Chauhan V.K., Mak S., Alomari M., Casassa L.,  Parlikad A., Brintrup A. (2023) Real-time large-scale multi-tier supplier selection and order assignments with penalty and dual-sourcing, Computers and Industrial Engineering


Zhou H., Parlikad A.,Brintrup A. (2023), Data-driven maintenance priority recommendations for civil aircraft engine fleets using reliability-based bivariate cluster analysis,  Quality Engineering


Zheng G., Kong L., Brintrup A. (2023), Federated Machine Learning for Privacy Preserving Collective Supply Chain Risk Prediction,  International Journal of Production Research 


Proselkov Y., Zhang J., Liming X., Hofmann E., Choi T.Y., Rogers D., Brintrup A. (2023), Financial ripple effect in complex adaptive supply networks: an agent based model,  International Journal of Production Research 





Giannikas, V., Ledwoch, A., Stojkovic, G., Costas, P., Brintrup, A., Al-Ali, A.A.S., Chauhan, V.K. and McFarlane, D., (2022). A data-driven method to assess the causes and impact of delay propagation in air transportation systems. Transportation Research Part C: Emerging Technologies, 143, p.103862.


Kosasih, E.E., Margaroli, F., Gelli, S., Aziz, A., Wildgoose, N. and Brintrup, A., (2022). Towards knowledge graph reasoning for supply chain risk management using graph neural networks. International Journal of Production Research, pp.1-17.


Bang Xiang Yong, Alexandra Brintrup, (2022), Do Autoencoders need a bottleneck for anomaly detection? IEEE Access


Bang Xiang Yong, Alexandra Brintrup, (2022) Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection, Expert Systems with Applications, Volume 209:118196


Bang Xiang Yong, Alexandra Brintrup, (2022) Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift, Applied Soft Computing 123, 108912


Zhou H., Genez-Lopez T., Parlikad A.,Brintrup A. (2022), A decomposition algorithm for competing risk analysis, Reliability Analysis


Petchrompo S., Coit D.W., Brintrup A., Wannakrairot A., Parlikad A (2022) A review of Pareto pruning methods for multi-objective optimization, Computers & Industrial Engineering


Wang, Tao, Peng, Brintrup, Kosasih, Lu, Tang, Hu (2022) Dynamic Inventory Replenishment Strategy for Aerospace Manufacturing Supply Chain: Combining Reinforcement Learning and Multi-agent Simulation, International Journal of Production Research.  



Brockmann, N., Kosasih, E., Margaroli, F., Gelli, S., Aziz, A., Wildgoose, N. and Brintrup, A., 2022. Supply Chain Link Prediction on an Uncertain Knowledge Graph, ACM SIGKDD Explorations Newsletter


Kosasih E., Brintrup A. (2022), Reinforcement learning for safety stock optimisation, IFAC/MIM, Nantes, June 


Proselkov Y., Herrera M., Hernandez M.P., Parlikad A.K., Brintrup A.(2022), The Value of Information for Dynamic Decentralised Criticality Computation, IMS/IFAC


Brintrup A., Kosasih E., (2022), Digital Supply Chain Surveillance, IFAC/MIM, Nantes, June 


Tang W., Brintrup A. (2022) Distributed Manufacturing for Digital Supply Chain: a brief review and future challenges, IFIP APMS


Peng T., Brintrup A. (2022) Dynamic job shop scheduling based on order remaining completion time prediction, IFIP APMS




Xu L., Mak S., Brintrup A. (2021) Will Bots Take over the Supply Chain? A review of agent based approaches, International Journal of Production Economics


Kosasih E., Brintrup A. (2021), Supply Chain Link Prediction with Machine Learning: A Graph Neural Network approach, International Journal of Production Research


Andrade J., Brintrup A., Salonitis K., (2021) Key enablers for the evolution of aerospace ecosystems, Journal of Aerospace Technology and Management


Kim, T., Kipouros, T., Brintrup, A., Farnfield, J. and Di Pasquale, D., (2021). Optimisation of Aero-Manufacturing Characteristics of Aircraft Ribs, The Aeronautical Journal, doi: 10.1017/aer.2016.1


Mak, S., Xu L.,Pearce T.,  Ostromouv M,  Brintrup A. (2021),  Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing, NeurIPS


Aziz A., Kosasih E., Brintrup A. (2021) Graph Representation Learning for Predicting Hidden Links in Supply Chain Networks, International Conference on Machine Learning (ICML)


Pearce T., Brintrup A., Zhu J. (2021), Understanding Softmax Confidence and Uncertainty, Uncertainty in AI (UAI)


Zhou H., Brintrup A., Parlikad A. (2021) Module Failure Feature Detection by Cluster Analysis for Fleets of Civil Aircraft Engines, INCOM/IFAC




Fathy, Y., Jaber, M. and Brintrup, A., (2020). Learning with imbalanced data in smart manufacturing: A comparative analysis. IEEE Access, 9, pp.2734-2757.


