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A novel interval linear programming based on probabilistic dominance. (English) Zbl 1522.90285

Summary: This study investigates a novel interval linear programming based on probabilistic dominance. Firstly, the definition of interval linear programming is briefly reviewed. Then a new interval linear programming model is presented based on probabilistic dominance. The probabilistic dominance index treats the intervals as uniformly distributed variables and the interval inequality relation is further defined by probability. To deal with non-linearity in probabilistic dominance index, sequential quadratic programming is used to solve the problem and the performance measure approach is proposed to overcome the convergence difficulties. The determination and sensitivity analysis of the target performance measure are discussed to assess the sequential quadratic programming algorithm. Meanwhile, the extension of the proposed method to fuzzy interval linear programming is discussed. Furthermore, the proposed method is applied to the design of the plane truss structure with interval parameters. Finally, the effectiveness and rationality of the developed method are demonstrated by two mathematical examples and one interval parametric plane truss structure optimization example.

MSC:

90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
90C05 Linear programming
90C20 Quadratic programming
Full Text: DOI

References:

[1] Dantzig, G. B.; Wolfe, P., The decomposition algorithm for linear programs, Econometrica, 767-778 (1961) · Zbl 0104.14305
[2] Liu, B. D., Theory and Practice of Uncertain Programming (2009), Springer: Springer Berlin · Zbl 1158.90010
[3] Slowinski, R., Fuzzy Sets in Decision Analysis, Operations Research and Statistics (1999), Kluwer Academic Publishers: Kluwer Academic Publishers Toulouse
[4] Lyu, Z.; Qiu, Z. P., An iteration method for predicting static response of nonlinear structural systems with non-deterministic parameters, Appl. Math. Model., 68, 48-65 (2019) · Zbl 1481.74714
[5] Lyu, Z.; Yang, Y. W.; Xia, H. J., Unknown-but-bounded uncertainty propagation in spacecraft structural system: interval reduced basis method and its integrated framework, Aerosp. Sci. Technol., 92, 945-957 (2019)
[6] Wang, L.; Wang, X. J.; Li, Y. L.; Hu, J. X., A non-probabilistic time-variant reliable control method for structural vibration suppression problems with interval uncertainties, Mech. Syst. Signal Process., 115, 301-322 (2019)
[7] Wang, L.; Liu, Y. R., A novel method of distributed dynamic load identification for aircraft structure considering multi-source uncertainties, Struct. Multidiscip. Optim., 61, 1-24 (2020)
[8] Wang, X. J.; Shi, Q. H.; Fan, W. C.; Wang, R. X.; Wang, L., Comparison of the reliability-based and safety factor methods for structural design, Appl. Math. Model., 72, 68-84 (2019) · Zbl 1481.90147
[9] Wang, X. J.; Ren, Q.; Chen, W. P.; Liu, Y. S.; Wang, L.; Ding, X. Y., Structural design optimization based on the moving baseline strategy, Acta Mech. Solida Sin., 1-20 (2019)
[10] Wang, L.; Liu, Y. R.; Gu, K. X.; Wu, T., A radial basis function artificial neural network (RBF ANN) based method for uncertain distributed force reconstruction considering signal noises and material dispersion, Comput. Methods Appl. Mech. Eng., 364, Article 112954 pp. (2020) · Zbl 1442.74254
[11] Wang, L.; Liu, J. X.; Li, Y. L., The optimal controller design framework for PID-based vibration active control systems via non-probabilistic time-dependent reliability measure, ISA Trans., 105, 129-145 (2020)
[12] Tong, S. C., Interval number and fuzzy number linear programmings, Fuzzy Sets Syst., 66, 301-306 (1994)
[13] Mráz, F., Calculating the exact bounds of optimal values in LP with interval coefficients, Ann. Oper. Res., 81, 51-62 (1998) · Zbl 0908.90188
[14] Chinneck, J. W.; Ramadan, K., Linear programming with interval coefficients, J. Oper. Res. Soc., 51, 209-220 (2000) · Zbl 1107.90420
