The Scientific Program includes five plenary lectures given by different distinguished researchers from the following associations:
  • IFORS. International Federation of Operational Research Societies: Anna Nagurney (Google Scholar).
  • ALIO. Association of Latin-Iberoamerican Operational Research Societies: Sebastian Ceria (Axioma)
  • EURO. Association of European Operational Research Societies: Emma Hart (Google Scholar)
  • SEIO. Spanish Society of Statistics and Operations Research: Ángel Corberán (Google Scholar)
  • APDIO. Portuguese Society of Operations Research: Carlos Henggeler Antunes (Google Scholar)

Anna Nagurney (IFORS distinguished lecturer)

Anna Nagurney is the John F. Smith Memorial Professor at the Isenberg School of Management at the University of Massachusetts Amherst and the Director of the Virtual Center for Supernetworks, which she founded in 2001. She holds ScB, AB, ScM and PhD degrees from Brown University in Providence, RI. She is the author of 14 books, more than 200 refereed journal articles, and over 50 book chapters. She has been recognized for her research on networks with the Kempe prize from the University of Umea, the Faculty Award for Women from the US National Science Foundation, the University Medal from the University of Catania in Italy, and was elected a Fellow of the RSAI (Regional Science Association International), an INFORMS (Institute for Operations Research and the Management Sciences) Fellow, and a Network Science Society Fellow. Among her recent awards are the Constantin Caratheodory Prize received in 2019 from the International Society of Global Optimization, the 2018 Omega Rho Distinguished Lecturer of INFORMS, and the 2020 Harold Larnder Prize of CORS. Anna has also been recognized for her mentorship of students and her female leadership with the INFORMS WORMS Award and the INFORMS Moving Spirit Award. Her research has garnered support from the AT&T Foundation, the Rockefeller Foundation through its Bellagio Center programs, the Institute for International Education, the National Science Foundation, and the Advanced Cybsersecurity Center. She has given plenary/keynote talks and tutorials on 5 continents. She presently serves on the editorial boards of a dozen journals and two book series and is the editor of another book series. Professor Nagurney has been a Fulbrighter twice (in Austria and Italy), was a Visiting Professor at the School of Business, Economics and Law at the University of Gothenburg in Sweden and was a Distinguished Guest Visiting Professor at the Royal Institute of Technology (KTH) in Stockholm. She was a Visiting Fellow at All Souls College at Oxford University during the 2016 Trinity Term and a Summer Fellow at the Radcliffe Institute for Advanced Study at Harvard in 2017 and 2018. Anna has held visiting appointments at MIT and at Brown University and was a Science Fellow at the Radcliffe Institute for Advanced Study at Harvard University in 2005-2006.

Anna's research focuses on network systems from transportation and logistical ones, including supply chains, to financial, economic, social networks and their integration, along with the Internet. She studies and models complex behaviors on networks with a goal towards providing frameworks and tools for understanding their structure, performance, and resilience and has contributed also to the understanding of the Braess paradox in transportation networks and the Internet. She has also been researching sustainability and quality issues with applications ranging from pharmaceutical and blood supply chains to perishable food products and fast fashion to humanitarian logistics. She has advanced methodological tools used in game theory, network theory, equilibrium analysis, and dynamical systems. She was a Co-PI on a multi-university NSF grant with UMass Amherst as the lead: Network Innovation Through Choice, which was part of the Future Internet Architecture (FIA) program and a Co-PI on a recent NSF EAGER grant.

NetwORks: Changing Our WORld for the Better.

The Operations Research community has been instrumental in advancing network models, algorithms, and applications globally over many decades. As a consequence, other disciplines have also greatly benefited from these scientific advances. In this talk, I will highlight interdisciplinary network applications, coupled with supporting methodologies, including variational inequality theory and game theory, that are transforming our world. Topics that will be discussed include: food quality and security, healthcare and disaster relief, as well as cybersecurity and the Future Internet.

