Alumur, S. A., Campbell, J. F., Contreras, I., Kara, B. Y., Marianov, V., & O’Kelly, M. E. (2021). Perspectives on modeling hub location problems. European Journal of Operational Research, 291(1), 1–17. https://doi.org/https://doi.org/10.1016/j.ejor.2020.09.039
Alumur, S., & Kara, B. Y. (2008). Network hub location problems: The state of the art. European Journal of Operational Research, 190(1), 1–21. https://doi.org/https://doi.org/10.1016/j.ejor.2007.06.008
Ben-Hur, A., Horn, D., Siegelmann, H. T., & Vapnik, V. (2001). Support vector clustering. Journal of Machine Learning Research, 2(Dec), 125–137.
Bertsimas, D., Gupta, V., & Kallus, N. (2018). Data-driven robust optimization. Mathematical Programming, 167, 235–292.
Campbell, J. F. (1994). Integer programming formulations of discrete hub location problems. European Journal of Operational Research, 72(2), 387–405.
Campbell, J. F., & O’Kelly, M. E. (2012). Twenty-five years of hub location research. Transportation Science, 46(2), 153–169.
Catanzaro, D., Gourdin, E., Labbé, M., & Özsoy, F. A. (2011). A branch-and-cut algorithm for the partitioning-hub location-routing problem. Computers & Operations Research, 38(2), 539–549. https://doi.org/https://doi.org/10.1016/j.cor.2010.07.014
Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
Dai, X., Wang, X., He, R., Du, W., Zhong, W., Zhao, L., & Qian, F. (2020). Data-driven robust optimization for crude oil blending under uncertainty. Computers & Chemical Engineering, 136, 106595.
Danach, K., Gelareh, S., & Neamatian Monemi, R. (2019). The capacitated single-allocation p-hub location routing problem: a Lagrangian relaxation and a hyper-heuristic approach. EURO Journal on Transportation and Logistics, 8(5), 597–631. https://doi.org/https://doi.org/10.1007/s13676-019-00141-w
de Camargo, R. S., de Miranda, G., & Løkketangen, A. (2013). A new formulation and an exact approach for the many-to-many hub location-routing problem. Applied Mathematical Modelling, 37(12), 7465–7480. https://doi.org/https://doi.org/10.1016/j.apm.2013.02.035
Ernst, A. T., & Krishnamoorthy, M. (1999). Solution algorithms for the capacitated single allocation hub location problem. Annals of Operations Research, 86(0), 141–159. https://doi.org/10.1023/A:1018994432663
Ghaffarinasab, N., Van Woensel, T., & Minner, S. (2018). A continuous approximation approach to the planar hub location-routing problem: Modeling and solution algorithms. Computers & Operations Research, 100, 140–154. https://doi.org/https://doi.org/10.1016/j.cor.2018.07.022
Ghahremani-Nahr, J., Nozari, H., & Aliahmadi, A. (2023). Contract Design for Return Products in a Cooperative Closed-Loop Supply Chain. Global Business Review, 09721509221148892.
Gilani, H., Sahebi, H., & Pishvaee, M. S. (2022). A data-driven robust optimization model for integrated network design solar photovoltaic to micro grid. Sustainable Energy, Grids and Networks, 31, 100714.
Goerigk, M., & Kurtz, J. (2023). Data-driven robust optimization using deep neural networks. Computers & Operations Research, 151, 106087.
Gumte, K. M., Pantula, P. D., Miriyala, S. S., & Mitra, K. (2021). Data driven robust optimization for handling uncertainty in supply chain planning models. Chemical Engineering Science, 246, 116889.
Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2003). A practical guide to support vector classification. Taipei, Taiwan.
Inapakurthi, R. K., Pantula, P. D., Miriyala, S. S., & Mitra, K. (2020). Data driven robust optimization of grinding process under uncertainty. Materials and Manufacturing Processes, 35(16), 1870–1876.
