| Evolving artificial neural networks using an improved PSO and DPSO J Yu, S Wang, L Xi Neurocomputing 71 (4-6), 1054-1060, 2008 | 269 | 2008 |
| Empirical analysis of support vector machine ensemble classifiers S Wang, A Mathew, Y Chen, L Xi, L Ma, J Lee Expert Systems with applications 36 (3), 6466-6476, 2009 | 205 | 2009 |
| An effective heuristic for flexible job-shop scheduling problem with maintenance activities S Wang, J Yu Computers & Industrial Engineering 59 (3), 436-447, 2010 | 176 | 2010 |
| Bi-objective identical parallel machine scheduling to minimize total energy consumption and makespan S Wang, X Wang, J Yu, S Ma, M Liu Journal of cleaner production 193, 424-440, 2018 | 138 | 2018 |
| An improved particle swarm optimization for evolving feedforward artificial neural networks J Yu, L Xi, S Wang Neural Processing Letters 26, 217-231, 2007 | 133 | 2007 |
| One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes S Chen, J Yu, S Wang Journal of Process Control 87, 54-67, 2020 | 130 | 2020 |
| Cyber Physical System and Big Data enabled energy efficient machining optimisation YC Liang, X Lu, WD Li, S Wang Journal of cleaner Production 187, 46-62, 2018 | 124 | 2018 |
| Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning S Wang, M Liu Journal of Manufacturing Systems 37, 182-192, 2015 | 123 | 2015 |
| Bi-objective optimization of a single machine batch scheduling problem with energy cost consideration S Wang, M Liu, F Chu, C Chu Journal of cleaner production 137, 1205-1215, 2016 | 113 | 2016 |
| A branch and bound algorithm for single-machine production scheduling integrated with preventive maintenance planning S Wang, M Liu International Journal of Production Research 51 (3), 847-868, 2013 | 109 | 2013 |
| An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production S Wang, X Wang, F Chu, J Yu International Journal of Production Research 58 (8), 2283-2314, 2020 | 105 | 2020 |
| A genetic algorithm for two-stage no-wait hybrid flow shop scheduling problem S Wang, M Liu Computers & Operations Research 40 (4), 1064-1075, 2013 | 96 | 2013 |
| A branch-and-bound algorithm for two-stage no-wait hybrid flow-shop scheduling S Wang, M Liu, C Chu International Journal of Production Research 53 (4), 1143-1167, 2015 | 86 | 2015 |
| Two-stage hybrid flow shop scheduling with preventive maintenance using multi-objective tabu search method S Wang, M Liu International Journal of Production Research 52 (5), 1495-1508, 2014 | 85 | 2014 |
| A modified support vector data description based novelty detection approach for machinery components S Wang, J Yu, E Lapira, J Lee Applied Soft Computing 13 (2), 1193-1205, 2013 | 84 | 2013 |
| A deep autoencoder feature learning method for process pattern recognition J Yu, X Zheng, S Wang Journal of Process Control 79, 1-15, 2019 | 83 | 2019 |
| Big Data enabled Intelligent Immune System for energy efficient manufacturing management S Wang, YC Liang, WD Li, XT Cai Journal of cleaner production 195, 507-520, 2018 | 80 | 2018 |
| A heuristic method for two-stage hybrid flow shop with dedicated machines S Wang, M Liu Computers & Operations Research 40 (1), 438-450, 2013 | 71 | 2013 |
| Fog computing and convolutional neural network enabled prognosis for machining process optimization YC Liang, WD Li, X Lu, S Wang Journal of Manufacturing Systems 52, 32-42, 2019 | 65 | 2019 |
| An improved exact algorithm for single-machine scheduling to minimise the number of tardy jobs with periodic maintenance M Liu, S Wang, C Chu, F Chu International Journal of Production Research 54 (12), 3591-3602, 2016 | 59 | 2016 |
