Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval research logistics quarterly, 2((1-2)), 83–97. DOI:10.1007/978-3-540-68279-0_2
 Das, S. K., & Deo, N. (1990). Parallel hungarian algorithm. Computer systems science and engineering, 5(3), 131-136. https://stars.library.ucf.edu/scopus1990/1508/
 Ishibuchi, H., & Tanaka, H. (1990). Multiobjective programming in optimization of the interval objective function. European journal of operational research, 48(2), 219–225. DOI:10.1016/0377-2217(90)90375-L
 Aldous, D. (1992). Asymptotics in the random assignment problem. Probability theory and related fields, 93(4), 507–534. DOI:10.1007/BF01192719
 Li, T., Li, Y., & Qian, Y. (2016). Improved Hungarian algorithm for assignment problems of serial-parallel systems. Journal of systems engineering and electronics, 27(4), 858–870.
 Rajabi-Alni, F., & Bagheri, A. (2022). Computing a many-to-many matching with demands and capacities between two sets using the Hungarian algorithm. Journal of mathematics, 2023. https://doi.org/10.1155/2023/7761902
 Bai, G. Z. (2009). Grey assignment problems. In Fuzzy information and engineering (pp. 245-250). Springer Berlin Heidelberg. https://link.springer.com/chapter/10.1007/978-3-540-88914-4_31
 Majumdar, S. (2013). Interval linear assignment problems. Journal of applied mathematics, 1(6), 14–16.
 Serratosa, F. (2015). Computation of graph edit distance: Reasoning about optimality and speed-up. Image and vision computing, 40, 38–48.
 Serratosa, F., & Cortés, X. (2015). Graph edit distance: Moving from global to local structure to solve the graph-matching problem. Pattern recognition letters, 65, 204–210.
 Lan, S., Fan, W., Liu, T., & Yang, S. (2019). A hybrid SCA--VNS meta-heuristic based on Iterated Hungarian algorithm for physicians and medical staff scheduling problem in outpatient department of large hospitals with multiple branches. Applied soft computing, 85, 105813. https://www.sciencedirect.com/science/article/abs/pii/S1568494619305940
 Yadav, S. S., Lopes, P. A. C., Ilic, A., & Patra, S. K. (2019). Hungarian algorithm for subcarrier assignment problem using GPU and CUDA. International journal of communication systems, 32(4), e3884. https://doi.org/10.1002/dac.3884
 Khan, A. A., Adve, R. S., & Yu, W. (2020). Optimizing downlink resource allocation in multiuser MIMO networks via fractional programming and the hungarian algorithm. IEEE transactions on wireless communications, 19(8), 5162–5175.
 Yang, X., Zhao, N., & Yu, S. (2020). Combined internal trucks allocation of multiple container terminals with hungarian algorithm. Journal of coastal research, 103(SI), 923–927.
 Kumarnath, J., & Batri, K. (2021). An optimized traffic grooming through modified pso based iterative hungarian algorithm in optical networks. Information technology and control, 50(3), 546–557.
 MacLean, M. T., Lysikowski, J. R., Rege, R. V, Sendelbach, D. M., & Mihalic, A. P. (2021). Optimizing medical student clerkship schedules using a novel application of the Hungarian algorithm. Academic medicine, 96(6), 864–868.
 Stevens, P., & Sciacchitano, A. (2021). Application of clustering and the Hungarian algorithm to the problem of consistent vortex tracking in incompressible flowfields. Experiments in fluids, 62, 1–11.
 Zhu, Z., Lou, K., Ge, H., Xu, Q., & Wu, X. (2022). Infrared target detection based on Gaussian model and Hungarian algorithm. Enterprise information systems, 16(10–11), 1573–1586.
 Zhang, S., Xue, Y., Zhang, H., Zhou, X., Li, K., & Liu, R. (2023). Improved Hungarian algorithm--based task scheduling optimization strategy for remote sensing big data processing. Geo-spatial information science, 1–14. https://doi.org/10.1080/10095020.2023.2178339
 Xie, N., & Liu, S. (2010). Novel methods on comparing grey numbers. Applied mathematical modelling, 34(2), 415–423.
 Li, G. D., Yamaguchi, D., & Nagai, M. (2007). A grey-based decision-making approach to the supplier selection problem. Mathematical and computer modelling, 46(3–4), 573–581.
 Tseng, M. L. (2009). A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert systems with applications, 36(4), 7738–7748.
 Liu, S., & Lin, Y. (2006). Grey clusters and grey statistical evaluations. In Grey information: theory and practical applications (pp. 139–189). Springer. https://link.springer.com/chapter/10.1007/1-84628-342-6_6
 Sadeghieh, A., Dehghanbaghi, M., Dabbaghi, A., & Barak, S. (2012). A genetic algorithm based grey goal programming (G3) approach for parts supplier evaluation and selection. International journal of production research, 50(16), 4612–4630.
 Moore, R. E. (1979). Methods and applications of interval analysis. SIAM. https://epubs.siam.org/doi/pdf/10.1137/1.9781611970906.bm
 Winston, W. L. (2004). Operations research: applications and algorithm. Thomson Learning, Inc. https://www.academia.edu/download/58159784/Winston_4th_ed.pdf
 Sayadi, M. K., Heydari, M., & Shahanaghi, K. (2009). Extension of VIKOR method for decision making problem with interval numbers. Applied mathematical modelling, 33(5), 2257–2262.
 Sevastianov, P. (2007). Numerical methods for interval and fuzzy number comparison based on the probabilistic approach and Dempster--Shafer theory. Information sciences, 177(21), 4645–4661.
 Parkouhi, S. V., & Ghadikolaei, A. S. (2017). A resilience approach for supplier selection: Using Fuzzy Analytic Network Process and grey VIKOR techniques. Journal of cleaner production, 161, 431–451.
 Lin, Y. H., Lee, P. C., & Ting, H.-I. (2008). Dynamic multi-attribute decision making model with grey number evaluations. Expert systems with applications, 35(4), 1638–1644.
 Nguyen, H. T., Dawal, S. Z. M., Nukman, Y., & Aoyama, H. (2014). A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes. Expert systems with applications, 41(6), 3078–3090.
 Esangbedo, M. O., & Che, A. (2016). Grey weighted sum model for evaluating business environment in West Africa. Mathematical problems in engineering, 2016. https://www.hindawi.com/journals/mpe/2016/3824350/abs/
 Baykasouglu, A., Subulan, K., & Karaslan, F. S. (2016). A new fuzzy linear assignment method for multi-attribute decision making with an application to spare parts inventory classification. Applied soft computing, 42, 1–17.