Our Publications
Showing 35 of 35 publications
(W2.1) A Big Data-driven Approach to Support Continuous Improvement Initiatives and Decision-Making in 4.0 Companies
Brochado, Â. F. (2025) “A Big Data-driven Approach to Support Continuous Improvement Initiatives and Decision-Making in 4.0 Companies". Universidade de Aveiro. Departamento de Economia Gestão Engenharia Industrial e Turismo. Doutoramento em Engenharia e Gestão Industrial.
(W2.1) A big data-driven system for smart continuous improvement
Brochado, Â. F., Rocha, E. M., & Pimentel, C. (2025) “A big data-driven system for smart continuous improvement”. Discover Applied Sciences. http://dx.doi.org/10.1007/s42452-025-07802-8.10.1007/
(W2.1) A Hybrid Analytics Framework for Seaport Equipment Efficiency Monitoring: Integrating MEA, Visual Analytics, and LLM-Driven Decision Support
Ribeiro, L., Kazmi, S., Brochado, Â. F. & Rocha, E. (2026). A Hybrid Analytics Framework for Seaport Equipment Efficiency Monitoring: Integrating MEA, Visual Analytics, and LLM-Driven Decision Support. Manuscript to be submitted in July 2026 to a Journal.
(W2.1) A mathematical framework for assessing disruptions in maritime logistics operations
Raza, A., Rocha, E. M., Brochado, A. F., & Mohsin, M. (2026). A mathematical framework for assessing disruptions in maritime logistics operations. Procedia Computer Science, 277, 3710–3720, DOI: https://doi.org/10.1016/j.procs.2026.02.406
(W2.2) A Meta-Learning-Based Dynamic Ensemble Framework for Time-Series Forecasting under Expanding Window Evaluation
Bukhari, S., Brochado, Â. F., & Rocha, E. (2026). A Meta-Learning-Based Dynamic Ensemble Framework for Time-Series Forecasting under Expanding Window Evaluation. Manuscript submitted for publication in the DII26 Conference Proceedings.
(W5.4) A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System
Brochado, Â. F., Rocha, E. M., & Costa, D. (2024). A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System. Sustainability, 16(2), 742. https://doi.org/10.3390/su16020742
(W2.1) A nonlinear multi-directional efficiency framework for modeling operational and environmental inefficiencies in sustainable port logistics,
Kazmi, S., Rocha, E., & Brochado, Â. F. (2026) “A nonlinear multi-directional efficiency framework for modeling operational and environmental inefficiencies in sustainable port logistics,” Research in Transportation Business & Management, Volume 67, 2026, 101722, ISSN 2210-5395, https://doi.org/10.1016/j.rtbm.2026.101722
(W2.1) Accurate Estimates of Drayage Dwell Times
Lima, A., Rocha, E., Macedo, P., & Madaleno, M. (2026). Accurate Estimates of Drayage Dwell Times. In Springer Proceedings in Mathematics & Statistics (pp. 783-793). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-00914-2_53
(W2.1) AMNN-Based Municipal Efficiency: Constrained Deep Learning for Regional Policy
Kazmi, S., Rocha, E. M., & Brochado, Â. F. (2026). AMNN-Based Municipal Efficiency: Constrained Deep Learning for Regional Policy. Manuscript submitted for publication in a Journal.
(W2.1) An Adaptive Non-Linear Model for Real-Time Logistics Synchronization
Iglesias, A., Brochado, Â. F., & Rocha, E. (2026). An Adaptive Non-Linear Model for Real-Time Logistics Synchronization. Manuscript submitted for publication in the DII26 Conference Proceedings.
(W5.4) Análise de Eficiência Aplicada à Otimização de Rotas em Transporte Multimodal
Pascoal, R. (2025). Universidade de Aveiro. Mestrado em Ciências de Dados.
Supervisor(s): E. Rocha
(W2.1) Aprimoramento da Análise de Eficiência Multidirecional por meio de Modelação Não-Linear
Kazmi, S. (2024). Universidade de Aveiro. Mestrado em Matemática e Aplicações.
