| Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA K Govindan, M Kadziński, R Ehling, G Miebs Omega 85, 1-15, 2019 | 150 | 2019 |
| Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system M Cinelli, M Kadziński, G Miebs, M Gonzalez, R Słowiński European Journal of Operational Research 302 (2), 633-651, 2022 | 86 | 2022 |
| Sustainability evaluation of retrofitting solutions for rural buildings through life cycle approach and multi-criteria analysis L Rocchi, M Kadziński, ME Menconi, D Grohmann, G Miebs, L Paolotti, ... Energy and Buildings 173, 281-290, 2018 | 61 | 2018 |
| Multiple criteria assessment of insulating materials with a group decision framework incorporating outranking preference model and characteristic class profiles M Kadziński, L Rocchi, G Miebs, D Grohmann, ME Menconi, L Paolotti Group Decision and Negotiation 27, 33-59, 2018 | 30 | 2018 |
| Heuristic algorithms for aggregation of incomplete rankings in multiple criteria group decision making G Miebs, M Kadziński Information Sciences 560, 107-136, 2021 | 15 | 2021 |
| Understanding the drivers of Urban Development Agreements with the rough set approach and robust decision rules A Oppio, M Dell’Ovo, F Torrieri, G Miebs, M Kadziński Land Use Policy 96, 104678, 2020 | 14 | 2020 |
| Efficient strategies of static features incorporation into the recurrent neural network G Miebs, M Mochol-Grzelak, A Karaszewski, RA Bachorz Neural Processing Letters 51 (3), 2301-2316, 2020 | 14 | 2020 |
| Advancing hazard assessment of energy accidents in the natural gas sector with rough set theory and decision rules M Cinelli, M Spada, M Kadziński, G Miebs, P Burgherr Energies 12 (21), 4178, 2019 | 6 | 2019 |
| An active preference learning approach to aid the selection of validators in blockchain environments J Gehrlein, G Miebs, M Brunelli, M Kadziński Omega 118, 102869, 2023 | 4 | 2023 |
| Classification models for the risk assessment of energy accidents in the natural gas sector M Cinelli, M Spada, G Miebs, M Kadziński, P Burgherr Resilience. The 2nd International Workshop on Modelling of Physical …, 2017 | 3 | 2017 |
| Predicting a time-dependent quantity using recursive generative query network G Miebs, M Wójcik, A Karaszewski, M Mochol-Grzelak, P Wawdysz, ... International Journal of Neural Systems 32 (11), 2250056, 2022 | 2 | 2022 |
| Multi-criteria human resources planning optimisation using genetic algorithms enhanced with MCDA M Jurczak, G Miebs, RA Bachorz Operations Research and Decisions 32, 2022 | 1 | 2022 |
| Priority Attachment: a Comprehensive Mechanism for Generating Networks M Morzy, T Kajdanowicz, P Kazienko, G Miebs, A Rusin Scientific Reports 9 (1), 3383, 2019 | 1 | 2019 |
| Aggregation of Stochastic Rankings in Group Decision Making M Kadziński, G Miebs, D Grynia, R Słowiński Collective Decisions: Theory, Algorithms And Decision Support Systems, 83-101, 2022 | | 2022 |
| Breaking the black-box nature predictive models P Wawdysz, G Miebs, L Van Nerom, RA Bachorz Steel Times International, 28-35, 2021 | | 2021 |
| Neural network as a tool capable of acquiring hydraulics of a pipeline G Miebs, A Karaszewski, M Mochol-Grzelak, P Wawdysz, M Wójcik, ... | | |
| IAIST. ORG G Miebs, R Bachorz | | |
| Application of Generative Query Networks for industrial time series G Miebs, M Mochol-Grzelak, A Karaszewski, P Wawdysz, RA Bachorz, ... | | |
Miłosz KadzińskiPoznan University of Technology, Faculty of Computing and Telecommunications, Institute of ComputingVerified email at cs.put.poznan.pl
