| Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview R Csalódi, Z Süle, S Jaskó, T Holczinger, J Abonyi Complexity 2021 (1), 6621235, 2021 | 52 | 2021 |
| Scheduling under uncertainty for Industry 4.0 and 5.0 K Bakon, T Holczinger, Z Süle, S Jaskó, J Abonyi IEEE Access 10, 74977-75017, 2022 | 39 | 2022 |
| Combinatorial algorithm for synthesizing redundant structures to increase reliability of supply chains: application to biodiesel supply B Bertok, K Kalauz, Z Sule, F Friedler Industrial & Engineering Chemistry Research 52 (1), 181-186, 2013 | 36 | 2013 |
| Optimal design of supply chains by P-graph framework under uncertainties Z Süle, B Bertok, F Friedler, LT Fan Chemical Engineering Transactions 25, 453-458, 2011 | 35 | 2011 |
| Extending process-network synthesis algorithms with time bounds for supply network design K Kalauza, Z Süle, B Bertók, F Friedler, LT Fan Chemical Engineering 29, 2012 | 32 | 2012 |
| The applicability of reinforcement learning methods in the development of industry 4.0 applications T Kegyes, Z Süle, J Abonyi Complexity 2021 (1), 7179374, 2021 | 27 | 2021 |
| P-graph-based multi-objective risk analysis and redundancy allocation in safety-critical energy systems Z Süle, J Baumgartner, G Dörgő, J Abonyi Energy 179, 989-1003, 2019 | 20 | 2019 |
| Population balance model of heat transfer in gas–solid particulate systems BG Lakatos, Z Süle, C Mihálykó International Journal of Heat and Mass Transfer 51 (7-8), 1633-1645, 2008 | 18 | 2008 |
| Modeling of heat transfer processes in particulate systems Z Süle, C Mihálykó, BG Lakatos Computer Aided Chemical Engineering 21, 589-594, 2006 | 11 | 2006 |
| Reliability-redundancy allocation in process graphs Z Sule, J Baumgartner, J Abonyi Chemical Engineering Transactions 70, 991-996, 2018 | 7 | 2018 |
| Test-sequence optimisation by survival analysis J Baumgartner, Z Süle, B Bertók, J Abonyi Central European Journal of Operations Research 27, 357-375, 2019 | 5 | 2019 |
| Managing alternative patient Appointments using P-graph methodology Z Süle, J Baumgartner, Á Vathy-Fogarassy dHealth 2020–Biomedical Informatics for Health and Care, 57-64, 2020 | 4 | 2020 |
| Axial dispersion/population balance model of heat transfer in gas–solid turbulent fluidized beds Z Süle, BG Lakatos, C Mihálykó Computers & Chemical Engineering 34 (5), 753-762, 2010 | 4* | 2010 |
| Proposing a decision-support system to maximize the robustness of computer network topologies T Nemes, A David, Z Sule 2019 17th International Conference on Emerging eLearning Technologies and …, 2019 | 2 | 2019 |
| Measuring sustainability—Network science based analysis of water resources models D Leitold, Á Vathy-Fogarassy, Z Süle, R Manchin, J Abonyi 7th EDSI Conference, 189, 2016 | 2 | 2016 |
| Comparison of optimization techniques in the P-graph framework for the design of supply chains under uncertainties E Konig, Z Sule, B Bertok INFORMATION SECURITY, 35, 2014 | 2 | 2014 |
| Axial dispersion/population balance model of heat transfer in turbulent fluidization Z Süle, BG Lakatos, C Mihálykó Computer Aided Chemical Engineering 26, 719-724, 2009 | 2 | 2009 |
| Axial dispersion/population balance model of heat transfer in turbulent fluidization Z Süle, BG Lakatos, C Mihálykó Computer Aided Chemical Engineering 26, 719-724, 2009 | 2 | 2009 |
| Disassembly line optimization with reinforcement learning T Kegyes, Z Süle, J Abonyi Central European Journal of Operations Research, 1-28, 2024 | 1 | 2024 |
| Machine learning-based decision support framework for CBRN protection T Kegyes, Z Süle, J Abonyi Heliyon 10 (4), 2024 | 1 | 2024 |
Janos AbonyiUniversity of Pannonia確認したメール アドレス: mk.uni-pannon.hu