مدل بهینه سازی چند هدفه غیر خطی برنامه ریزی تولید بر اساس منطق فازی و یادگیری ماشین

Abdallah, M., Hamdan, S., & Shabib, A. (2021). A multi-objective optimization model for strategic waste management master plans. Journal of Cleaner Production, 284, 124714. https://doi.org/10.1016/j.jclepro.2020.124714

Afzal, S., Shokri, A., Ziapour, B. M., Shakibi, H., & Sobhani, B. (2024). Building energy consumption prediction and optimization using different neural network-assisted models; comparison of different networks and optimization algorithms. Engineering Applications of Artificial Intelligence, 127, 107356. https://doi.org/10.1016/j.engappai.2023.107356

Ala, A., Goli, A., Mirjalili, S., & Simic, V. (2024). A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design. Applied Soft Computing, 150, 111012. https://doi.org/10.1016/j.asoc.2023.111012

Alinezhad, M., Mahdavi, I., Hematian, M., & Tirkolaee, E. B. (2022). A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries. Environment, Development and Sustainability, 1-28. https://doi.org/10.1007/s10668-021-01809-y

Ardanta, M. A., Fauzi, A., Patimah, P., Khadijah, F., Sihombing, Y. T., Hasan, F. S., & Rahmawati, D. (2024). Optimization of Business Decision Accuracy through the Application of Mathematical Economics. International Journal of Advanced Multidisciplinary, 2(4), 866-885. https://doi.org/10.38035/ijam.v2i4.447

Azevedo, B. F., Rocha, A. M. A., & Pereira, A. I. (2024). Hybrid approaches to optimization and machine learning methods: a systematic literature review. Machine Learning, 1-43. https://doi.org/10.1007/s10994-023-06467-x

Behera, I., & Sobhanayak, S. (2024). Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approach. Journal of Parallel and Distributed Computing, 183, 104766. https://doi.org/10.1016/j.jpdc.2023.104766

Chen, L., Fan, H., & Zhu, H. (2024). Multi-objective optimization of cancer treatment using the multi-objective gray wolf optimizer (MOGWO). Multiscale and Multidisciplinary Modeling, Experiments and Design, 7(3), 1857-1866. https://doi.org/10.1007/s41939-023-00307-0

Duan, F., Eslami, M., Khajehzadeh, M., Basem, A., Jasim, D. J., & Palani, S. (2024). Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms. Scientific Reports, 14(1), 13354. https://doi.org/10.1038/s41598-024-64234-x

Elfatih, N. M., Ali, E. S., & Saeed, R. A. (2023). Navigation and Trajectory Planning Techniques for Unmanned Aerial Vehicles Swarm. In Artificial Intelligence for Robotics and Autonomous Systems Applications (pp. 369-404). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-28715-2_12.

Gargari, F. J., & Pourjavad, E. (2020, December). Supplier Selection and Order Allocation Under Disruption: Multi-Objective Evolutionary Algorithms. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM),  868-872. IEEE. 10.1109/IEEM45057.2020.9309949

Guo, Z., & Wang, H. (2023). Implications on managing inventory systems for products with stock-dependent demand and nonlinear holding cost via the adaptive EOQ policy. Computers & Operations Research, 150, 106080. https://doi.org/10.1016/j.cor.2022.106080

Hong, Y., Linton, O., McCabe, B., Sun, J., & Wang, S. (2024). Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach. Journal of Econometrics, 238(2), 105603. https://doi.org/10.1016/j.jeconom.2023.105603

Huang, L., Qin, J., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2023). Normalization techniques in training dnns: Methodology, analysis and application. IEEE transactions on pattern analysis and machine intelligence, 45(8), 10173-10196. 10.1109/TPAMI.2023.3250241

Javadi Gargari, F., Saeidi-Mobarakeh, Z., & Amoozad Khalili, H. (2024). A Multi-objective Leagile Demand-Driven Optimization Model incorporating a Reliable Omnichannel Retailer: A Case Study. Journal of Industrial Engineering International, 2(2). https://sanad.iau.ir/Journal/jiei/Article/1105240

Ifaei, P., Nazari-Heris, M., Charmchi, A. S. T., Asadi, S., & Yoo, C. (2023). Sustainable energies and machine learning: An organized review of recent applications and challenges. Energy, 266, 126432. https://doi.org/10.1016/j.energy.2022.126432

