conference chair, conference organizer, and distinguished invited speaker at conferences all over the world. He was general Chair of the INFORMS 2015 Annual Meeting, a former Chair of INFORMS’ Optimization Society, Chair of the ICCOPT Steering Committee of the Mathematical Optimization Society, currently Chair of the SIAM Activity Group on Optimization, he is Fellow of the Fields Institute, and Fellow of INFORMS. He received the MITACS Mentorship Award for his distinguished graduate student supervisory record, and the Award of Merit of the Canadian Operations Research Society. November 2017 he received the Wagner Prize of INFORMS and the Egerváry Award of the Hungarian Operations Research Society

His research interest includes high performance optimization algorithms, optimization modeling and its applications.

Visits to companies and non-profit organizations typically provide inspiration for challenging research projects. In this presentation, information will be given on the organization of public-private partnerships addressing societal challenges. Several examples of projects will be shown in which solving the real-life questions required the design of new OR-based models and solution approaches. Specifically, we will discuss the design of distribution networks for sustainable fuels for transportation, replenishment operations at ATMs, physical internet operations and logistics for education. For each of those projects, we will explain the start of the project, the creation of the consortium, the formulation of the research questions and methodology, the actual research and results obtained.

(Talk not recorded due to request from the speaker)

Iris Vis is Dean of Industry Relations at the University of Groningen. In this function she is at a university level responsible for initiating and maintaining sustainable cooperation of the university with businesses, governmental and non-profit organizations. She is also a professor of industrial engineering at the Faculty of Economics and Business. Vis has a Master’s degree in mathematics from the University of Leiden and received her PhD at the Erasmus University Rotterdam. Before joining the University of Groningen, she was an associate professor at the VU University Amsterdam. She was a visiting researcher at CIRRELT (Montreal / Quebec City, Canada), Virginia Tech (Blacksburg, USA) and Georgia Institute of Technology (Atlanta, USA).

Vis has performed numerous projects in cooperation with companies, resulting in a blend of rigorous academic work with practical applicability. Vis is currently Associate Editor of OR Spectrum. She currently acts as a project leader in the NWO project “Towards virtual ports in a physical internet”. In the past years she acted as project leader in the Dinalog (Dutch Institute for Advanced Logistics) project “Design of LNG networks”, and as a work package leader in three other projects

Many challenging applications in the field of transportation and logistics often involve the solution of underlying large-scale network problems with shortest path structures. The pulse framework implements ideas and strategies that have been available in the playground of network optimization for years, but when used collectively under a single framework they become stronger and are able to solve a wide array of hard shortest path problems. Initially, we proposed the pulse algorithm as an exact method for solving shortest paths with side constraints. Later, we identified other shortest path variants where the same principle behind the pulse algorithm applied. In this tutorial, we present the pulse algorithm as a framework based on the idea of performing an implicit enumeration of the entire solution space supported by pruning strategies that efficiently discard a vast number of solutions. We focus on the key aspects that have made possible the effective solution of several shortest path variants. We also illustrate the use of the pulse algorithm as a building block to solve hard combinatorial problems beyond the shortest path domain.

Andres L. Medaglia is Professor of Industrial Engineering at Universidad de los Andes (Bogotá, Colombia) and director of the research center Centro para la Optimización y Probabilidad Aplicada (COPA). He holds a Ph.D. (2001) in Operations Research (OR) from North Carolina State University (USA). From 1999 to 2002 he worked as an optimization specialist developing decision support systems for SAS (Cary, NC, USA). In 2002, he joined the Industrial Engineering Department at Universidad de los Andes. His current research interests include the development and application of optimization techniques to transportation and logistics, health systems, agricultural systems, and engineering design. He has served as Secretary and Vicepresident of the Latin-Ibero American Association of Operations Research (ALIO); as Vicepresident of Central/South America for the Institute of Industrial Engineers (IIE); and as Vicepresident of the Colombian Operational Research Society (ASOCIO). In INFORMS, he served as the Liason for the Americas and in the Best Paper Award Committee for the Transportation Science & Logistics Society (TSL). He has been the recipient of several prizes, most recently, the INFORMS/TSL President’s Service Award (2017), the IFORS Prize for OR in Development (Quebec City, Canada, 2017), the first prize in the INFORMS Railway Application Section (RAS) Problem Solving Competition (2011), and the EURO Award for the Best EJOR (Review) Paper in 2015. More information at: http://wwwprof.uniandes.edu.co/~amedagli