Arora S., Brintrup A. (2021),  An empirical study of large-scale supply network effects on firm performance, Applied Network Science


Kumar V., Perera S.,  Brintrup A. (2020) The relationship between nested patterns and the ripple effect in complex supply networks, International Journal of Production Economics


Andrade J. L., Salonitis K., Brintrup A. (2020), Analysing the evolution of aerospace ecosystem development, PLOS ONE


Wichmann P., Brintrup A., Baker S., Woodall P., McFarlane D. (2020), Towards automatically generating supply chain maps from text using Deep Learning, International Journal of Production Research


Pearce T., Foong A. Y. K., Brintrup, A. (2020), Structured Weight Priors for Convolutional Neural Networks, International Conference on Machine Learning (ICML)


Yong B.X., Fathy Y., Brintrup A.(2020), Uncertainty of Likelihood Estimation with Bayesian Autoencoder for Anomaly Detection, International Conference on Machine Learning (ICML)


Proselkov Y., Herera M., Parlikad A., Brintrup A. (2020), Distributed Dynamic Measures of Criticality for Telecommunication Networks , SOHOMA/IFAC.


Yong B.X., Fathy Y., Brintrup A.(2020), Development of a Bayesian Autoencoder for detecting drift in product quality prediction, IEEE Metrology for Industry 4.0 and IoT . 


 Pearce T., Zaki, M., Brintrup, A. (2020),Uncertainty in Neural Networks: Approximately Bayesian Ensembling, 23rd International Conference on Artificial Intelligence and Statistics (AI-STATS), June 3 - 5, 2020 Palermo, Sicily, Italy.





Brintrup A., Pak J., Woodall P., Wichmann P., McFarlane D., Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing (2019), International Journal of Production Research


Wang, G., Ledwoch, A., Hasani, R.M., Grosu, R. and Brintrup, A., 2019. A generative neural network model for the quality prediction of work in progress products. Applied Soft Computing, 85, p.105683.


Brintrup A., Ledwoch A. (2018), Supply network science: Emergence of a new perspective on a classical field, Chaos 28(3):033120


Brintrup A., Wichmann P., Woodall P., McFarlane D., Krechel W., Nicks E., (2018) Predicting hidden links in supply networks, Complexity.



Brintrup A., Perera S. (2019), Identifying Interdependencies in Outsourcing Networks, IEEE Graph Computing, September 2019, California, US


Brintrup A., A framework for conceptualising Artificial Intelligence in the Supply Chain, Production and Operations Management (POMS), September 2019, Brighton, UK.


 Pearce, T., Tsuchida, R., Zaki, M., Brintrup, A., & Neely, A. (2019). Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions. In Proceedings of the 35th conference on Uncertainty in Artificial Intelligence, UAI. https://arxiv.org/abs/1905.06076 


Yong X. B., Brintrup A. (2019), An agent-based consensus approach to uncertainty measurement in manufacturing, SOHOMA/IFAC. 


Pearce T., Zaki M., Neely A., Brintrup A. (2019), Uncertainty in neural networks: Bayesian Ensembling, The 22nd International Conference on 

Artificial Intelligence and Statistics, April 2019, Okinawa, Japan.


Pearce, T., Zaki, M., Brintrup, A., Neely, A. (2019). Representing Uncertainty in Biological and Artificial Neural Networks. Artificial & Biological Cognition, 7th Cambridge Neuroscience Symposium.




Ledwoch A., Brintrup A. (2018), The moderating impact of topology on supply chain risk mitigation, International Journal of Production Economics.


Brintrup A., Puchkova A. (2018), An Optimisation Framework for Incorporating Reliability Data in Complex Supply Networks, Applied Network Science.


Ledwoch A., Brintrup A. (2017), Systemic Risk Assessment in Complex Supply Networks, IEEE Systems Journal.


Brintrup A., Barros J., Ledwoch A. (2017), Topological analysis of robustness in the global automotive industry, Logistics Research.



Aristodemou L., Tietze F., Brintrup A., Deeble S. (2018), Early Stage Technology Strategic Decision Making: a machine learning approach using Intellectual Property Analytics, Conference: R&D Management Conference 2018, Milan, Italy.


Pearce T., Zaki M., Neely A., Brintrup A.(2018), High quality prediction intervals for deep learning: A distribution free objective function, 35th Int. Conf. on Machine Learning (ICML), July 2018. 


Luna Andrade J.J., Salonitis K., Brintrup A.(2018), Evolution of aerospace ecosystems applying network science, 8th International Conference on Operations and Supply Chain Management (OSCM). 9th – 12th September 2018, Cranfield, UK


Wichmann P., Srinivasan R., Baker S., Woodall P., Brintrup A., McFarlane D.(2018), Who is behind the curtain? A data mining approach to detect hidden members of supply networks , INCOM/IFAC


McFarlane D., Srinivasan R., Puchkova A., Thorne A., Brintrup A., A maturity framework for operational resilience and its application for production control (2018), in Service Orientation in Holonic and Multi-Agent Manufacturing (SOHOMA), February 2018, pp. 51-62

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