Pablo Cortés

Professor

Universidad de Sevilla

Spain

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Professor Pablo Cortés is a faculty member at Universidad de Sevilla in Spain. His research interests encompass water distribution networks, last-mile logistics, energy resource optimization, and manufacturing processes. Recent publications include methodologies for incident detection in water networks, efficiency measurement in logistics, and the application of machine learning in manufacturing lead-time prediction.

Articles (14)

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2025

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2024

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2023

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2020

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2020

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2020

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2020

Collaborators (2)

José Guadix

Profesor Titular de Universidad

Universidad de Sevilla

SPAIN

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Luis Onieva

Full Professor

Universidad de Sevilla

SPAIN

View details

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Pablo Cortés

Research Interests

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Articles (14)

A methodology for incident detection in sectorized water distribution networks based on pressure and flow data

Measuring couriers individual efficiency in last-mile logistics

A production-inventory model to optimize the operation of distributed energy resource networks in a rolling horizon

Investment strategies to maintain the state of water networks

Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach

Solving the picker routing problem in multi-block high-level storage systems using metaheuristics

Prediction of pipe failures in water supply networks for longer time periods through multi-label classification

Flexible Job Shop Scheduling Problem with Fuzzy Times and Due-Windows: Minimizing Weighted Tardiness and Earliness Using Genetic Algorithms

Multi-objective grouping genetic algorithm for the joint order batching, batch assignment, and sequencing problem

The one container drayage problem with soft time windows

Using IoT data and applications to improve port-based intermodal supply chains

Aplicación de la regresión logística para la predicción de roturas de tuberías en redes de abastecimiento de agua

Near-optimal operation of the distributed energy resources in a smart microgrid district

Prediction of pipe failures in water supply networks using logistic regression and support vector classification

Collaborators (2)

José Guadix

Luis Onieva

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