[1] Azizi V, Javadi SM, Razavi SA. The Relationship between Oil Price Uncertainty and Earnings Management in Refining and Petrochemical Companies of Tehran Stock Exchange. Information Sciences and Technological Innovations. 2025 Mar 25;2(1):35-47.
[2] Jiang G, Chen F, Gu M. Supply chain digitization and energy resilience: Evidence from China. Energy Economics. 2025 Apr 1; 144:108420.
[3] Wang T, Cai X, Xu Q. Energy market price forecasting and financial technology risk management based on generative AI. APPLIED AND COMPUTATIONAL ENGINEERING Учредители: EWA Publishing. 2025;116(1):29-34.
[4] Kallrath J, Pardalos PM, Rebennack S, Scheidt M, editors. Optimization in the energy industry. Berlin: Springer; 2009.
[5] Xu B, Fu R, Lau CK. Energy market uncertainty and the impact on the crude oil prices. Journal of Environmental Management. 2021 Nov 15; 298:113403.
[6] Lu J, Zhao R, Yu Z, Dai Y, Zeng K. RL and AHP-Based Multi-Timescale Multi-Clock Source Time Synchronization for Distribution Power Internet of Things. Computers, Materials & Continua. 2024 Mar 1;78(3).
[7] Li C, Zheng P, Yin Y, Wang B, Wang L. Deep reinforcement learning in smart manufacturing: A review and prospects. CIRP Journal of Manufacturing Science and Technology. 2023 Feb 1; 40:75-101.
[8] Aksakal E, Dağdeviren M. Analyzing reward management framework with multi criteria decision making methods. Procedia-Social and Behavioral Sciences. 2014 Aug 25; 147:147-52.
[9] He Z, Tran KP, Thomassey S, Zeng X, Xu J, Yi C. A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. Computers in Industry. 2021 Feb 1; 125:103373.
[10] Yu Y, Zhang N. Revealing the power of market-based energy policy: Evidence from China's energy quota trading system using machine learning. Energy Policy. 2026 Jan 1; 208:114905.
[11] Lara-Perez JI, Trejo-Caballero G, Tapia-Tinoco G, Raya-González LE, Garcia-Perez A. Deep Reinforcement Learning-Based Voltage Regulation Using Electric Springs in Active Distribution Networks. Technologies. 2026 Feb 1;14(2):87.
[12] Shoaei M, Noorollahi Y, Yousefi H. Scenario-based long-term planning for a 100% renewable energy system at the regional scale. Renewable Energy. 2026 May 24:125986.
[13] Wang X, Huang Y, Liu Y, Liang D, Zhou Y. Deep reinforcement learning-based coordinated optimization between distribution networks and microgrids towards demand response uncertainty. Energy and AI. 2026 Mar 17:100717.
[14] Aruldoss M, Lakshmi TM, Venkatesan VP. A survey on multi criteria decision making methods and its applications. American Journal of Information Systems. 2013 Dec;1(1):31-43.
[15] Aronofsky, J. S., and Williams, T. S., “Refinery scheduling by linear programming,” Operations Research, 1962.
[16] Braun, M. A., and Burnham, D., “Mathematical programming in process coordination,” AIChE Journal, 1990.
[17] Ribas, G., Leiras, A., and Rebello, R., “Optimization in integrated refinery–petrochemical planning using MILP,” Energy, 2010.
[18] Shah, P., and Mishra, S., “Optimization of refinery operations with environmental constraints,” Journal of Cleaner Production, 2013.
[19] Ivanov, D., and Ray, P., “Multi‑objective genetic algorithm for resilient production and environmental performance,” International Journal of Production Economics, 2014.
[20] Li, H., Ma, J., and Chen, Z., “A two‑stage stochastic MILP for refinery supply under demand uncertainty,” Energy Reports, 2020.
[21] Soni, A., Chaudhary, R., and Yadav, D., “Multi‑objective MILP framework for sustainable oil‑refinery scheduling,” Computers & Chemical Engineering, 2021.
[22] Krasnyuk, S., et al., “Integrated economic–mathematical modeling for refinery–petrochemical coordination,” Energy, 2022.
[23] Keefer, D. L., “Risk analysis in decision making: The role of risk attitudes,” Decision Sciences, 1991.
[24] Coopersmith, E., Kutz, J., and Voss, C., “Value‑of‑information in managerial decisions,” European Journal of Operational Research, 2000.
[25] Batubara, J., et al., “Policy‑based decision framework for sustainable resource development,” Energy Policy, 2016.
[26] MacKenzie, A., “Dynamic allocation and Markov decision optimization in complex systems,” Journal of Industrial Engineering, 2016.
[27] Sudhakar, B., “Reinforcement learning for dynamic refinery scheduling,” Expert Systems with Applications, 2020.
[28] Kwon, J. S.‑I., et al., “Decision‑supporting platform for petrochemical supply chain management,” Computers & Chemical Engineering, 2022.
[29] Raja, M., et al., “Reinforcement learning for adaptive resource coordination,” Applied Energy, 2023.
[30] Odimarha, A., et al., “Process control through self‑learning adaptive agents,” Energy AI, 2024.
[31] Cui, T., and Yao, L., “Hybrid neuro‑symbolic automation in industrial decision systems,” Applied Energy, 2024.
[32] Ma, L., Zhong, R., and Hu, C., “Adaptive coordination between refinery and petrochemical units via deep RL,” Energy Reports, 2023.
[33] Lee, C.‑Y., Chou, B.‑J., and Huang, C.‑F., “Data‑science and reinforcement learning for raw‑material procurement in petrochemical industry,” Advanced Engineering Informatics, 2022.
[34] Cui, T., Zhou, J., and Liu, Y., “Deep reinforcement learning for multi‑criteria resource control,” Applied Energy, 2024.
[35] Wang, Y., and Zhang, S., “Analytic hierarchy process in renewable energy policy evaluation,” Renewable Energy, 2020.
[36] Tatar, O., and Kara, M., “AI‑supported policy learning in governance models,” Energy Economics, 2024.
[37] Bhol, S. K., and Reddy, P. V., “Multi‑criteria AI design for sustainable decisions,” Expert Systems with Applications, 2024.
