Our Publications
Showing 52 of 52 publications
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.
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/
A Composed and Derivative-Augmented Framework for Forecasting Intermittent Demand in Spare Parts Logistics
Aliu, D., Gomes, N., & Rocha, E. (2026). A Composed and Derivative-Augmented Framework for Forecasting Intermittent Demand in Spare Parts Logistics. Manuscript submitted for publication in the DII26 Conference Proceedings.
A Data-Driven Framework for Early Sequential Feature Selection using Belief Propagation
J.D. Martins; E. Rocha; D. Costa. (2025) “A Data-Driven Framework for Early Sequential Feature Selection using Belief Propagation”. International scientific conference (DII26 / accepted submission). Status: accepted.
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.
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
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.
A Meta-Ranking Framework for Adaptive Model Selection in Time Series Forecasting
João Sousa. (2026) “A Meta-Ranking Framework for Adaptive Model Selection in Time Series Forecasting”. DII26 — Data-Driven Innovation in the Industry, Aveiro, 3–7 June 2026. Status: conference submission reported in the project document.
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
A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System
Â.F. Brochado; E.M. Rocha; D. Costa. (2024) “A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System”. Sustainability, 16, 742. Status: published.
A new parametric information-gain criterion for tree-based machine learning algorithms
D. Costa; V.V. Costa; E.M. Rocha. (2025) “A new parametric information-gain criterion for tree-based machine learning algorithms”. PeerJ Computer Science. Status: published.
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
A Parametric Information-Gain Framework for Streaming Fault Prediction in Assembly Lines
V. Costa; D. Costa; B. Veloso; E.M. Rocha. (2025) “A Parametric Information-Gain Framework for Streaming Fault Prediction in Assembly Lines”. International scientific conference (DII26 / accepted submission). Status: accepted.
A Parametric Information-gain to Improve Online Tree-based Machine Learning Models
V. Costa; D. Costa; B. Veloso; E.M. Rocha. (2025) “A Parametric Information-gain to Improve Online Tree-based Machine Learning Models”. SSRN preprint; submitted to journal. Status: under evaluation.
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
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.
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.
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
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
Belief Propagation for Early Sequential Feature Selection in Manufacturing
Joana Martins; D. Costa; E. Rocha. (2026) “Belief Propagation for Early Sequential Feature Selection in Manufacturing”. ENBIS-26 abstract, submitted on 6 April 2026 and accepted. Status: accepted abstract.
Belief Propagation for Early Sequential Feature Selection in Manufacturing
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
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.
Complexity-Aware Conformal Classification for Timely Industrial Fault Prediction
D. Costa; V. Costa; E.M. Rocha. (2025) “Complexity-Aware Conformal Classification for Timely Industrial Fault Prediction”. International scientific conference (DII26 / accepted submission). Status: accepted.
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.
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
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.
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
EDATA: Automated Exploratory Data Analysis with Conformal Prediction for Industrial Applications
V. Costa; D. Costa; et al. (2025) “EDATA: Automated Exploratory Data Analysis with Conformal Prediction for Industrial Applications”. Procedia Computer Science 253, 2615–2624; 6th International Conference on Industry 4.0 and Smart Manufacturing. Status: published.
Edge-Deployed Computer Vision for Glove-Aware Assembly Validation in Industrial Environments
D. Pereira; D. Costa; A. Carvalho; E.M. Rocha. (2025/2026) “Edge-Deployed Computer Vision for Glove-Aware Assembly Validation in Industrial Environments”. International scientific conference submission. Status: accepted/submitted.
Enhancing Conformal Prediction Efficiency via Multiscale Impurity Analysis: A Complexity-Aware Non-Conformity Function
V. Costa; D. Costa; E.M. Rocha. (2025) “Enhancing Conformal Prediction Efficiency via Multiscale Impurity Analysis: A Complexity-Aware Non-Conformity Function”. SSRN preprint; submitted to journal. Status: under evaluation.
Estimating data complexity and drift through a multiscale generalized impurity approach
D. Costa; E.M. Rocha; N. Ferreira. (2024) “Estimating data complexity and drift through a multiscale generalized impurity approach”. Journal of Computational Mathematics and Data Science, vol. 12. Status: published.
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
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
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
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.
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
Gossip Learning architecture for decentralized industrial analytics
Project 3 team. (2026) “Gossip Learning architecture for decentralized industrial analytics”. International scientific conference submission referenced in S25.12. Status: in preparation/accepted.
Low-cost and non-intrusive human digital twin component for task and navigation tracking through pose estimation
D. Costa; D. Pereira; Â.F. Brochado; E.M. Rocha. (2025) “Low-cost and non-intrusive human digital twin component for task and navigation tracking through pose estimation”. CIRP Journal of Manufacturing Science and Technology. Status: published.
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.
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.
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.
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.
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.
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
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
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.
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.
Processes Reconstruction for Workflow Analysis and Bottleneck Detection
Cristiano Nicolau. (2026) “Processes Reconstruction for Workflow Analysis and Bottleneck Detection”. DII26 — Data-Driven Innovation in the Industry, Aveiro, 3–7 June 2026. Status: conference submission reported in the project document.
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.
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
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.