Allende, G, & B. G. Still. (2012). Solving bi-level programs with the KKT-approach, Springer and Mathematical Programming Society 1 31:37 – 48.
Arora, S.R, & R. Gupta. (2007). Interactive fuzzy goal programming approach for bi-level programming problem, European Journal of Operational Research 176 1151–1166.
Bard, J.F. (1991). Some properties of the bi-level linear programming, Journal of Optimization Theory and Applications 68 371–378.
Bard, J.F. (1998). Practical bi-level optimization: Algorithms and applications, Kluwer Academic Publishers, Dordrecht.
Dempe, S, & A.B. Zemkoho.(2012). On the Karush–Kuhn–Tucker reformulation of the bi-level optimization problem, Nonlinear Analy sis 75 1202–1218.
Facchinei, F, H. Jiang, & L. Qi. (1999). A smoothing method for mathematical programming with equilibrium constraints, Mathematical Programming 85 107-134.
He, X, & C. Li, T. Huang, C. Li. (2014).Neural network for solving convex quadratic bilevel programming problems, Neural Networks, Volume 51, March, Pages 17-25.
Hosseini, E, & I.Nakhai Kamalabadi. (2013).A Genetic Approach for Solving Bi-Level Programming Problems, Advanced Modeling and Optimization, Volume 15, Number 3.
Hosseini, E , & I.Nakhai Kamalabadi. (2013).Solving Linear-Quadratic Bi-Level Programming and Linear-Fractional Bi-Level Programming Problems Using Genetic Based Algorithm, Applied Mathematics and Computational Intellegenc, Volume 2.
Hosseini, E & I.Nakhai Kamalabadi. (2014). Taylor Approach for Solving Non-Linear Bi-level Programming Problem ACSIJ Advances in Computer Science: an International Journal, Vol. 3, Issue 5, No.11 , September.
Hejazi, S.R, A. Memariani, & G. Jahanshahloo. (2002). Linear bi-level programming solution by genetic algorithm, Computers & Operations Research 29 1913–1925.
Hu, T. X, Guo, X. Fu, & Y. Lv, (2010). A neural network approach for solving linear bi-level programming problem, Knowledge-Based Systems 23 239–242.
Khayyal, A.AL. (1985). Minimizing a Quasi-concave Function Over a Convex Set: A Case Solvable by Lagrangian Duality, proceedings, I.E.E.E. International Conference on Systems, Man,and Cybemeties, Tucson AZ 661-663.
Kuen-Ming, L, Ue-Pyng.W, & Hsu-Shih.S. (2007). A hybrid neural network approach to bi-level programming problems, Applied Mathematics Letters 20 880–884
Luce, B, Saïd.H, & Raïd.M. (2013).One-level reformulation of the bi-level Knapsack problem using dynamic programming, Discrete Optimization 10 1–10.
Masatoshi, S, & Takeshi.M. (2012).Stackelberg solutions for random fuzzy two-level linear programming through possibility-based probability model, Expert Systems with Applications 39 10898–10903.
Mathieu, R, L. Pittard, & G. Anandalingam. (1994). Genetic algorithm based approach to bi-level Linear Programming, Operations Research 28 1–21.
Nocedal, J, & S.J. Wright. (2005). Numerical Optimization, Springer-Verlag, , New York.
Pramanik, S, & T.K. Ro. (2009). Fuzzy goal programming approach to multilevel programming problems, European Journal of Operational Research 194 368–376.
Sakava, M, I. Nishizaki, & Y. Uemura. (1997). Interactive fuzzy programming for multilevel linear programming problem, Computers & Mathematics with Applications 36 71–86.
Sinha, S. (2003). Fuzzy programming approach to multi-level programming problems, Fuzzy Sets And Systems 136 189–202.
Thoai, N. V, Y. Yamamoto, & A. Yoshise. (2002). Global optimization method for solving mathematical programs with linear complementary constraints, Institute of Policy and Planning Sciences, University of Tsukuba, Japan 978.
Vicente, L, G. Savard, & J. Judice. (1994). Descent approaches for quadratic bi-level programming, Journal of Optimization Theory and Applications 81 379–399.
Wend, W. T, & U. P. Wen. (2000). A primal-dual interior point algorithm for solving bi-level programming problems, Asia-Pacific J. of Operational Research, 17.
Wang, G. Z, Wan, X. Wang, & Y.Lv. (2008).Genetic algorithm based on simplex method for solving Linear-quadratic bi-level programming problem, Computers and Mathematics with Applications 56 2550–2555.
Wan, Z. G, Wang, & B. Sun. ( 2012). A hybrid intelligent algorithm by combining particle Swarm optimization with chaos searching technique for solving nonlinear bi-level programming Problems, Swarm and Evolutionary Computation.
Wan, Z, L. Mao, & G. Wang. (2014). Estimation of distribution algorithm for a class of nonlinear bilevel programming problems, Information Sciences, Volume 256, 20 January, Pages 184-196.
Xu, P, & L. Wang. (2014). An exact algorithm for the bilevel mixed integer linear programming problem under three simplifying assumptions, Computers & Operations Research, Volume 41, January, Pages 309-318.
Yan, J, Xuyong.L, Chongchao.H, & Xianing.W. (2013). Application of particle swarm optimization based on CHKS smoothing function for solving nonlinear bi-level programming problem, Applied Mathematics and Computation 219 4332–4339.
Yibing, Lv, Hu. Tiesong, & Wang. Guangmin. (2007). A penalty function method Based on Kuhn–Tucker condition for solving linear bilevel programming, Applied Mathematics and Computation 1 88 808–813.
Zhang , G, J. Lu , J. Montero , & Y. Zeng , Model. (2010). solution concept, and Kth-best algorithm for linear tri-level, programming Information Sciences 180 481–492
Zhongping, W, & Guangmin.W. (2008).An Interactive Fuzzy Decision Making Method for a Class of Bi-level Programming, Fifth International Conference on Fuzzy Systems and Knowledge Discovery.
Zheng, Y, J. Liu, & Z. Wan. (2014). Interactive fuzzy decision making method for solving bi-level programming problem, Applied Mathematical Modelling, Volume 38, Issue 13, 1 July, Pages 3136-3141.