Supervisor(s): E. Rocha
Grade: 20 in 20
(W2.1) Breaking the Linear Barrier: A Deep Learning Approach to Nonlinear Efficiency in Ports
Kazmi, S. M., Rocha, E. M., & Brochado, Â. F. (2025). Breaking the Linear Barrier: A Deep Learning Approach to Nonlinear Efficiency in Ports. In 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS) (pp. 727-732). IEEE. https://doi.org/10.1109/amlds63918.2025.11159469
(W2.1) Clustering and machine-learning anomaly detection technique of count time series
Sousa, L., Monteiro, M., Pereira, I., & Rocha, E. (2026). Clustering and machine-learning anomaly detection technique of count time series. Manuscript submitted for publication in the DII26 Conference Proceedings.
(W2.1) Convergence and Correctness of Belief Propagation for Nonlinear Programming via Sequential Linearization
Khan, M., & Rocha, E. (2026). Convergence and Correctness of Belief Propagation for Nonlinear Programming via Sequential Linearization. Manuscript submitted for publication in a Journal.
(W2.1) Diagnostic and Prescriptive Insights through XGBoost, SHAP and Differential Evolution: Application to Last-Mile Delivery in Portugal
Addo, E., Brochado, Â. F., & Rocha, E. M. (2025). Diagnostic and Prescriptive Insights through XGBoost, SHAP and Differential Evolution: Application to Last-Mile Delivery in Portugal. In 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS) (pp. 558-563). IEEE. https://doi.org/10.1109/amlds63918.2025.11159384
(W2.1) Discovering latent structures in container logistics operations: a distributional clustering approach with high-density region
Lima, A., Rocha, E., Madaleno, M., Macedo, P. (2026). Discovering latent structures in container logistics operations: a distributional clustering approach with high-density region. Submitted in 2025.
(W2.1) Distribution-Focused Clustering for Revealing Patterns in Container Logistics
Lima, A., Rocha, E., Madaleno, M., Macedo, P. (2026). Distribution-Focused Clustering for Revealing Patterns in Container Logistics. In: Mizuyama, H., Morinaga, E., Nonaka, T., Kaihara, T., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Cyber-Physical-Human Production Systems: Human-AI Collaboration and Beyond. APMS 2025. IFIP Advances in Information and Communication Technology, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-032-03515-8_18
(W2.1) Evaluation of seaport equipment performance using multi-directional efficiency analysis for sustainable logistics operations: A case from Portugal
Kazmi, S. M., Rocha, E. M., & Brochado, Â. F. (2025). Evaluation of seaport equipment performance using multi-directional efficiency analysis for sustainable logistics operations: A case from Portugal. Research in Transportation Business & Management, 62, 101442. https://doi.org/10.1016/j.rtbm.2025.101442
(W5.4) Federated/Gossip Learning applied to Multimodal Container Transportation
Liebau, N. (2025). Mestrado em Ciências de Dados, Universidade de Aveiro.
Supervisor(s): E. Rocha
Grade: 17 in 20
(W2.1) Ferramenta de Apoio à Decisão para Análise das Causas-raiz utilizando SHAP, PDP e ICE
Cativa, M. (2025). Universidade de Aveiro. Mestrado em Matemática e Aplicações.
Supervisor(s): E. Rocha
(W2.1) Fuzzy Bootstrapped Multi-directional Efficiency Analysis with Dominance-Based Ranking
Kazmi, S., Rocha, E. M., & Brochado, Â. F. (2026). Fuzzy Bootstrapped Multi-directional Efficiency Analysis with Dominance-Based Ranking. Manuscript submitted for publication in a Journal.
(W2.1) General Prescriptive System for Smart KPI Management: Application in Container Terminal Operations
Rocha, E. M., & Brochado, Â. F. (2025). General Prescriptive System for Smart KPI Management: Application in Container Terminal Operations. In 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS) (pp. 595-600). IEEE. https://doi.org/10.1109/amlds63918.2025.11159435
(W2.1) Mathematical Approaches to Supply Chain Disruption: A Review of Metrics, Techniques, and Applications Across Logistics Modes
Brochado, Â. F., Mohsin, M., Rocha, E. M., Raza, A. (2026). Mathematical Approaches to Supply Chain Disruption: A Review of Metrics, Techniques, and Applications Across Logistics Modes. Manuscript submitted for publication in a Journal.