Javadi Gargari, F., Sayad, M., Posht Mashhadi, S. A., Sadrnia, A., Nedjati, A., & Yousefi Golafshani, T. (2021). Five‐Echelon Multiobjective Health Services Supply Chain Modeling under Disruption. Mathematical Problems in Engineering, 2021(1), 5587392. https://doi.org/10.1155/2021/5587392

Javadi Gargari, F., & Seifbarghy, M. (2020). Solving multi-objective supplier selection and quota allocation problem under disruption using a scenario-based approach. Engineering Review: Međunarodni časopis namijenjen publiciranju originalnih istraživanja s aspekta analize konstrukcija, materijala i novih tehnologija u području strojarstva, brodogradnje, temeljnih tehničkih znanosti, elektrotehnike, računarstva i građevinarstva, 40(3), 78-89.. https://doi.org/10.30765/er.40.3.08

Jiang, C., Xie, J., & Ye, T. (2024). Network structure guided multi-objective optimization approach for key entity identification. Applied Soft Computing, 151, 111115. https://doi.org/10.1016/j.asoc.2023.111115

Kalita, K., Ramesh, J. V. N., Cepova, L., Pandya, S. B., Jangir, P., & Abualigah, L. (2024). Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems. Scientific reports, 14(1), 1816. https://doi.org/10.1038/s41598-024-52083-7

Kaliyaperumal, P., & Das, A. (2022). A mathematical model for nonlinear optimization which attempts membership functions to address the uncertainties. Mathematics, 10(10), 1743. https://doi.org/10.3390/math10101743

Khalili-Fard, A., Parsaee, S., Bakhshi, A., Yazdani, M., Aghsami, A., & Rabbani, M. (2024). Multi-objective optimization of closed-loop supply chains to achieve sustainable development goals in uncertain environments. Engineering Applications of Artificial Intelligence, 133, 108052. https://doi.org/10.1016/j.engappai.2024.108052

Khan, A. S. (2022). Problem-Specific Heuristics for Diagnosability and Inventory Analysis in a Reconfigurable Manufacturing System. IEEE Access, 10, 70032-70052. 10.1109/ACCESS.2022.3187812

Kristian, A., Goh, T. S., Ramadan, A., Erica, A., & Sihotang, S. V. (2024). Application of ai in optimizing energy and resource management: Effectiveness of deep learning models. International Transactions on Artificial Intelligence, 2(2), 99-105. https://doi.org/10.33050/italic.v2i2.530

Kushartanto, A. I. (2024, February). Comparative Analysis Various Membership Functions Based on Neural Networks for Prediction of Students Graduation. In 2024 2nd International Conference on Software Engineering and Information Technology (ICoSEIT) (pp. 299-304). IEEE. 10.1109/ICoSEIT60086.2024.10497491

Li, P., Xu, T., Wei, S., & Wang, Z. H. (2022). Multi-objective optimization of urban environmental system design using machine learning. Computers, Environment and Urban Systems, 94, 101796. https://doi.org/10.1016/j.compenvurbsys.2022.101796

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

Lorente-Leyva, L. L., Alemany, M. M. E., & Peluffo-Ordóñez, D. H. (2024). A conceptual framework for the operations planning of the textile supply chains: Insights for sustainable and smart planning in uncertain and dynamic contexts. Computers & Industrial Engineering, 187, 109824. https://doi.org/10.1016/j.cie.2023.109824

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

Most, T., & Will, J. (2024). Sensitivity analysis using the Metamodel of Optimal Prognosis. arXiv preprint arXiv:2408.03590. https://doi.org/10.48550/arXiv.2408.03590

Mu'azu, M. A. (2023). Hybridized artificial neural network with metaheuristic algorithms for bearing capacity prediction. Ain Shams Engineering Journal, 14(5), 101980. https://doi.org/10.1016/j.asej.2022.101980

Nadeem, A., Rizvi, A. A., & Noor, M. Y. (2024). Applying a Higher Number of Output Membership Functions to Enhance the Precision of a Fuzzy System. IEEE Transactions on Emerging Topics in Computational Intelligence. 10.1109/TETCI.2024.3425309

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

Nili, M., Seyedhosseini, S. M., Jabalameli, M. S., & Dehghani, E. (2021). A multi-objective optimization model to sustainable closed-loop solar photovoltaic supply chain network design: A case study in Iran. Renewable and sustainable energy reviews, 150, 111428. https://doi.org/10.1016/j.rser.2021.111428

