ELAVIO

José-Fernando Camacho-Vallejo

Tecnológico de Monterrey, Mexico

Mini-course

Bilevel Optimization: Foundations, Solution Methods, and Emerging Research Directions

Abstract

Bilevel optimization provides a mathematical framework for modeling hierarchical decision-making processes involving two interacting decision makers with conflicting or sequential objectives. Over the past decades, it has evolved from a theoretical topic into an active research area with applications in logistics, transportation, energy systems, pricing, machine learning, and many other domains.

This minicourse introduces the fundamental concepts of bilevel optimization, including its mathematical structure, computational complexity, and modeling characteristics. Classical exact solution approaches based on reformulations, optimality conditions, and decomposition techniques are presented, followed by an overview of metaheuristic algorithms designed for large-scale and computationally challenging problems. Finally, the course discusses several emerging research directions, including multiobjective and semivectorial bilevel optimization, as well as the integration of machine learning techniques within optimization algorithms through Learning for Optimization. The objective is to provide participants with both a solid introduction to the field and a perspective on current research challenges and future opportunities.

Short bio

José-Fernando Camacho-Vallejo is an Associate Research Professor at the Industrial and Systems Engineering Department in Tecnológico de Monterrey.

Fernando is a Level II member of the Mexican System of Researchers and a regular member of the Mexican Academy of Sciences. Additionally, he served as the president of the Mexican Society of Operations Research from 2020 to 2022. He is chair of the Latin-Iberoamerican Association of Operations Research (ALIO) Working Group on Bilevel Optimization, co-chair of the IEEE Task Force on Bilevel Evolutionary Optimization, the current Vice President of ALIO, and member of the Administrative Committee of the International Federation of Operational Research Societies (IFORS).

He is Area Editor for “Operations Research: Deterministic Models” at the prestigious journal Computers & Industrial Engineering. He has supervised 7 PhD dissertations, 13 master’s theses, and 4 postdoctoral research stays.

His main research interests include the mathematical modeling of hierarchical decision systems and algorithm design.

Rosa G. González Ramírez

Universidad de los Andes, Chile

Mini-course

Applications of Operations Research and Analytics in Port Management and Maritime Transportation

Abstract

This course provides an introduction to the management and planning of port operations and maritime freight transportation, emphasizing the role of optimization models, algorithms, and data analytics as decision-support tools for improving resource utilization, process coordination, and workforce management in cargo-handling operations.

The session begins with an integrated overview of the port logistics system, introducing its main stakeholders, their hierarchical relationships, and the planning processes carried out at the strategic, tactical, and operational levels. It also reviews the most significant planning and scheduling problems addressed in the scientific literature, together with the principal modeling and solution approaches developed to tackle these challenges.

The course includes several case studies conducted in collaboration with ports across Latin America, with particular emphasis on Chilean ports. These real-world applications demonstrate how optimization and analytical methods have been successfully employed to address practical planning and operational challenges, highlighting their impact on improving efficiency, coordination, and overall system performance.

The final part of the course explores current research trends and emerging challenges in port operations and maritime transportation. Special attention is given to decision-making problems involving multiple stakeholders with potentially conflicting objectives, modeled through bilevel optimization frameworks. The methodological challenges associated with these models, as well as promising directions for future research, will be discussed, illustrating how advanced OR techniques can contribute to more effective and sustainable port and maritime logistics systems.

Short bio

Rosa Guadalupe González Ramírez is an Associate Professor in the Faculty of Engineering and Applied Sciences at the Universidad de los Andes Chile. She is also a Principal Investigator and Research Line Coordinator at the Advanced Center for Transportation, Logistics and Economic Competitiveness (CATLEC), a recently funded research center conformed by the Pontificia Universidad Católica de Chile, Universidad Andrés Bello, Universidad de Concepción and the Universidad de los Andes Chile.

She holds a Ph.D. in Engineering Sciences from the Tecnológico de Monterrey, an M.S. in Industrial Engineering from Arizona State University, and an M.S. in Quality and Productivity Systems from Tecnológico de Monterrey. She earned her B.S. in Industrial Engineering from the Instituto Tecnológico de Morelia.

Her research focuses on port logistics, where she develops optimization and simulation models to support operational planning and resource allocation. Her work also includes empirical research on port performance, inter-organizational information systems and port competitiveness as well as operations planning in agri-food supply chains, last-mile logistics, and macro-logistics.

Professor González Ramírez has served on research evaluation panels for Chile's National Agency for Research and Development (ANID) and has been the secretary of several international OR societies, including the Chilean Institute for Operations Research, the Mexican Society for Operations Research (SMIO), and the Ibero-American Society of Operations Research (ALIO). She is also part of the board of the Mexican Logistics Association. She currently serves as General Coordinator of the ALIO Working Group on Maritime & Port Logistics, is a member of the Coordinating Committee of the EURO Working Group on Maritime & Port Logistics, and member of the Steering Committee of GLOW (Global Leadership in OR for Women), an initiative promoted by IFORS.