(W2.1) Mathematical Modeling of Maritime Logistics Disruptions with Logistic Growth and Nonlinear Incidence Dynamics
Mohsin, M., Rocha, E. M., Raza, A., Brochado, Â. F. (2026). Mathematical Modeling of Maritime Logistics Disruptions with Logistic Growth and Nonlinear Incidence Dynamics. Manuscript submitted for publication in a Journal.
(W2.2) Meta-Ranking Algorithms for Sequential Model Selection in Real-World Time Series Forecasting
Sousa, J., & Rocha, E. (2026). Meta-Ranking Algorithms for Sequential Model Selection in Real-World Time Series Forecasting. Manuscript to be submitted in July 2026 to a Journal.
(W2.1) Optimization of Logistics Operations based on the Convergence and Correctness of Belief Propagation for Nonlinear Programming via Sequential Linearization
Khan, M., & Rocha, E. (2026). Optimization of Logistics Operations based on the Convergence and Correctness of Belief Propagation for Nonlinear Programming via Sequential Linearization. Manuscript submitted for publication in the DII26 Conference Proceedings.
(W5.4) Optimizing Fleet Deployment: Real-Time Truck Scheduling Using Non-Linear Dynamics
Iglesias, A., Brochado, Â., & Rocha, E. (2026). Optimizing Fleet Deployment: Real-Time Truck Scheduling Using Non-Linear Dynamics. Manuscript to be submitted in June 2026 to a Journal.
(W2.1) Otimização e Gestão Inteligente de KPIs: Caso Prático sobre Operações Logísticas e Portuárias do Porto de Sines
Esteves, L. (2025). Universidade de Aveiro. Mestrado em Matemática e Aplicações.
Supervisor(s): E. Rocha
Grade: 17 in 20
(W5.4) Performance Evaluation and Explainability of Last-Mile Delivery
Brochado, Â. F.; Rocha, Eugénio; Addo, Emmanuel; Silva, Samuel. (2024) “Performance Evaluation and Explainability of Last-Mile Delivery”. Procedia Computer Science 232, 2478-2487. http://dx.doi.org/10.1016/j.procs.2024.02.067
(W2.1) Predictive Modeling of MEA Scores Using Constrained Deep Learning: A Comparative Study with Machine Learning Baselines
Kazmi, S., Rocha, E. M., & Brochado, Â. F. (2026). Predictive Modeling of MEA Scores Using Constrained Deep Learning: A Comparative Study with Machine Learning Baselines. Manuscript submitted for publication in a Journal.
(W2.1) Predictive Modeling of Port Operational Performance Using ML Models: A Data-Driven Analysis of Vessel Turnaround Time, Container Dwell Time, and Daily Throughput Volume
Zafar, M., Rocha, E., & Brochado, Â. F. (2026). Predictive Modeling of Port Operational Performance Using ML Models: A Data-Driven Analysis of Vessel Turnaround Time, Container Dwell Time, and Daily Throughput Volume. Manuscript to be submitted in July 2026 to a Journal.
(W2.1) Risk analysis in maritime logistics amid global crises: a systematic review of mathematical models, empirical validation, and policy implications
Khan, M., Brochado, Â. F., & Rocha, E. (2026). Risk analysis in maritime logistics amid global crises: a systematic review of mathematical models, empirical validation, and policy implications. Manuscript to be submitted in July 2026 to a Journal.
(W5.4) Sistema de explicabilidade mista aplicado a entregas de mercadorias na última milha
Addo, E. (2024). Universidade de Aveiro. Mestrado em Matemática e Aplicações.
Supervisor(s): E. Rocha
Grade: 18 in 20
(W2.1) The Computability Gap in Sustainable Supply Chain Metrics: A Systematic Review and Taxonomy
Brochado, Â. F., Esteves, L., Lourenço Marques, J., & Rocha, E. M. (2026). The Computability Gap in Sustainable Supply Chain Metrics: A Systematic Review and Taxonomy. Manuscript submitted for publication in a Journal.