Solving Real-World Fuzzy Quadratic Programming Problems by a Parametric Method

Although fuzzy quadratic programming problems are of the utmost importance in an increasing variety of practical fields, there are remaining technological areas in which has not been tested their applicability or, if tried, have been little studied possibilities. This may be the case of Renewable Energy Assessment, Service Quality, Technology Foresight, Logistics, Systems Biology, etc. With this in mind, the goal of this paper is to apply a parametric approach previously developed by authors to solve some of these problems, specifically the portfolio selection problem by using BM&FBOVESPA data of some Brazilian securities and the economic dispatch problem, which schedules a power generation in an appropriate manner in order to satisfy the load demand.

[1]  Jiafu Tang,et al.  An interactive approach based on a genetic algorithm for a type of quadratic programming problems with fuzzy objective and resources , 1997, Comput. Oper. Res..

[2]  José L. Verdegay,et al.  A parametric convex programming approach applied to portfolio pelection problems with fuzzy costs , 2010, International Conference on Fuzzy Systems.

[3]  Carlos Cruz Corona,et al.  A Parametric Method to Solve Quadratic Programming Problems with Fuzzy Costs , 2009, IFSA/EUSFLAT Conf..

[4]  José L. Verdegay,et al.  Two-phase method to solve fuzzy quadratic programming problems , 2007, 2007 IEEE International Fuzzy Systems Conference.

[5]  Masaaki Ida Portfolio selection problem with interval coefficients , 2003, Appl. Math. Lett..

[6]  Carlos Cruz Corona,et al.  Extending and relating different approaches for solving fuzzy quadratic problems , 2011, Fuzzy Optim. Decis. Mak..

[7]  N.P. Padhy,et al.  Unit commitment-a bibliographical survey , 2004, IEEE Transactions on Power Systems.

[8]  Hans-Jürgen Zimmermann,et al.  Fuzzy mathematical programming , 1983, Comput. Oper. Res..

[9]  Hideo Tanaka,et al.  On Fuzzy-Mathematical Programming , 1973 .

[10]  Christer Carlsson,et al.  A Possibilistic Approach to Selecting Portfolios with Highest Utility Score , 2001, Fuzzy Sets Syst..

[11]  Ruey-Hsun Liang,et al.  A Fuzzy-Optimization Approach for Generation Scheduling With Wind and Solar Energy Systems , 2007, IEEE Transactions on Power Systems.

[12]  Teresa León,et al.  Viability of infeasible portfolio selection problems: A fuzzy approach , 2002, Eur. J. Oper. Res..

[13]  Elias Kyriakides,et al.  Heuristic solution for the nonconvex dispatch of generation in power systems with high wind power share , 2009, 2009 IEEE Power & Energy Society General Meeting.

[14]  Carlos Cruz Corona,et al.  A Survey of Fuzzy Convex Programming Models , 2010, Fuzzy Optimization.

[15]  B. Werners Interactive multiple objective programming subject to flexible constraints , 1987 .

[16]  C. R. Bector,et al.  Application of possibility theory to investment decisions , 2008, Fuzzy Optim. Decis. Mak..

[17]  János Abonyi,et al.  Effective optimization for fuzzy model predictive control , 2004, IEEE Transactions on Fuzzy Systems.

[18]  A. Stuart,et al.  Portfolio Selection: Efficient Diversification of Investments , 1959 .

[19]  Carlos Cruz Corona,et al.  A Parametric Approach to Solve Quadratic Programming Problems with Fuzzy Environment in the Set of Constraints , 2009, IFSA/EUSFLAT Conf..

[20]  Peijun Guo,et al.  Portfolio selection based on fuzzy probabilities and possibility distributions , 2000, Fuzzy Sets Syst..

[21]  K. Feldman Portfolio Selection, Efficient Diversification of Investments . By Harry M. Markowitz (Basil Blackwell, 1991) £25.00 , 1992 .

[22]  Lingfeng Wang,et al.  Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization , 2008 .

[23]  José L. Verdegay,et al.  A Survey of Fuzzy Quadratic Programming , 2008 .

[24]  H. Zimmermann Fuzzy programming and linear programming with several objective functions , 1978 .

[25]  C. R. Bector,et al.  Fuzzy Mathematical Programming and Fuzzy Matrix Games , 2005 .

[26]  Lingfeng Wang,et al.  Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm , 2007 .