Sebastian Ceria (ALIO distinguished lecturer)

Sebastian Ceria is Chief Executive Officer of Qontigo. Launched in September 2019, Qontigo combines the sophisticated risk analytics and portfolio-construction tools of Axioma with the market-defining indices of STOXX and DAX, creating a financial intelligence innovator focused on modernizing investment management with its partners. Sebastian was previously CEO of Axioma, which he founded and led since 1998. Before that, he was an Associate Professor of Decision, Risk and Operations at Columbia Business School. He has worked extensively in robust optimization and its application to portfolio management, authoring many articles in industry and academic publications. He is a recipient of the Career Award for Operations Research from the National Science Foundation. Sebastian holds a PhD in Operations Research from Carnegie Mellon University's Tepper School of Business, and a degree in Applied Math from the University of Buenos Aires, Argentina..

Advances in Portfolio Optimization

The usage of optimization in portfolio construction has exploded over the last few years. Advances in modeling and solution techniques now make it possible to solve portfolio construction problems in a wide range of applications. In this talk, we review the current state of the art, as well as discuss future directions.

Emma Hart (EURO distinguished lecturer)

Emma Hart is a Professor in the School of Computing at Edinburgh Napier University where she leads the Nature-Inspired Computing research group that specialises in optimisation and learning algorithms applied in domains that range from combinatorial optimisation to swarm robotics. She has an Honours Degree in Chemistry from the University of Oxford, and an MSc and PhD in Artificial Intelligence from the University of Edinburgh, where she developed novel evolutionary and immune-inspired methods to solve combinatorial optimisation problems.

She was appointed as Editor-in-Chief of Evolutionary Computation (MIT Press) in 2017 which focus mainly evolutionary optimisation. She is an elected member of the Executive Board of the ACM SIG on Evolutionary Computation. More broadly, she invited member of the UK Operations Research Society Research Panel, and in Scotland, co-leads the Artificial Intelligence theme within SICSA. She has recently been appointed to the Scottish Government led Steering Committee which is developing a new AI Strategy for Scotland to be rolled out in 2020. She has a sustained track record of obtaining funding from the EU, EPSRC and of engaging with industry via KTP projects and consultancy, and participates enthusiastically in public-engagement activity, e.g Pint of Science. Current projects include an EPSRC funded project on optimising the design of robot morphologies and controllers and UKRI funded project to optimise customer experience in call-centres.

Interdisciplinary Approaches to Optimisation

Recent years have seen a growing trend towards a more interdisciplinary approach to problem-solving; this is particularly obvious within the optimisation and machine-learning communities, with both communities benefitting from techniques developed by the other. In this talk I will highlight three examples of ideas originating from outside of the traditional optimisation field that can enhance the development of new algorithms and approaches to optimisation.

The first idea uses methods from deep-learning to improve algorithm-selection for online optimisation problems, i.e. problems where multiple decisions are made sequentially based on a piece-by-piece input: the approach exploits temporal patterns in the sequencing rather taking the classical approach of deriving features, and is shown to outperform state-of-the-art feature-based classifiers on large sets of packing problems. The second idea brings the notion of continual (lifelong) learning - common in machine-learning – to optimisation, showing how a meta-heuristic approach to generating new heuristics can autonomously self-adapt over time to a changing stream of problem instances, drawing on a history of previously solved instances to speed up adaptation. Finally, I will discuss methods arising in the evolutionary robotics community that are geared towards creating repertoires of solutions to a problem, where each solution is diverse with respect to user-defined features of interest. Adapted to combinatorial optimisation, this not only provides new insights into factors that influence solution quality, but enables a user to select the most appropriate solution for a given circumstance.

Ángel Corberán (SEIO distinguished lecturer)

Born in 1954 in Valencia (Spain), he received his Ph.D. degree in Mathematics at the University of Valencia in 1982 and is full Professor in the Department of Statistics and Operations Research at the University of Valencia since 2006.

His main research interests include Combinatorial Optimization, Vehicle Routing, Location, and Logistics. Dr. Corberán has published about 90 papers and book chapters in the Combinatorial Optimization area, and has been the lead researcher in several research proposals funded by the Spanish Ministries of Science and Technology and Economy and Competitiveness, as well as in others funded by the Government of the Valencia's Region. At this moment he is Associate Editor of Computational Optimization and Applications and TOP, and member of the Editorial Boards of Computers & Operations Research, EURO Journal on Transportation and Logistics and the EURO Journal on Computational Optimization.

