References
- Blanco-Fernández, A., Casals, M.R., Colubi, A., Corral, N., García-Bárzana, M., Gil, M.A.,… Sinova, B. (2014). A distance-based statistical analysis of fuzzy number-valued data. International Journal of Approximate Reasoning, 55, 1487–1501.
- Cěrný, M., & Hladík, M. (2014). The complexity of computation and approximation of the t-ratio over one-dimensional interval data. Computational Statistics & Data Analysis, 80(0), 26–43.
- Chiang, A.J., & Jeang, A. (2015). Stochastic project management via computer simulation and optimisation method. International Journal of Systems Science: Operations
Logistics, 2, 211–230.
- Dempster, A.P. (1968). Upper and lower probabilities generated by a random closed interval. Annals of Mathematical Statistics, 39, 957–966.
- Dubois, D., & Prade, H. (1987). The mean value of a fuzzy number. Fuzzy Sets and Systems, 24(3), 279–300.
- Ferson, S., & Ginzburg, L. (2005). Exact bounds on finite populations of interval data. Reliable Computing, 11, 207–233.
- Ferson, S., Ginzburg, L., Kreinovich, V., Longpré, L., & Aviles, M. (2002). Computing variance for interval data is NP-hard. ACM SIGACT News, 33(2), 108–118.
- Grzegorzewski, P. (2004). Distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the Hausdorff metric. Fuzzy Sets and Systems, 148, 319–328.
- Hansen, E. (1997). Sharpness in interval computations. Reliable Computing, 3(1), 17–29.
- Hildenbrand, W. (1975). Core and equilibria of a large economy (Vol. 85, pp. 672–674). Princeton, NJ: Princeton University Press.
- Kall, P., & Mayer, J. (2004). Stochastic linear programming. New York, NY: Springer.
- Kearfott, R.B. (1996). Rigorous global search: Continuous problems. Dordrecht: Kluwer Academic Publishers.
- Kreinovich, V., Nguyen, H.T., & Wu, B. (2007). On-line algorithms for computing mean and variance of interval data, and their use in intelligent systems. Information Sciences, 177, 3228–3238.
- Kruse, R., & Meyer, K.D. (1987). Statistics with vague data. Dordrecht: D. Reidel.
- Lu, H.W., Cao, M.F., Li, J., Huang, G.H., & He, L. (2015). An inexact programming approach for Urban electric power systems management under random-interval-parameter uncertainty. Applied Mathematical Modelling, 39, 1757–1768.
- Mathéron, G. (1975). Random sets and integral geometry. New York, NY: John Wiley & Sons.
- Miranda, E., Couso, I., & Gil, P. (2005). Random intervals as a model for imprecise information. Fuzzy Sets and Systems, 154, 386–412.
- Nguyen, H.T., Kreinovich, V., Wu, B., & Xiang, G. (2012). Computing statistics under interval and fuzzy uncertainty. In Kacprzyk, Janusz (Series Ed.) Applications to computer science and engineering. Studies in Computational Intelligence (Vol. 393, pp. 19–50). Berlin: Springer.
- Ramos-Guajardo, A.B., & Lubiano, M.A. (2012). K-sample tests for equality of variances of random fuzzy sets. Computational Statistics and Data Analysis, 56, 956–966.
- Sakawa, M., Nishizaki, I., & Katagiri, H. (2011). Fuzzy stochastic multi-objective programming. Higashi-Hiroshima: Springer.
- Trutschnig, W., González-Rodríguez, G., Colubi, A., & Ángeles Gil, M. (2009). A new family of metrics for compact, convex (fuzzy) sets based on a generalized concept of mid and spread. Information Sciences, 179, 3964–3972.
- Xu, M., Wang, X., & Zhao, L. (2014). Predicted supply chain resilience based on structural evolution against random supply disruptions. International Journal of Systems Science: Operations & Logistics, 1, 105–117.
- Xu, Z. (2012). Fuzzy ordered distance measures. Fuzzy Optimization and Decision-Making, 11, 73–97.