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About

About

I´m a professional and researcher in Logistics and Supply Chain Management, especially interested in Agri-food Supply Networks. I’m currently doing a doctoral program in Engineering and Industrial Management at the Faculty of Engineering of the University of Porto (FEUP). I got my Master´s degree in Industrial Engineering in 2014 at Universidad Distrital Francisco José de Caldas (Bogota-Colombia), where I specialized in Logistics and Operations Research (OR). I´m Industrial Engineer from Fundación Universitaria Agraria de Colombia. I have been working from three fronts which are academia, research and consulting. This allows me to bring organizational practices to academic scenarios as well as to transmit, apply and validate research results in organizations and communities. My experience in the industrial and academic sectors amounts to 14 years. My research interests have a focus on the use of OR & Machine Learning techniques in the framework of Sustainable Supply Chain, especially to find optimal and resilient supply network designs that ensure economic, social and environmental benefits jointly in developing economies.

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Publications

2022

Machine Learning for Short-Term Load Forecasting in Smart Grids

Authors
Ibrahim, B; Rabelo, L; Gutierrez-Franco, E; Clavijo-Buritica, N;

Publication
ENERGIES

Abstract
A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation. However, smart grids require that energy managers become more concerned about the reliability and security of power systems. Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. This paper deals with short-term load forecasting (STLF), which has become an active area of research over the last few years, with a handful of studies. STLF deals with predicting demand one hour to 24 h in advance. We extensively experimented with several methodologies from machine learning and a complex case study in Panama. Deep learning is a more advanced learning paradigm in the machine learning field that continues to have significant breakthroughs in domain areas such as electricity forecasting, object detection, speech recognition, etc. We identified that the main predictors of electricity demand in the short term: the previous week’s load, the previous day’s load, and temperature. We found that the deep learning regression model achieved the best performance, which yielded an R squared (R2) of 0.93 and a mean absolute percentage error (MAPE) of 2.9%, while the AdaBoost model obtained the worst performance with an R2 of 0.75 and MAPE of 5.70%.

2022

A hybrid modeling approach for resilient agri-supply network design in emerging countries: Colombian coffee supply chain

Authors
Clavijo-Buritica N.; Triana-Sanchez L.; Escobar J.W.;

Publication
Socio-Economic Planning Sciences

Abstract

2021

Multi-Objective Optimization to Support the Design of a Sustainable Supply Chain for the Generation of Biofuels from Forest Waste

Authors
Gutierrez Franco, E; Polo, A; Clavijo Buritica, N; Rabelo, L;

Publication
SUSTAINABILITY

Abstract
The production and supply chain management of biofuels from organic waste as raw material has been identified as a promising strategy in the field of renewable energies and circular economy initiatives. This industry involves complex tasks such as strategic land use, feedstock purchasing, production plant location, production capacity strategy, and material flows, which can be solved by mathematical modeling. The study proposed a multi-objective mixed-integer linear programming model to design a sustainable supply chain of biofuels with forest residues from its triple function: economic, environmental, and social. The trade-offs between the proposed objectives were determined with computational results. The proposed objectives were profit maximization, CO2 minimization, and employment generation maximization. Thus, the proposed model serves as a tool for decision-making, allowing the projection of a long-term structure of the biofuel supply chains and contribute to the United Nations Sustainable Development Goals.

2019

Selection of supplier management policies using clustering and fuzzy-AHP in the retail sector

Authors
Buriticá N.C.; Matamoros M.Y.; Castillo F.; Araya E.; Ahumada G.; Gatica G.;

Publication
International Journal of Logistics Systems and Management

Abstract
Supplier development contributes to supply chain integration and performance, thus playing an essential role in any industry. In the retail market, no formal procedures exist for the selection-assignment of supplier development programs, meaning that respective management is complex and unclear given actor and requirement heterogeneities. Multi-criteria techniques are currently used for selection and evaluation processes. However, no models exist that integrate clustering and multi-criteria techniques together to efficiently select-assign supplier development programs. This study proposes a four-phase methodology - diagnosis, program design, assignment, and proposal - that considers supplier clustering through K-means and multi-criteria fuzzification. Additionally, case-study analysis of 149 retail suppliers in Colombia clustered businesses into high (8), medium (32), and low (109) risk sets, each of which was assigned tailored development programs. The obtained results support use of the proposed methodology to improve supply chain performance for organisations with many suppliers requiring development programs.