| Reinforcement learning for dynamic condition-based maintenance of a system with individually repairable components N Yousefi, S Tsianikas, DW Coit Quality Engineering 32 (3), 388-408, 2020 | 57 | 2020 |
| Economic and resilience benefit analysis of incorporating battery storage to photovoltaic array generation J Zhou, S Tsianikas, DP Birnie III, DW Coit Renewable energy 135, 652-662, 2019 | 51 | 2019 |
| Dynamic maintenance model for a repairable multi-component system using deep reinforcement learning N Yousefi, S Tsianikas, DW Coit Quality Engineering 34 (1), 16-35, 2022 | 36 | 2022 |
| Economic trends and comparisons for optimizing grid-outage resilient photovoltaic and battery systems S Tsianikas, J Zhou, DP Birnie III, DW Coit Applied energy 256, 113892, 2019 | 34 | 2019 |
| A storage expansion planning framework using reinforcement learning and simulation-based optimization S Tsianikas, N Yousefi, J Zhou, MD Rodgers, D Coit Applied Energy 290, 116778, 2021 | 16 | 2021 |
| Battery selection for optimal grid-outage resilient photovoltaic and battery systems S Tsianikas, J Zhou, N Yousefi, DW Coit arXiv preprint arXiv:1901.11389, 2019 | 15 | 2019 |
| Combined optimization of system reliability improvement and resilience with mixed cascading failures in dependent network systems J Zhou, DW Coit, FA Felder, S Tsianikas Reliability Engineering & System Safety 237, 109376, 2023 | 9 | 2023 |
| Inspection plan prediction for multi-repairable component systems using neural network N Yousefi, S Tsianikas, J Zhou, DW Coit arXiv preprint arXiv:2001.09015, 2020 | 6 | 2020 |
| Deep reinforcement learning for resilient microgrid expansion planning with multiple energy resource K Pang, J Zhou, S Tsianikas, Y Ma Quality and Reliability Engineering International 40 (1), 34-56, 2024 | 5 | 2024 |
| Long-term microgrid expansion planning with resilience and environmental benefits using deep reinforcement learning K Pang, J Zhou, S Tsianikas, DW Coit, Y Ma Renewable and Sustainable Energy Reviews 191, 114068, 2024 | 4 | 2024 |
| Deep reinforcement learning based microgrid expansion planning with battery degradation and resilience enhancement K Pang, J Zhou, S Tsianikas, Y Ma 2021 3rd International Conference on System Reliability and Safety …, 2021 | 4 | 2021 |
| Resilience based optimization for western US transmission grid against cascading failures J Zhou, S Tsianikas, DW Coit, FA Felder arXiv preprint arXiv:1912.02887, 2019 | 4 | 2019 |
| A sequential resource investment planning framework using reinforcement learning and simulation-based optimization: A case study on microgrid storage expansion S Tsianikas, N Yousefi, J Zhou, D Coit, M Rodgers Submitted manuscript to:" Production and Operations Management, 2019 | 3 | 2019 |
| Comparison of neural network based approaches for short-term residential energy load forecasting S Tsianikas, X Xie, RS Puri, AK Parlikad, DW Coit IIE Annual Conference. Proceedings, 1-6, 2022 | 2 | 2022 |
| The impact of analytical outage modeling on expansion planning problems in the area of power systems S Tsianikas, N Yousefi, J Zhou, DW Coit arXiv preprint arXiv:2001.08815, 2020 | 2 | 2020 |
| Microgrid Expansion Planning Using Simulation-Based Optimization and Reinforcement Learning S Tsianikas Rutgers The State University of New Jersey, School of Graduate Studies, 2020 | 2 | 2020 |
| Techno-economic optimization of a PV+ battery system: A case study for a hospital in Orlando, FL S Tsianikas, J Zhou, DP Birnie, D Coit IISE Annual Conference, 2019 | 1 | 2019 |
| Optimum sizing of economic and grid-outage resilient solar array considering the forecasting of solar project cost and customer load demand J Zhou, S Tsianikas, N Yousefi, DW Coit 12th International Conference on Quality, Reliability, Risk, Maintenance …, 2022 | | 2022 |
| Analysis of innovative e-government services S Tsianikas | | 2016 |
David W. CoitProfessor of Industrial & Systems Engineering at Rutgers University在 soe.rutgers.edu 的电子邮件经过验证
David W. CoitProfessor of Industrial & Systems Engineering at Rutgers University在 soe.rutgers.edu 的电子邮件经过验证