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Héctor Fabio Bonilla Londono

PhD Student

Centro de Estudos de Gestão do Instituto Superior Técnico

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Héctor Fabio Bonilla Londoño is a PhD student in Engineering Management at IST, CEGIST, 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

Projects

WSmart Route+ : Towards a Smart Waste Collection Route Planning System

The WSmart Route+ project studies a new paradigm on smart waste management. Data analytics combined with route optimization will be explored to improve waste collection operation supporting the adoption of a smart waste collection based on dynamic routes as opposed to the traditional blind collection that is based on static routes. Everyday thousands of kilometres are travelled to collect tonnes of waste with high uncertainty on the amount of waste in each bin, a fact that can lead to inefficient collection operations where resources are used to collect often a small amount of waste.