She also serves on the editorial boards of several international journals, including International Journal of Operational Research, WMU Journal of Maritime Affairs, Supply Chain Analytics, Sustainability Analytics and Modelling, Journal of Advanced Transportation, Decision Analytics Journal, and IEEE Open Journal of Intelligent Transportation Systems. In addition, she has been a Guest Editor for special issues in leading journals such as Computers & Industrial Engineering, Transportation Research Part E, and Annals of Operations Research.

Elena Valentina Gutiérrez

Universidad del Valle, Colombia

Talk

From Data to Decisions: Integrating Analytics and Operations Research in Healthcare

Abstract

The rapid digitalization of healthcare is generating unprecedented opportunities to use data for improving decision-making. However, descriptive, diagnostic, predictive, and prescriptive analytics are often developed independently, creating a gap between the ability to understand and anticipate healthcare needs and the ability to determine and implement effective actions. Addressing this gap requires greater integration between data analytics and Operations Research (OR), particularly in complex healthcare systems characterized by uncertainty, resource constraints, and interdependent decisions.

This presentation explores this challenge through three case studies representing different levels of analytical maturity. The first examines descriptive analytics in home health care, illustrating how fragmented data can provide a foundation for understanding demand and system performance. The second focuses on predictive analytics for cervical cancer diagnosis, highlighting the challenges of translating predictive information into actionable decisions. The third considers prescriptive analytics for the assessment of primary health care accessibility in rural areas, considering population vulnerability. Together, these cases illustrate the opportunities and limitations of developing analytical capabilities in isolation and motivate the need to connect them within a coherent decision-making process.

Based on these experiences, a conceptual and methodological framework is proposed to facilitate the integration of descriptive, predictive and prescriptive analytics in healthcare. Rather than viewing analytics and OR as separate methodological domains, this perspective seeks to connect data, prediction, optimization, and decision-making, creating opportunities for research that is both methodologically rigorous and relevant to real-world healthcare challenges.

Short bio

Elena Valentina Gutiérrez is a Full Professor and Director of Graduate Programs in School of Industrial Engineering at Universidad del Valle, Cali (Colombia). She holds a Ph.D. and M.Sc. in Industrial Engineering, and a B.S. in Industrial Engineering from Universidad del Valle. Dr. Gutiérrez-Gutiérrez currently serves as president of the Latin Ibero-American Association of Operations Research (ALIO, 2025-2026), she was also vice-president (ALIO, 2022-2024), and co-founder of the ALIO Working Group in Healthcare. Elena Valentina was president of the Colombian Association of Operations Research (ASOCIO, 2020-2023), and she was also co-founder of the ASOCIO Working Group in Healthcare in 2019.

Cristóbal Miralles

Universitat Politècnica de València, Spain

Talk

Ethical dilemmas of optimization: impacts to evaluate and pending challenges.

Abstract

Contemporary Operations Research is at a crossroads where algorithmic rigor can no longer be separated from social and environmental responsibility. Traditionally focused on technical efficiency and resource optimization, the discipline often assumes a pretended neutrality that ignores the deep ethical implications of its models. This conference invites young researchers to question the foundations of their analytical practice, examining how allocation, planning, and decision-making algorithms directly and sometimes detrimentally influence vulnerable communities and stakeholders.

Through practical cases and real-world dilemmas, we will explore the hidden risks in the massive use of data, as well as the implicit biases and moral issues raised by the algorithmic opacity characteristic of Artificial Intelligence. Furthermore, we will reflect on the active role to be adopted by systems analysts: moving from being mere technical executors to becoming critical agents capable of anticipating negative externalities, questioning constraints and objectives, and advocating for justice and common sense in solution design.

Ultimately, the session proposes rethinking optimization and conceiving it as a deeply human act, where true excellence in Operations Research means balancing efficiency with the dignity of the people affected by our decisions.

Short bio

Cristóbal Miralles holds a PhD in Industrial Engineering, is a Full Professor at the Universitat Politècnica de València (UPV), and a regular guest speaker at other international universities. He teaches in four languages and served for five years as Chair and head of the EURO Working Group "Ethics and OR"—a position he left in 2016 to serve to this day as coordinator of the CEDAT Foundation within the Vice-Rectorate for Social Responsibility of the UPV, and over the last four years as Director of the Interuniversity Chair of Innovation in Social Services of the Generalitat Valenciana.

His main research line has focused on the design of inclusive systems and workstations for people with disabilities, publishing multiple works in conferences and indexed journals where he also occasionally performs editorial and evaluation duties. These include various staff allocation and rotation models and metaheuristics that combine maximum productivity with worker well-being, taking into account workers' capabilities and skills, as well as Universal Design proposals that make their disabilities invisible and enable them to hold jobs that were previously inaccessible.

