![](/sites/default/files/styles/scale_and_crop_100x100/public/pictures/2024-07/HectorBonilla_2.png?itok=zp0uMszb)
Héctor Fabio Bonilla Londono
PhD Student
Centro de Estudos de Gestão do Instituto Superior Técnico
Héctor Fabio Bonilla Londoño is a PhD student in Engineering Management at IST, CEG-IST, Instituto Superior Técnico, University of Lisbon, commencing his studies in April 2024 with a scholarship for the WSmart Route+ project. This initiative explores smart waste management through the integration of data analytics and Operational Research techniques. Supervised by Professors Tânia Ramos and Ana Póvoa, his PhD research focuses on Optimization Models and Methods for Dynamic and Stochastic Vehicle Routing in Waste Collection.
Héctor holds an MSc in Production Engineering at Federal São Carlos University, Brazil. His award-winning master’s thesis on humanitarian logistics resulted in the publication "Building disaster preparedness and response capacity in humanitarian supply chains using the Social Vulnerability Index," which was honored as the "Best EJOR Paper 2023." He also earned bachelor’s degrees in applied mathematics (2015) and Industrial Engineering (2014) at Pontifical University Javeriana, Colombia. With over five years of experience as an assistant professor in Operational Research and Operations Management. Héctor specializes in mathematical optimization, Python, and optimization software such as GAMS and AMPL, along with solvers like GUROBI and CPLEX. His research interests encompass Vehicle Routing, Waste Collection, Robust Optimization, Stochastic Programming, and Simulation Methods
Research Groups
OpLog – Operations, Logistics and Supply Chain Management
Projects
WSmart Route - Waste Collection Based on a Real Time Route Planning System
The WSmart Route project explores a new paradigm based on smart waste management, where real-time information plays a central role in changing the way operations are managed today. Recent advances in ICT make it possible to access information in real time through the use of sensors located inside the containers. However, access to the information collected by these sensors is not sufficient to increase efficiency in the waste collection operation by itself, making it essential to optimize the collection routes according to that information.