[15] Fiedler, M.; Nedoma, J.; Ramík, J.; Rohn, J.; Zimmermann, K., Linear Optimization Problems with Inexact Data (2006), Springer-Verlag: Springer-Verlag New York · Zbl 1106.90051
[16] Rohn, J., Stability of the optimal basis of a linear program under uncertainty, Oper. Res. Lett., 13, 9-12 (1993) · Zbl 0777.90029
[17] Koníckocá, J., Sufficient condition of basis stability of an interval linear programming problem, Z. Angew. Math. Mech., 81, 677-678 (2001) · Zbl 0983.90037
[18] Hladík, M., How to determine basis stability in interval linear programming, Optim. Lett., 8, 375-389 (2014) · Zbl 1288.90047
[19] Liu, X. W.; Da, Q. L., A satisfactory solution for interval number linear programming, J. Syst. Eng. Electron., 14, 123-128 (1999)
[20] Kono, Y.; Yamaguchi, T., An interactive method for multi-goal programming problems with fuzzy solution, Int. Ser. Manag. Sci./Oper. Res., 37, 603-610 (1992)
[21] Sengupta, A.; Pal, T. K.; Chakraborty, D., Interpretation of inequality constraints involving interval coefficients and a solution to interval linear programming, Fuzzy Sets Syst., 119, 129-138 (2001) · Zbl 1044.90534
[22] Guo, J. P.; Wu, Y. H., Standard form of interval linear programming and its solution, Syst. Eng., 21, 79-82 (2003)
[23] Chen, M. Z.; Wang, S. G.; Wang, P. P.; Ye, X. X., A new equivalent transformation for interval inequality constraints of interval linear programming, Fuzzy Optim. Decis. Mak., 15, 155-175 (2016) · Zbl 1428.90098
[24] Zhang, Q.; Fan, Z. P.; Pan, D. H., A ranking approach for interval numbers in uncertain multiple attribute decision making problems, Syst. Eng. - Theory & Practice, 19, 129-133 (1999)
[25] Jiang, C.; Han, X.; Liu, G. R., A nonlinear interval number programming method for uncertain optimization problems, Eur. J. Oper. Res., 188, 1-13 (2008) · Zbl 1135.90044
[26] Jiang, C.; Han, X., A new uncertain optimization method based on intervals and an approximation management model, Comput. Model. Eng. Sci., 22, 97-118 (2007) · Zbl 1184.90177
[27] Zhao, Z. H.; Han, X.; Jiang, C.; Zhou, X. X., A nonlinear interval-based optimization method with local-densifying approximation technique, Struct. Multidiscip. Optim., 42, 559-573 (2010)
[28] Huynh, V. N.; Nakamori, Y.; Lawry, J., A probability-based approach to comparison of fuzzy numbers and applications to target-oriented decision making, IEEE Trans. Fuzzy Syst., 16, 371-387 (2008)
[29] Delgado, M.; Verdegay, J. L.; Vila, M. A., A general model for fuzzy linear programming, Fuzzy Sets Syst., 29, 21-29 (1989) · Zbl 0662.90049
[30] Wang, D., An inexact approach for linear programming problems with fuzzy objective and resources, Fuzzy Sets Syst., 89, 61-68 (1997)
[31] Zimmermann, H.-J., Fuzzy Set Theory-and Its Applications (1991), Kluwer Academic: Kluwer Academic Norwell · Zbl 0719.04002
[32] Allahdadi, M.; Nehi, H. M., The optimal solution set of the interval linear programming problems, Optim. Lett., 7, 1893-1911 (2013) · Zbl 1311.90069
[33] Dai, S. H.; Wang, M. O., Reliability Analysis in Engineering Applications (1992), Van Nostrand Reinhold: Van Nostrand Reinhold New York
[34] Ayyub, B. M.; McCuen, R. H., Probability, Statistics, & Reliability for Engineers (1997), CRC Press: CRC Press New York · Zbl 0899.62001
[35] Tu, J.; Choi, K. K.; Park, Y. H., A new study on reliability-based design optimization, J. Mech. Des., 121, 4, 557-564 (1999)
[36] Wang, X. J.; Qiu, Z. P.; Elishakoff, I., Non-probabilistic set-theoretic model for structural safety measure, Acta Mech., 198, 51-64 (2008) · Zbl 1151.74378
[37] Klir, G. J., Uncertainty and Information: Foundations of Generalized Information Theory (2006), Wiley-Interscience: Wiley-Interscience New York · Zbl 1280.94004
[38] Alefeld, G.; Herzberger, J., Introduction to Interval Computations (1983), Academic Press: Academic Press London · Zbl 0552.65041
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