Courses & Talks


Mario Guajardo
NHH (Norway)
Benefits sharing in collaborative logistics by cooperative game theory.

Collaboration among different agents (companies, players) is an effective way to improve logistic operations. Improvements include economic and environmentalaspects, such as cost savings and reduction of CO2 emissions. The current state-of-the-art offers collaborative approaches for a variety of well-known problems such as the classic transportation problem, the traveling salesman problem, and inventory pooling. Crucial challenges arising in these problems are how should the agents share the benefits of the collaboration, how to ensure that all the agents have no incentives to deviate from the collaboration, and how to find suitable groups of collaborators. A current trend is to study such collaborative situations as cooperative games. In this mini-course we will overview concepts, models, methods and applications in the intersection of logistics and cooperative game theory. These include, for example, the use of linear programming to model stability conditions, the design of cost allocation methods, and the use of integer programming to group players in coalitions.


Antonio Alonso Ayuso
Universidad Rey Juan Carlos (Spain)
Risk management in planning of natural resource operations: an approach based on stochastic programming.

In this talk I will review my latest publications in the field of stochastic programming in the management of natural resources under uncertainty (basically, price and demand). In particular, the impact of including risk measures in the solutions proposed by the mathematical optimization models will be analyzed. The problems analyzed are: mining operations (deciding the exploration sequence of a mine when there is uncertainty in the price of copper) and forestry operations (to decide the logistics network for access to forests when there is uncertainty in the price and the wood demand). Different risk policies and various levels of decision (strategic and tactical) will be analyzed.


Jordi Castro
Universitat Politècnica de Catalunya (Spain)
Optimization and Support Vector Machines

In this short course we will provide the basic optimization theory behind the  tool known as "support vector machine" in the field of machine learning or  data science. The outline of the course is:- The primal formulation of support vector machines (SVM) as a  convex optimization problem.- The concept of kernel.- KKT optimality conditions of the SVM- The dual of the SVM.- Solution approaches for the primal and dual formulation of SVMs.


Guillermo Duran
University of Buenos Aires, Argentina, CONICET (Argentina), University of Chile, Chile
Successful Operations Research applications from the academy in Chile and Argentina in the last 15 years

We present in this mini-course several projects carried out from the University of Buenos Aires (Argentina) and the University of Chile (Chile) in the last 15 years of OR applications to auctions, logistics, transport, public management, and sports problems, among other sectors of application.The mathematical techniques used, the impact obtained, the main difficulties and the reasons for the success of the different projects are shown.