Dr. Fernando Camacho
School of Engineering and Sciences, Tecnologico de Monterrey, Campus Monterrey, Mexico
Title: Hierarchical Decision-Making Problems: Characteristics, Solution Methods and Applications
Fernando Camacho has a bachelor’s degree in Mathematics from the Faculty of Physical-Mathematical Sciences of the Autonomous University of Nuevo León (UANL). He holds a Master of Science in Engineering with a specialization in Industrial Engineering from Arizona State University, as well as a Ph.D. in Engineering with a specialization in Industrial Engineering awarded by the Tecnologico de Monterrey. Currently, he is a full-time professor at the School of Engineering and Sciences of the Tecnologico de Monterrey at the Monterrey Campus, affiliated with the Department of Industrial Engineering. He is a member of the National System of Researchers with level II (second period) and a regular member of the Mexican Academy of Sciences. He has published more than 30 articles in journals indexed by the Journal Citation Reports (JCR) and has received over 900 citations on Google Scholar with an h-index of 15. He served as the President of the Mexican Society of Operations Research from 2020 to 2022. Additionally, for 4 years, he was the Research Coordinator at the Research Center in Physical-Mathematical Sciences, and for 8 years, he was the Coordinator of the Graduate Program in Sciences with a focus on Mathematics, both at the Faculty of Physical-Mathematical Sciences of the UANL. He has supervised 7 doctoral theses, 14 master’s theses, and 3 undergraduate theses. He has also supervised 2 postdoctoral research projects. He received an award for supervising the best Master’s thesis in the area of natural and exact sciences at UANL in 2015. His research interests lie in the resolution of operations research problems, particularly focusing on the theory and applications of bi-level programming, the design of exact methods, and heuristic techniques to solve these problems.
In real-life scenarios, hierarchical relationships between two decision makers are quite common. For instance, let’s consider a situation where an authority sets toll prices for a road network, while users have the freedom to choose their preferred routes. Another example could be an international brand opening new stores, prompting a local brand to respond by strategically locating new stores in order to maintain its market share. In these cases, an upper-level decision maker aims to optimize its own objective function by selecting a decision that constrains the decision space of the lower-level decision maker, who in turn seeks to make the best decision based on its own objective function. This hierarchical relationship results in the lower-level decision impacting either the decision space or the objective function of the upper level. To model such relationships within a decision-making process, bilevel programming models are employed. In this presentation we will discuss the characteristics and complexities associated with bilevel optimization models, as well as common solution approaches, with a particular focus on metaheuristics. Furthermore, some important applications of these models are presented and some important research directions within this topic are given.