José Luís Borges

Domains

Industrial Engineering and Management Artificial Intelligence Systems Engineering and Management

Projects

opti-MOVES

The project combined diagnosis and optimization methods to improve the quality of urban public transport, particularly intermodal transport services. The system aims to support decisionmaking of mobility operators and authorities, in planning, monitoring and optimizing intermodal operations and in the definition of urban policies. Positive impacts are expected, with an increase of public transport utilization, and a reduction of energy costs and pollutant emissions.

Quality management of intermodal public transport services: diagnosis and optimization

Publications

A Data-Driven Monitoring Approach for Diagnosing Quality Degradation in a Glass Container Process

Oliveira, MA;Guimaraes, L;Borges, JL;Almada-Lobo, B;

2024

MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE, LOD 2023, PT I

Data mining of user navigation patterns

Borges, J;Levene, M;

2000

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A Heuristic to Capture Longer User Web Navigation Patterns

Borges, J;Levene, M;

2000

Electronic Commerce and Web Technologies - Lecture Notes in Computer Science

Condorcet versus Borda, a response to: Comment on “A New Method to Obtain a Consensus Ranking of a Region's Vintages' Quality”

Borges, J;Real, AC;Sarsfield Cabral, J;Jones, GV;

2012

Journal of Wine Economics - J Wine Econ

View all publications

Supervised theses

Inteligência Artificial Generativa Aplicada na Optimização do Serviço de Cliente para Pequenas Empresas

Gonçalo de Lima Gomes e Boa-Nova

M - 2024

UP-FEUP

Centres

Industrial Engineering and Management

The Centre for Industrial Engineering and Management (CEGI) is internationally recognised in the fields of decision science, optimisation, operations management and service engineering. The mission is to support organisations and communities in decision-making by harnessing the power of data to develop robust and sustainable solutions to complex problems. Research at CEGI focuses on real-world challenges like transport planning, supply chain management, healthcare service networks, industrial operations optimisation, disaster response and recovery, and the impact assessment of public policies. By resorting to Artificial Intelligence, data science and mathematical modelling, CEGI aims to understand system behaviours, improve process efficiency, increase organisational resilience, and anticipate the effects of decisions in uncertain contexts. CEGI operates across critical sectors including mobility, healthcare, retail, industry, energy, and services, focusing on digital transition and sustainability. We collaborate with academia, companies, municipal authorities, hospitals, and regulators, transforming scientific knowledge into applied solutions. We believe that evidence-based management is key to tackling the challenges of a constantly changing world.