Semblance
Professor Rafael Batres teaches at the School of Engineering and Sciences at Tecnológico de Monterrey. He earned his Ph.D. from the Tokyo Institute of Technology and his bachelor's degree from the National Autonomous University of Mexico (UNAM). Before joining Tecnológico de Monterrey, he was an Associate Professor of Industrial Engineering at Toyohashi University of Technology (2005-2014) and an Assistant Professor at the Tokyo Institute of Technology (1998-2005). He also worked as a visiting scientist at the Institut Français du Pétrole.
His research focuses on computational methodologies to solve complex problems, including optimal vehicle routing, production scheduling with shared robots, production system design, energy retrofitting of buildings, machine optimization, and metamaterial design. These computational methodologies leverage algorithms such as machine learning, metaheuristic optimization, and ontologies.
Dr. Batres has supervised and co-supervised a total of 107 undergraduate, master's, and doctoral students. He has published over 50 articles in scientific journals and presented a similar number of papers at international conferences. He serves as a reviewer for several international scientific journals and has held various roles in scientific committees of different organizations and meetings.
Expertise and Skills
- Applied Artificial Intelligence
- Systems Engineering
- Modeling and Simulation
- Applied Optimization
- Surrogate Modeling
- Industrial Engineering
- Metaheuristic Optimization
- Manufacturing Systems
Field
Optimal vehicle routing, operation scheduling with shared robots, production system design, energy rehabilitation of facilities, machine optimization, metamaterial design, and safe operation generation for plant startup and shutdown.
Academic Publications
- Maximizing mechanical properties of aluminium alloys by microstructural optimisation using a coarsened surrogate model
- G-code evaluation in CNC milling to predict energy consumption through Machine Learning
- Micro Evolutionary Particle Swarm Optimization (MEPSO): A new modified metaheuristic
- MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization
- Optimization models for nopal crop planning with land usage expansion and government subsidy
- Surrogate-based optimization of microstructural features of structural materials
- Teaching ill-defined problems in engineering
- Action Research as a Way to Guide Research Projects in Engineering
- Industry 4.0 and International Collaborative Online Learning in a Higher Education Course on Machine Learning
- A Hybrid Metaheuristic Optimization Approach for the Synthesis of Operating Procedures for Optimal Drum-Boiler Startups
- Enhanced dynamic simulation approach towards the efficient mining thermal energy supply with improved operational flexibility
- Semi-automatic simulation modelling. Results with Tecnomatix Portfolio in the automotive sector
- Knowledge modelling for ill-defined domains using learning analytics: Lineworkers case
- Digital Pyramid: An approach to relate industrial automation and digital twin concepts
- A framework for the synthesis of optimum operating profiles based on dynamic simulation and a micro genetic algorithm
- Optimization of a drum boiler startup using dynamic simulation and a micro-genetic algorithm
- Implementing the simulated annealing algorithm to optimize the startup of a drum boiler
- Prediction interval adjustment for load-forecasting using machine learning