[2023]
Pichler A., Diem C., Brintrup A., Lafond F., Magerman G., Buiten G., Choi T.Y., Carvalho V., Farmer J.D, Building an alliance to map global supply networks: New firm-level data can inform policy-making, Science 382:6668
Zheng G., Kong L., Brintrup A. (2023), Federated Machine Learning for Privacy Preserving Collective Supply Chain Risk Prediction, International Journal of Production Research
Kosasih E., Papadakis E., Baryannis G., Brintrup A. (2023) Explainable Artificial Intelligence in Supply Chain Management: A Systematic Review of Neurosymbolic Approaches, International Journal of Production Research
Kong L., Zheng G., Brintrup A. (2023) A Federated Machine Learning Approach for Order-level Risk Prediction in Supply Chain Financing, International Journal of Production Economics
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
Foster J., Brintrup A. (2023) Aiding food security and sustainability efforts through graph neural network-based consumer food ingredient detection and substitution, Scientific Reports
Mak S., Xu L., Pearce T., Ostroumov M., Brintrup A. (2023) Fair collaborative vehicle routing: a deep multi-agent reinforcement learning approach, Transportation Research Part C: Emerging Technologies, 157, p.104376
Brintrup A., Kosasih E., Schaeffer P., Zheng G., Demirel G., MacCarthy (2023) Digital Supply Chain Surveillance using Artificial Intelligence: Definitions, Opportunities and Risks, International Journal of Production Research
Schoepf, Stefan, Jack Foster, and Alexandra Brintrup. Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach. ECML PKDD AI4M: AI for Manufacturing Workshop (2023).
Xu L, Proselkov Y, Schoepf S, Minarsch D, Minaricova M, Brintrup A. Implementation of Autonomous Supply Chains for Digital Twinning: a Multi-Agent Approach. IFAC World Congress 2023, Japan.
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
Chauhan, V.K., Alomari, M., Arney, J., Parlikad, A.K. and Brintrup, A. (2023) Exploitation of material consolidation trade-offs in a multi-tier complex supply networks, Supply Chain Analytics
Foster, Jack, Stefan Schoepf, and Alexandra Brintrup (2023). Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening.
Mak, S., Pearce, T., Macfarlane, M., Xu, L., Ostroumov, M., & Brintrup, A. (2023 - forthcoming). Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale? NeurIPS 2023 Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment. NeurIPS 2023, New Orleans, Louisiana, United States.
Foster, Jack, and Alexandra Brintrup (2023). Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization
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
Chauhan, V.K., Ledwoch, A., Brintrup, A., Herrera, M., Giannikas, V., Stojkovic, G. and Mcfarlane, D., (2023). Network science approach for identifying disruptive elements of an airline. Data Science and Management.
Steinberg, F., Burggräf, P., Wagner, J., Heinbach, B., Saßmannshausen, T. and Brintrup, A., (2023). A novel machine learning model for predicting late supplier deliveries of low-volume-high-variety products with application in a German machinery industry. Supply Chain Analytics.
Schoepf, S., Mak, S., Senoner, J., Xu, L., Torbjörn, N. and Brintrup, A., 2023. Unlocking Carbon Reduction Potential with Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem, IJCAI, Search and Planning with Complex Objectives Workshop
Mak, Stephen, Kyle Mana, Parisa Zehtabi, Michael Cashmore, Daniele Magazzeni, and Manuela Veloso. 2023. Towards Accelerating Benders Decomposition via Reinforcement Learning Surrogate Models, In International Conference on Machine Learning (ICML): Workshop on Sampling and Optimization in Discrete Space
Cipolina-Kun, Lucia, Ignacio Carlucho, Stephen Mak, Kalesha Bullard, Vahid Yazdanpanah, Enrico Gerding, and Sebastian Stein. 2023. Theoretical Principles of Multi-Agent Reinforcement Learning for Coalitional Bargaining Games. In International Conference on Learning Representations (ICLR)
Xu, L., Mak, S., Schoepf, S., Ostroumov, M., & Brintrup, A. (2023). AgentChat: Multi-Agent Collaborative Logistics for Carbon Reduction
[2022]
Sharma, A., Kosasih, E., Zhang, J., Brintrup, A. and Calinescu, A., 2022. Digital twins: State of the art theory and practice, challenges, and open research questions. Journal of Industrial Information Integration, p.100383.
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
[2021]
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
[2020]
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.
[2019]
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.
[2018]
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
