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Advanced Methods in Operations Research for Logistics and Transportation

¡Información de Interés!

Curso aplazado para 2021 debido a la contingencia global por el COVID-19. Una vez se definan las nuevas fechas, las informaremos. 

In the context of decision-making processes, we use Operations Research methods to understand, predict and optimize the behavior of real-life systems through mathematical models. Operations Research techniques are designed to tackle problems with practical meaning, which are typically very complex. These models and methods have been applied in numerous contexts such as defining public and private policies, and planning processes for government and industry with an exceptionally broad spectrum.

General Objectives
  • To promote scientific collaboration in research lines that focus on advanced methods in Operations Research
  • To present state-of-the-art methodologies and problems within the context of Logistics and Transportation.
Specifics Objectives
  • To present concrete applications in Logistics and Transportation where advanced optimization and machine learning techniques are applied.
  • To stablish potential future lines of research.
  • To communicate recent research projects of the participants and the invited speakers.
  • To introduce state-of-the-art solution methods for various optimization and prediction problems in Logistics and Transportation.
Value proposal

In the context of decision-making processes, we use Operations Research methods to understand, predict and optimize the behavior of real-life systems through mathematical models. Operations Research techniques are designed to tackle problems with practical meaning, which are typically very complex. These models and methods have been applied in numerous contexts such as defining public and private policies, and planning processes for government and industry with an exceptionally broad spectrum.

Nowadays, in the era of information and data, the development of technological tools that support the decision-making process gains vital importance, which in turn, reaffirms the value of studying Operations Research methods. The area of Logistics and Transportation systems is a special case where the technological growth is evident in the last two decades. There, predicting patterns and optimizing decisions plays a major role in the overall operations. Moreover, this area has been a particular source of study for researchers and practitioners in Operations Research. The problems encountered usually represent a high-value decision but also, they possess an intrinsic mathematical structure that provoke a detailed scientific study.

Having in mind today’s global requirements in the development of algorithms and efficient solution methods to problems that appear in the contexts of Logistics and Transportation we have elaborated an initiative to promote the better understanding of cutting-edge technology and the scientific collaboration for the design and creation of new models and methods in Operations Research.


There will be four short courses or modules in recent topics of Operations Research. Each module consists of 6 hours of lecture and will be taught by a different expert. There will be time for questions and comments from the participants. We will also offer a 6 hours hands-on workshop on specialized optimization software.

There will be parallel sessions for short talks and poster presentations from the research projects of the participants.

All the modules and workshop will be in English. The presentations from the participants must be either in English or in Spanish with the slides in English.

Addressed to

The school is targeted to all the members of the academic community and practitioners who are interested in advanced topics in Operations Research. We expect the focus of participants to be graduate students from the areas of mathematics, industrial engineering and computer science. It is important the interested applicant has some knowledge of Operations Research methods.

Academic Content
  • Modules
  • Models and Decomposition Methods for Large-Scale Network Optimization (Prof. Ivan Contreras)

    Network optimization problems lie at the hearth of network design planning in transportation and telecommunications systems. This series of talks focus on a challenging class of problems referred to as general network design, which offers a unified view of integrated facility location, and network design. Their main difficulty stems from the inherent interrelation between two levels of the decision process. The first level considers design decisions such as the selection of nodes to locate facilities and the activation of links to connect nodes. The second level considers tactical decisions such as the assignment of nodes to facilities and the routing of flows through the network. We present an overview of some of the most prominent mathematical programming formulations that have been used in combination with decomposition methods to develop state-of-the-art exact solution algorithms capable of solving large-scale instances of three important classes of problems involving both linear and nonlinear cost functions: hub network design, multi-level facility location, and multi-commodity network design. We highlight the connections between Lagrangean relaxation, Dantzig-Wolfe decomposition, Benders decomposition, and related primal relaxations in the context of such problems. We also point out to several algorithmic refinements that have been used to accelerate the convergence of such decomposition methods. Practical implementation guidelines to improve their overall performance are discussed.

    Constraint Programming for Optimisation (Prof. Louis-Martin Rousseau)

    In these series of sessions, we will present the key concepts of Constraint Programming (CP) and illustrate how it can be applied to classic and combinatorial problems, such as the Traveling Salesman Problem and Vehicle Routing Problem as well as some real-life challenges, which arise in supply chain logistics. We will illustrate the efficiency of CP in context of personnel scheduling model, and discuss some realistic cases study from the Canadian industry. Finally, we will give an intuitive introduction to Column Generation and illustrate how CP can be used a pricing strategy.

    Mixed Integer Programming: Past, Present and Future (Prof. Andrea Lodi)
    Demand Forecasting and Statistical Learning for Transport Planning (Prof. Emma Frejinger)
  • Optimization Software Workshop (Josh Woodruff - IBM)
  • Short presentation by the participants
  • Poster Session
  • “The content may have variations, being a flexible proposal, that pursuit the maximum development of the students according to their specific needs.”

Este temario puede tener variaciones, siendo una propuesta de capacitación flexible, que busque el máximo desempeño de los alumnos, de acuerdo con las necesidades específicas de los mismos.

Emma Frejinger Ph.D.

