Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning

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Li, X., Wang, Z., Yang, C., & Bozkurt, A. (2024). An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms. Energy, 296, 131259. https://doi.org/10.1016/j.energy.2024.131259

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Lorenz, R., Kraus, M., Wolf, H., Feuerriegel, S., & Netland, T. H. (2024). Selecting advanced analytics in manufacturing: a decision support model. Production Planning & Control, 35(7), 711-724. https://doi.org/10.1080/09537287.2022.2126951

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Narang, D., Madaan, J., Chan, F. T., & Chungcharoen, E. (2024). Managing open loop water resource value chain through IoT focused decision and information integration (DII) modelling using fuzzy MCDM approach. Journal of Environmental Management, 350, 119609. https://doi.org/10.1016/j.jenvman.2023.119609

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Wang, J., Qian, Y., Zhang, L., Wang, K., & Zhang, H. (2024). A novel wind power forecasting system integrating time series refining, nonlinear multi-objective optimized deep learning and linear error correction. Energy Conversion and Management, 299, 117818. https://doi.org/10.1016/j.enconman.2023.117818

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Zhang, W., Xiao, G., Gen, M., Geng, H., Wang, X., Deng, M., & Zhang, G. (2024). Enhancing multi-objective evolutionary algorithms with machine learning for scheduling problems: recent advances and survey. Frontiers in Industrial Engineering, 2, 1337174. https://doi.org/10.3389/fieng.2024.1337174

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