Courses & Talks

Gerard Blanco
Universitat de Lleida (Spain)
Posibilidades de la tecnología de la información en la ganadería porcina del siglo XXI

La ganadería es una de las principales fuentes de abastecimiento de proteínas del ser humano. El crecimiento demográfico de la población mundial, que se estima en un 34% hasta el 2050, provocará una gran demanda, entrando en riesgo la sostenibilidad del actual modelo de producción.El único camino posible es la utilización de procesos más eficientes. Para conseguir este objetivo, necesitamos recoger y procesar en nuestras granjas los datos que nos permitan crear modelos de optimización que incrementen el rendimiento de los animales con el mínimo desperdicio de recursos, el mínimo impacto ambiental, y el máximo bienestar.

Rafael De Oliveira Silva
University of Edinburgh (UK)
Modelling sustainable livestock systems with examples from Brazil

In this short course, you will learn how Linear Programming techniques can be applied to solve complex problems applied to sustainable livestock production.  The course  will include examples of different modelling techniques used for cost-analysis; greenhouse gas mitigation, and land-sparing analysis at farm and regional scales.  Model elements include consideration of  lifecycle assessment, soil organic carbon sequestration, feedlot systems and minimum cost and maximum profit diet formulation problems. Such modelling can be used to inform farm-level decisions and regional policy assessment.

Emilio Carrizosa
IMUS - University of Seville (Spain)
Mathematical Optimization in Data Science

Mathematical Optimization is omnipresent in Data Science tasks such as dimensionality reduction and visualization, classification and regression.The type of Mathematical Optimization tools is wide, including nonlinear (convex or not) and mixed integer programming, in many cases of large scale.Some recent examples will be presented, mostly around the topic of Cost-sensitive Constrained Classification and Regression Models, based on (randomized) trees or Support Vector Machines. 

Victor Albornoz
Universidad Técnica Federico Santa María. (Chile)
Modelling and solving problems in agriculture by column generation.

Optimization is one of the methodologies commonly used in decision support systems and nowadays farming management methods to improve the decision-making processes combine optimization models and new technologies such as satellite imagery,information technology and geospatial tools. Information compiled from field data is used to define management zones with relatively homogeneous characteristics that permit site-specific application of agronomic practices such as crop planning, water management and to define tactical harvest plans, among others. In this talk we will present different problems in the precision agriculture framework, will introduce the column generation algorithm and will describe how this algorithmic strategy can be used to solve large instances of the problems described.

Ana Paula Barbosa Póvoa
University of Lisbon (Portugal)
Design and Planning Sustainable Supply Chains

Today organizations long-term success is built not only on profitability but also on its contribution to society. In this set organizations are pressured to design and plan their supply chains towards sustainability goals. In this seminar the concept of sustainable supply chains is discussed and the use of optimization methods as tools to support decisions on how to design and planning such systems is explored. Real case examples, studied by the Operations and Logistics Group of the Centre for Management Studies at Instituto Superior Técnico (IST), University of Lisbon are presented and analyzed. The seminar concludes in an interactive way where students should be able to identify some of the main tendencies and future challenges in the area.

José F. Oliveira
Universidade do Porto (Portugal)
Cutting and Packing: what you see is what you model

Cutting and Packing problems are hard combinatorial optimization problems that arise in the context of several manufacturing and process industries or in their supply chains. These problems occur whenever a bigger object or space has to be divided into smaller objects or spaces, so that waste is minimized. This is the case when cutting paper rolls in the paper industry, large wood boards into smaller rectangular panels in the furniture industry, irregularly shaped garment parts from fabric rolls in the apparel industry, but also the case when packing boxes on pallets and these inside trucks or containers, in logistics applications. All these problems have in common the existence of a geometric sub-problem, which deals with the small object non-overlap constraints. The resolution of these problems is not only a scientific challenge, given its intrinsic difficulty, but has also a great economic impact as it contributes to the decrease of one of the major cost factors for many production sectors: the raw-materials. In some industries raw-material may represent up to 40% of the total production costs. It has also a significant environmental repercussion as it leads to a less intense exploration of the natural resources from where the raw-materials are extracted, and decreases the quantity of garbage generated, which frequently has also important environmental impacts. In logistics applications, minimizing container and truck loading space waste directly leads to less transportation needs and therefore to smaller logistics costs and less pollution.In this talk the several Cutting and Packing problems will be characterized and exemplified, based on Gerhard Wäscher’s typology (2007), allowing non-specialists to have a broad view over the area. Afterwards, as geometry plays a critical role in these problems, the geometric manipulation techniques more relevant for Cutting and Packing problems resolution will be presented. Finally, aiming to illustrate some of the most recent developments in the area, some approaches based on mathematical programming models, for the irregular packing problem, will be described.  

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.