Based on his experience in technology transfer and consulting projects, his focus has also expanded to include more stakeholders, defining generic models to translate social and environmental responsibility into business practice, as well as analyzing the link between sustainable strategies and good practices at the operational level. Finally, it is worth noting his interest in Education in Values, with Service-Learning (SL) experiences in various Valencian organizations, as well as case studies and moral dilemmas that he applies in the classroom with the aim of fostering ethical and critical awareness in students.

Débora Ronconi

Universidade de São Paulo, Brazil

Talk

Optimizing the Flexible Job Shop Scheduling Problem under Nonlinear Routes, Learning Effect, and Energy and Operational Efficiency

Abstract

The flexible job shop with nonlinear routes is a production environment with wide practical relevance, particularly in the current print-on-demand industry. In this setting, customers place online orders for products (books, invitations, photo albums, among others) that require printing. Since the raw material (paper) is expensive, orders sharing the same paper and printing type are grouped together, and a cutting problem is solved to accommodate the printing operations on large paper rolls, which are then printed and cut. In addition, the production of a book, for instance, includes operations such as pagination, printing, binding, and packaging. Thus, the scheduling tasks, whose printing operations have been combined, are diverse and complex. There are precedence relations between the operations of a task, but some operations are unrelated to each other and can even be processed in parallel; in general, these relations can be represented by a directed acyclic graph. In this context, production activities are organized on flexible machines to better manage the wide range of tasks demanded. Considering that other industry sectors also fit this production environment, it is important that effective and efficient solution methods be developed. In this talk, we will address recent studies in which a wide variety of special characteristics, which bring the problem closer to real-world applications, such as nonlinear routes, learning effect, and energy and operational efficiency, are considered. In each case, the properties of the problems are analyzed to support the development of mixed integer linear programming models, constructive heuristics, local searches, and metaheuristics.

Short bio

The speaker research focuses on Operations Research, particularly the development of heuristic and exact methods for scheduling problems, cutting and packing, routing, and logistics. She holds a PhD in Electrical Engineering from UNICAMP and completed a postdoctoral fellowship at the Operations Research and Financial Engineering Department of Princeton University. She is currently a full professor in the Department of Production Engineering at the Polytechnic School of the University of São Paulo. Her scientific output is indexed in the main international databases, with 1,350 citations in Web of Science, 1,446 in Scopus, and 2,315 in Google Scholar with an h-index of up to 24. This track record supports her ongoing research productivity fellowship from CNPq, the Brazilian federal government's research funding agency, in which she holds a level 1 classification. Her career includes international collaborations and plenary talks at relevant events as, for example, ALIO/EURO Workshops on Applied Combinatorial Optimization. She has supervised numerous of master's, doctoral, and undergraduate research students, as well as postdoctoral researchers. Since 2010, she has led and participated as principal investigator in research projects funded by FAPESP, the research funding agency of the state of São Paulo, Brazil, in the areas of combinatorial optimization and mathematics applied to industry, building a research line centered on international collaboration, researcher training, and the development of applied optimization methods.

Diego Rossit

Universidad Nacional del Sur, Argentina

Mini-course

Fundamentals of Multiobjective Optimization: Solution Methods and Analysis of Results

Abstract

Many problems in operations research and decision-making require the simultaneous consideration of multiple conflicting objectives. Minimizing costs, reducing environmental impacts, improving service levels, and increasing efficiency are examples of criteria that frequently need to be evaluated together. In these situations, multiobjective optimization makes it possible to identify a set of efficient solutions representing different trade-offs among the objectives under consideration. This mini-course introduces the fundamental concepts required to formulate, solve, and analyze multiobjective optimization problems. It will first present the notions of dominance, Pareto optimality, efficient solutions, and the Pareto front. It will then describe some of the most widely used solution approaches. Scalarization techniques, including the weighted-sum and epsilon-constraint methods, will be discussed, together with the basic principles of multiobjective evolutionary algorithms. The main advantages, limitations, and suitable application contexts of these approaches will also be examined. Part of the mini-course will be devoted to the analysis of computational results. Metrics for assessing the convergence, diversity, and distribution of solutions along the Pareto front will be introduced, as well as basic criteria for identifying and selecting compromise alternatives. The concepts will be illustrated through application examples drawn from urban planning and management problems, showing how multiobjective optimization can support the analysis of complex decisions.

Short bio

Diego Rossit is a full-time Adjunct Professor at Universidad Nacional del Sur, Argentina, and an Associate Researcher at Argentina’s National Scientific and Technical Research Council (CONICET). He received his degree in Industrial Engineering in 2014 and his Ph.D. in Engineering, with a focus on Operations Research, in 2018. His research focuses on optimization models and decision-support methods, particularly their application to smart cities, logistics, production planning, and other industrial engineering problems, and has resulted in more than 60 indexed publications. He has also conducted research stays at several international institutions, including the University of Málaga, the Australian National University, the University of Texas at Austin as a Fulbright Scholar, Universidad de la República, Uruguay, and Universidade Federal do ABC, Brazil.