Arc Routing Problems with drones

We present here some arc routing problems with drones (Drone ARPs) and study their relation with well-known postman ARPs. Applications for Drone ARPs include traffic monitoring by flying over roadways, infrastructure inspection such as by flying along power transmission lines, pipelines or fences, and surveillance along linear features such as coastlines or territorial borders.

Unlike the postmen in traditional ARPs, drones can travel directly between any two points in the plane without following the edges of the network. As a consequence, a drone route may service only part of an edge, with multiple routes being used to cover the entire edge. Thus the Drone ARPs are continuous optimization problems with an infinite number of feasible solutions. In order to solve them as a discrete optimization problem, we approximate each curve in the plane by a polygonal chain, thus allowing the vehicle to enter and leave each curve only at the points of the polygonal chain.

If the capacity of the vehicles is unlimited, the resulting problem is a Rural Postman Problem (RPP). For this case we present an algorithm that iteratively solves RPP instances with an increasing number of points of the polygonal chain. We also discuss the case in which the drones have limited capacity and a fleet of drones is available. For this problem, the K-Drones Arc Routing Problem, we have implemented a metaheuristic and a branch-and-cut algorithm producing good results.

Carlos Henggeler Antunes (APDIO distinguished lecturer)

Carlos Henggeler Antunes is a full Professor with the Department of Electrical and Computer Engineering, Director of the R&D Institute INESC Coimbra, and member of the coordination committee of the Energy for Sustainability Initiative of the University of Coimbra, Portugal. He obtained his PhD in Electrical Engineering (Optimization and Systems Theory) at the University of Coimbra in 1992. His research interests include multiple objective optimization with mathematical programming and metaheuristics, multicriteria analysis, and energy systems and policies with focus on energy efficiency and demand response. He has participated in several R&D and consulting projects in the domains of energy efficiency and decision support systems. He co-authored the book “Multiobjective Linear and Integer Programming” published by Springer in April 2016.

(Orcid ID: 0000-0003-4754-2168; Scopus Author ID: 7004835237; Researcher ID: F-8517-2011)

Optimizing electricity time-of-use retail pricing in the residential sector - a semivectorial bilevel programming approach (joint work with Maria João Alves)

Residential electricity consumers are, in general, charged at flat or dual time-of-use tariffs along the day, which are defined by the retailer for long periods. These pricing schemes do not convey price signals reflecting generation costs and grid conditions. Hence, consumers lack the incentives to adopt different consumption patterns using the flexibility they generally have in the operation of some end-use appliances. Dynamic time-of-use tariffs are expected to become a relevant pricing scheme in smart grids, fostering a “demand follows supply” paradigm and facilitating larger shares of renewable generation. At end-users’ premises, automated home energy management systems endowed with optimization algorithms will control the operation of appliances, balancing economic and quality of service objectives with potential benefits for customers and grid management.

A semivectorial bilevel programming approach is presented to model the interaction between electricity retailers and consumers. The retailer (upper level decision maker) establishes dynamic time-of-use prices to maximize profits. The consumer (lower level decision maker) responds by selecting, under that price setting, an appliance scheduling considering the minimization of the electricity bill and the minimization of the dissatisfaction associated with operating appliances outside habitual patterns.

The lower level optimization problem is formulated as a bi-objective mixed-integer linear programming problem. A hybrid approach is presented, which consists of a genetic algorithm for the upper level problem and an exact solver for the surrogate scalar problems at the lower level. Results of an illustrative case study are presented.

Latest news

  • 11/15/19

    IBERIA L.A.E. Preferred Air Carrier of the XX Latin Ibero-American Conference on Operations Research will grant a 10% discount to conference attendees.

    More information here.



Cookie policy

We use cookies in order to be able to identify and authenticate you on the website. They are necessary for the correct functioning of it, and therefore they can not be disabled. If you continue browsing the website, you are agreeing with their acceptance, as well as our Privacy Policy.

Additionally, we use Google Analytics in order to analyze the website traffic. They also use cookies and you can accept or refuse them with the buttons below.

You can read more details about our Cookie Policy and our Privacy Policy.