Comparison of least squares Monte Carlo methods with applications to energy real options S Nadarajah, F Margot, N Secomandi European Journal of Operational Research 256 (1), 196-204, 2017 | 101* | 2017 |
Relaxations of approximate linear programs for the real option management of commodity storage S Nadarajah, F Margot, N Secomandi Management Science 61 (12), 3054-3076, 2015 | 87* | 2015 |
Less-than-truckload carrier collaboration problem: modeling framework and solution approach S Nadarajah, JH Bookbinder Journal of heuristics 19, 917-942, 2013 | 45 | 2013 |
A level-set method for convex optimization with a feasible solution path Q Lin, S Nadarajah, N Soheili URL: http://www. optimization-online. org/DB_HTML/2017/10/6274. html, 2017 | 34 | 2017 |
Revisiting approximate linear programming: Constraint-violation learning with applications to inventory control and energy storage Q Lin, S Nadarajah, N Soheili Management Science 66 (4), 1544-1562, 2019 | 27* | 2019 |
Merchant energy trading in a network S Nadarajah, N Secomandi Operations Research 66 (5), 1304-1320, 2018 | 27 | 2018 |
Managing shutdown decisions in merchant commodity and energy production: A social commerce perspective A Trivella, S Nadarajah, SE Fleten, D Mazieres, D Pisinger Manufacturing and Service Operations Management, 2019 | 19* | 2019 |
A review of the operations literature on real options in energy S Nadarajah, N Secomandi European Journal of Operational Research 309 (2), 469-487, 2023 | 18* | 2023 |
Meeting corporate renewable power targets A Trivella, D Mohseni-Taheri, S Nadarajah Management science 69 (1), 491-512, 2023 | 17* | 2023 |
Relationship between least squares Monte Carlo and approximate linear programming S Nadarajah, N Secomandi Operations Research Letters 45 (5), 409-414, 2017 | 17 | 2017 |
Collaborative logistics in vehicle routing S Nadarajah University of Waterloo, 2008 | 15 | 2008 |
Data‐driven storage operations: Cross‐commodity backtest and structured policies C Mandl, S Nadarajah, S Minner, S Gavirneni Production and Operations Management 31 (6), 2438-2456, 2022 | 14* | 2022 |
Offline-online reinforcement learning for energy pricing in office demand response: lowering energy and data costs D Jang, L Spangher, T Srivistava, M Khattar, U Agwan, S Nadarajah, ... Proceedings of the 8th ACM International Conference on Systems for Energy …, 2021 | 14 | 2021 |
Dynamic pricing for hotel rooms when customers request multiple-day stays S Nadarajah, YF Lim, Q Ding Available at SSRN 2639188, 2015 | 13 | 2015 |
A data efficient and feasible level set method for stochastic convex optimization with expectation constraints Q Lin, S Nadarajah, N Soheili, T Yang Journal of machine learning research 21 (143), 1-45, 2020 | 12 | 2020 |
Deep reinforcement learning with planning guardrails for building energy demand response D Jang, L Spangher, S Nadarajah, C Spanos Energy and AI 11, 100204, 2023 | 8 | 2023 |
Real option management of hydrocarbon cracking operations S Nadarajah, N Secomandi, G Sowers, JM Wassick Real options in energy and commodity markets, 173-202, 2017 | 7 | 2017 |
Decarbonizing buildings via energy demand response and deep reinforcement learning: The deployment value of supervisory planning and guardrails D Jang, L Spangher, S Nadarajah, C Spanos Available at SSRN 4078206, 2022 | 6 | 2022 |
A machine learning approach to methane emissions mitigation in the oil and gas industry J Wang, S Nadarajah, J Wang, AP Ravikumar EarthArXiv, 2020 | 6 | 2020 |
Self-guided approximate linear programs P Pakiman, S Nadarajah, N Soheili, Q Lin arXiv preprint arXiv:2001.02798, 2020 | 6 | 2020 |