| Digital Twins: State of the art theory and practice, challenges, and open research questions A Sharma, E Kosasih, J Zhang, A Brintrup, A Calinescu Journal of Industrial Information Integration 30, 100383, 2022 | 439 | 2022 |
| High-quality prediction intervals for deep learning: A distribution-free, ensembled approach T Pearce, A Brintrup, M Zaki, A Neely International conference on machine learning, 4075-4084, 2018 | 353 | 2018 |
| Uncertainty in neural networks: Approximately bayesian ensembling T Pearce, F Leibfried, A Brintrup International conference on artificial intelligence and statistics, 234-244, 2020 | 335* | 2020 |
| Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing A Brintrup, J Pak, D Ratiney, T Pearce, P Wichmann, P Woodall, ... International Journal of Production Research 58 (11), 3330-3341, 2020 | 230 | 2020 |
| A machine learning approach for predicting hidden links in supply chain with graph neural networks EE Kosasih, A Brintrup International Journal of Production Research 60 (17), 5380-5393, 2022 | 158 | 2022 |
| Ergonomic chair design by fusing qualitative and quantitative criteria using interactive genetic algorithms AM Brintrup, J Ramsden, H Takagi, A Tiwari IEEE Transactions on Evolutionary Computation 12 (3), 343-354, 2008 | 137 | 2008 |
| RFID opportunity analysis for leaner manufacturing A Brintrup, D Ranasinghe, D McFarlane International journal of production research 48 (9), 2745-2764, 2010 | 135 | 2010 |
| A review of Pareto pruning methods for multi-objective optimization S Petchrompo, DW Coit, A Brintrup, A Wannakrairot, AK Parlikad Computers & Industrial Engineering 167, 108022, 2022 | 128 | 2022 |
| An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization AM Brintrup, J Ramsden, A Tiwari Computers in Industry 58 (3), 279-291, 2007 | 124 | 2007 |
| Extracting supply chain maps from news articles using deep neural networks P Wichmann, A Brintrup, S Baker, P Woodall, D McFarlane International Journal of Production Research 58 (17), 5320-5336, 2020 | 123 | 2020 |
| Understanding softmax confidence and uncertainty T Pearce, A Brintrup, J Zhu arXiv preprint arXiv:2106.04972, 2021 | 117 | 2021 |
| Supply networks as complex systems: a network-science-based characterization A Brintrup, Y Wang, A Tiwari IEEE Systems Journal 11 (4), 2170-2181, 2015 | 114 | 2015 |
| The structure of the Toyota supply network: an empirical analysis T Kito, A Brintrup, S New, F Reed-Tsochas Saïd Business School WP 3, 2014 | 83 | 2014 |
| Fast machine unlearning without retraining through selective synaptic dampening J Foster, S Schoepf, A Brintrup Proceedings of the AAAI conference on artificial intelligence 38 (11), 12043 …, 2024 | 80 | 2024 |
| The moderating impact of supply network topology on the effectiveness of risk management A Ledwoch, H Yasarcan, A Brintrup International Journal of Production Economics 197, 13-26, 2018 | 78 | 2018 |
| Topological robustness of the global automotive industry A Brintrup, A Ledwoch, J Barros Logistics Research 9 (1), 1, 2016 | 73 | 2016 |
| Behaviour adaptation in the multi-agent, multi-objective and multi-role supply chain A Brintrup Computers in Industry 61 (7), 636-645, 2010 | 71 | 2010 |
| Federated machine learning for privacy preserving, collective supply chain risk prediction G Zheng, L Kong, A Brintrup International Journal of Production Research 61 (23), 8115-8132, 2023 | 68 | 2023 |
| Learning with imbalanced data in smart manufacturing: A comparative analysis Y Fathy, M Jaber, A Brintrup IEEE Access 9, 2734-2757, 2020 | 66 | 2020 |
| Towards knowledge graph reasoning for supply chain risk management using graph neural networks EE Kosasih, F Margaroli, S Gelli, A Aziz, N Wildgoose, A Brintrup International Journal of Production Research 62 (15), 5596-5612, 2024 | 65 | 2024 |
