Luis Aburto

Luis Aburto
Universidad Adolfo Ibáñez · Facultad de Ingeniería y Ciencias

Doctor of Engineering


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Citations since 2017
1 Research Item
169 Citations
I work at the Faculty of Engineering and Sciences of Universidad Adolfo Ibañez. My main research topics are machine learning, operations/ marketing and Industrial Engineering. My current project is Pricing Optimization and Category Management in the retail industry.
Additional affiliations
January 2005 - September 2018
University of Chile
  • PhD Student


Publications (6)
Purpose This paper aims to present an approach to detect interrelations among product categories, which are then used to produce a partition of a retailer’s business into subsets of categories. The methodology also yields a segmentation of shopping trips based on the composition of each shopping basket. Design/methodology/approach This work uses...
Conference Paper
Demand prediction plays a crucial role in advanced systems for supply chain management. Having a reliable estimation for a product’s future demand is the basis for the respective systems. Various forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivated the dev...
Full-text available
Demand forecasts play a crucial role for supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Several forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivates the development of hybrid systems...


Cited By


Projects (2)
NeEDS (Network of European Data Scientists) provides an integrated modelling and computing environment that facilitates data analysis and data visualization to enhance interaction. NeEDS brings together an excellent interdisciplinary research team that integrates expertise from three relevant academic disciplines, Mathematical Optimization, Visualization and Network Science, and is excellently placed to tackle the challenges. NeEDS develops mathematical models, yielding results which are interpretable, easy-to-visualize, and flexible enough to incorporate user knowledge from complex data. These models require the numerical resolution of computationally demanding Mixed Integer Nonlinear Programming formulations, and for this purpose NeEDS develops innovative mathematical optimization based heuristics.