Associate Professor of the Department of Computer Science and Operations Research at the University of Montreal and holder of two chairs: the Canada Research Chair in Demand Forecasting and Optimization of Transport Systems and the Canadian National (CN) Chair in Optimization of Railway Operations. She holds a Ph.D. in mathematics awarded by the Ecole Polytechnique Fédérale de Lausanne, Switzerland. Her areas of expertise include both transportation demand prediction and optimization of transportation networks. More precisely, her current major projects are related to the optimization of railway operations and combine operations research and data science methodologies. Her students and herself have won several international awards, including twice the prestigious TSL Dissertation Prize bestowed by the INFORMS Transport Science and Logistics Society. She has realized numerous projects in collaboration with public and private actors. In 2017, the Association québecoise des transports (AQTr) awarded the excellence prize in the freight transportation category to a collaborative project between CardoM and CIRRELT, for which she was lead of research.

Ivan Contreras Ph.D.

Associate Professor of the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University and holder of the Research Chair in Transportation and Logistics Network Optimization. He obtained his B.Sc. and M.Sc. in Industrial Engineering from the University of Americas, Mexico. In 2009 he received his Ph.D. degree from the Department of Statistics and Operations Research at the Technical University of Catalonia, Barcelona, Spain. Prior to joining Concordia in 2011, he worked as a researcher at the University of Heidelberg, Germany, and as a post-doctoral fellow at HEC Montréal and at the CIRRELT. In 2015 he received the Chuck ReVelle Rising Star Award. His research interests deal with the study and development of mathematical models and solution algorithms for strategic, tactical and operational decision problems arising in production, transportation, logistics, telecommunications and computer networks. His areas of expertise are transportation and logistics, warehouse and distribution management, and decomposition methods for large-scale optimization.

Andrea Lodi Ph.D.

Professor of the Mathematics and Industrial Engineering Department at École Polytechnique Montréal. Since 2014, Professor Lodi holds the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making at Polytechnique Montréal—Canada’s leading research chair in the field of operations research. Internationally recognized for his work on mixed linear and nonlinear programming, Prof. Lodi is focused on developing new models and algorithms to quickly and efficiently process massive amounts of data for multiple sources. These algorithms and models are expected to yield optimized real-time decision-making strategies. His research applies in a range of sectors, including energy, transport, health, production and supply chain logistics management. He holds a PhD in systems engineering (2000) and was a full professor of operations research in the Department of Electrical, Electronic and Information Engineering at the University of Bologna. He coordinates large-scale European operations research projects and has worked as a consultant for the CPLEX R&D team at IBM since 2006. He has published over 80 articles in major journals in mathematical programming and served as an associate editor on many of them. Prof. Lodi was awarded the 2010 Google Faculty Research Award and the 2011 IBM Faculty Award. He was a member of the prestigious Herman Goldstine program at the IBM Thomas J. Watson Research Center in 2005–2006.

Louis-Martin Rousseau Ph.D.

Professor of the Mathematics and Industrial Engineering Department at École Polytechnique Montréal. He holds The Canadian Research Chair on Analytics and Logistics of Healthcare, which is devoted to the study of problems where decisions related to the design of care plans and their implementation, are both complex and/or interconnected. He holds a Ph.D. in computer science and operations research from Université de Montréal. His areas of expertise include the hybridization of classical operations research methods and constraint programming, which comes from artificial intelligence. Prof. Rousseau has published over 150 research articles in top international journals. His current research focuses on transportation logistics, scheduling and resource optimization in healthcare.

Josh Woodruff

Modeling Director at Optimized FInancial Systems and ILOG Pre-acquisition in New York. He opened ILOG’s Latin American Offices contributing on the technical side and was IBM Technical Sales director for Latin America. Josh has occupied positions at Morgan Stanley Equities Risk Management in both New York and Brazil as well as at IBM Global Technical Sales department. He received his undergraduate degree from Yale University in Applied Mathematics and Economics and his Masters degree in Industrial Engineering (with full scholarship) from University of Southern California. He has now started a PhD program at the University of Texas at Austin in the areas of financial risk management and optimization.

Organizing committee:
  • Camilo Ortiz Astorquiza, Pontificia Universidad Javeriana (Chair)
  • Jairo Montoya Torres, Universidad de La Sabana
  • Andrés Medaglia González, Universidad de los Andes
  • Fernando Novoa Ramírez, Pontificia Universidad Javeriana
Scientific Committee:
  • TBD
Submit an abstract

Participants are encouraged to submit an extended abstract of up to 2 pages (including bibliography) of their work to be presented in the posters session or in the short presentations (20 minutes) session. Interested participants must send along the abstract a recent curriculum vitae. Graduate students should also submit a recommendation letter written by their advisor. The submission deadline for full consideration is February 28th, 2020. All application material must be sent to the organizing committee chair, professor Camilo Ortiz-Astorquiza at camiloortiz@javeriana.edu.co

Important Information

  • Modalidad:
  • Initial date:
  • End date:
  • Duration:
    35 hours
  • Schedule:
    9am - 5pm
  • Registration:
    $595.000 COP
  • **For more information on how to validate 2 academic credits for PUJ students please contact your program coordinator. The price of registration will vary according to the credits price.


4% for early registration, valid if paying 30 days before the starting date of the course (cumulative with other discounts).

10% for alumni and current students of the following Universities: Pontificia Universidad Javeriana, Universidad de los Andes and Universidad de La Sabana. Also, applicable to current members of ASOCIO.

ASOCIO will cover the registration fee for one participant among those members that would like to be part of the Summer School experience. Those interested in this opportunity must express their interest when sending you application dossier for the short presentation or poster sessions. The criteria for the selection will be published in the next few weeks.

With the support of