Nixon, M. P., Gloor, G. B., & Silverman, J. D. (2024). Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis. Biorxiv. https://doi.org/10.1101/2024.04.01.587602

Parhi, S. K., & Panigrahi, S. K. (2024). Alkali–silica reaction expansion prediction in concrete using hybrid metaheuristic optimized machine learning algorithms. Asian Journal of Civil Engineering, 25(1), 1091-1113. https://doi.org/10.1007/s42107-023-00799-8

Pasupuleti, V., Thuraka, B., Kodete, C. S., & Malisetty, S. (2024). Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management. Logistics, 8(3), 73. https://doi.org/10.3390/logistics8030073

Pokushko, M., Stupina, A., Medina-Bulo, I., Ezhemanskaya, S., Kuzmich, R., & Pokushko, R. (2023). Algorithm for Application of a Basic Model for the Data Envelopment Analysis Method in Technical Systems. Algorithms, 16(10), 460. https://doi.org/10.3390/a16100460

Rajwar, K., Deep, K., & Das, S. (2023). An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artificial Intelligence Review, 56(11), 13187-13257. https://doi.org/10.1007/s10462-023-10470-y

Rashed, N. A., Ali, Y. H., Rashid, T. A., & Salih, A. (2024). Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems. arXiv preprint arXiv:2407.08754. https://doi.org/10.48550/arXiv.2407.08754

Saha, C., Jana, D. K., & Duary, A. (2023). Enhancing production inventory management for imperfect items using fuzzy optimization strategies and Differential Evolution (DE) algorithms. Franklin Open, 5, 100051. https://doi.org/10.1016/j.fraope.2023.100051

Sharma, V., Raj, A., & Chakraborty, A. (2023). Analysis of power dynamics in sustainable supply chain under non-linear demand setup. Operations Management Research, 16(1), 18-32. https://doi.org/10.1007/s12063-022-00268-6

Song, Y., Wu, Y., Guo, Y., Yan, R., Suganthan, P. N., Zhang, Y., ... & Feng, Q. (2024). Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities. Swarm and Evolutionary Computation, 86, 101517. https://doi.org/10.1016/j.swevo.2024.101517

Sotelo-Salas, C., Monardes-Concha, C. A., Pérez-Galarce, F., & Santa González, R. (2024). A multi-objective optimization model for planning emergency shelters after a tsunami. Socio-Economic Planning Sciences, 93, 101909. https://doi.org/10.1016/j.seps.2024.101909

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Tavana, Madjid, Hosein Arman, Abdollah Hadi-Vencheh, and Sadegh Mansoori. "A fuzzy multi-objective optimization model for sustainable location planning using volumetric fuzzy sets." Annals of Operations Research (2023): 1-29. https://doi.org/10.1007/s10479-023-05505-0

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

Wang, Z., Pei, Y., & Li, J. (2023). A survey on search strategy of evolutionary multi-objective optimization algorithms. Applied Sciences, 13(7), 4643. https://doi.org/10.3390/app13074643

Xin, X. Y., Ma, J., Liu, H. Q., Gu, Y. J., Wang, Y. F., & Cui, H. Z. (2023). A simple Pb-doping to achieve bonding evolution, VSn and resonant level shifting for regulating thermoelectric transport behavior of SnTe. Journal of Materials Science & Technology, 151, 66-72. https://doi.org/10.1016/j.jmst.2022.12.021

Yan, M., Yuan, H., Xu, J., Yu, Y., & Jin, L. (2021). Task allocation and route planning of multiple UAVs in a marine environment based on an improved particle swarm optimization algorithm. EURASIP Journal on Advances in Signal Processing, 2021, 1-23. https://doi.org/10.1186/s13634-021-00804-9

Yousefzadeh, R., Kazemi, A., & Al-Maamari, R. S. (2024). Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening. Scientific Reports, 14(1), 9200. https://doi.org/10.1038/s41598-024-59387-8

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

Zhu, Z., Li, X., Chen, H., Zhou, X., & Deng, W. (2024). An effective and robust genetic algorithm with hybrid multi-strategy and mechanism for airport gate allocation. Information Sciences, 654, 119892. https://doi.org/10.1016/j.ins.2023.119892

Zou, F., Yen, G. G., Tang, L., & Wang, C. (2021). A reinforcement learning approach for dynamic multi-objective optimization. Information Sciences, 546, 815-834. https://doi.org/10.1016/j.ins.2020.08.101