Karimi, H. (2018). The capacitated hub covering location-routing problem for simultaneous pickup and delivery systems. Computers & Industrial Engineering, 116, 47–58. https://doi.org/https://doi.org/10.1016/j.cie.2017.12.020
Klincewicz, J. G. (1991). Heuristics for the p-hub location problem. European Journal of Operational Research, 53(1), 25–37. https://doi.org/https://doi.org/10.1016/0377-2217(91)90090-I
Lopes, M. C., de Andrade, C. E., de Queiroz, T. A., Resende, M. G. C., & Miyazawa, F. K. (2016). Heuristics for a hub location‐routing problem. Networks, 68(1), 54–90.
Lotfi, R., Kargar, B., Gharehbaghi, A., Afshar, M., Rajabi, M. S., & Mardani, N. (2022). A data-driven robust optimization for multi-objective renewable energy location by considering risk. Environment, Development and Sustainability, 1–22.
Mohseni, S., & Pishvaee, M. S. (2020). Data-driven robust optimization for wastewater sludge-to-biodiesel supply chain design. Computers & Industrial Engineering, 139, 105944.
Mohseni, S., Pishvaee, M. S., & Dashti, R. (2023). Privacy-preserving energy trading management in networked microgrids via data-driven robust optimization assisted by machine learning. Sustainable Energy, Grids and Networks, 34, 101011.
Najafi, S. E., Nozari, H., & Edalatpanah, S. A. (2022). Artificial Intelligence of Things (AIoT) and Industry 4.0–Based Supply Chain (FMCG Industry). A Roadmap for Enabling Industry 4.0 by Artificial Intelligence, 31-41.
Nozari, H., Ghahremani-Nahr, J., Fallah, M., & Szmelter-Jarosz, A. (2022). Assessment of cyber risks in an IoT-based supply chain using a fuzzy decision-making method. International Journal of Innovation in Management, Economics and Social Sciences, 2(1).
O’kelly, M. E. (1987). A quadratic integer program for the location of interacting hub facilities. European Journal of Operational Research, 32(3), 393–404.
Rahmaty, M., & Nozari, H. (2023). Optimization of the hierarchical supply chain in the pharmaceutical industry. Edelweiss Applied Science and Technology, 7(2), 104-123.
Ratli, M., Urošević, D., El Cadi, A. A., Brimberg, J., Mladenović, N., & Todosijević, R. (2022). An efficient heuristic for a hub location routing problem. Optimization Letters, 1–20.
Rodríguez-Martín, I., Salazar-González, J.-J., & Yaman, H. (2014). A branch-and-cut algorithm for the hub location and routing problem. Computers & Operations Research, 50, 161–174. https://doi.org/https://doi.org/10.1016/j.cor.2014.04.014
Shang, C., & You, F. (2019). A data-driven robust optimization approach to scenario-based stochastic model predictive control. Journal of Process Control, 75, 24–39.
Shang, C., Huang, X., & You, F. (2017). Data-driven robust optimization based on kernel learning. Computers & Chemical Engineering, 106, 464–479.
Shen, F., Zhao, L., Du, W., Zhong, W., & Qian, F. (2020). Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach. Applied Energy, 259, 114199.
Wu, Y., Qureshi, A. G., & Yamada, T. (2022). Adaptive large neighborhood decomposition search algorithm for multi-allocation hub location routing problem. European Journal of Operational Research, 302(3), 1113–1127. https://doi.org/https://doi.org/10.1016/j.ejor.2022.02.002
Zhang, C., Wang, Z., & Wang, X. (2022). Machine learning-based data-driven robust optimization approach under uncertainty. Journal of Process Control, 115, 1–11.
Zheng, Y., You, S., Li, X., Bindner, H. W., & Münster, M. (2022). Data-driven robust optimization for optimal scheduling of power to methanol. Energy Conversion and Management, 256, 115338.
