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Research Article Open access CC BY 4.0

On the Solution of Bi-level Large Scale Quadratic Programming Problem with Stochastic Parameters in the Constraints

O. E. Emam, S. A. Kholeif, S. M. Azzam

Journal of Advances in Mathematics and Computer Science · pp. 571–582 · Published 18 Nov 2014

10.9734/BJMCS/2015/14371

Abstract

In this paper, the bi-level large scale quadratic programming problem with stochastic parameters in the constraints (SBLLSQPP) is solved. Randomness is assumed only on the right-hand side of the constraints and the random variables are assumed to be normally distributed. The main features of the proposed solution procedure are based on convert the probabilistic nature of this problem into an equivalent deterministic, Taylor transformation and decomposition algorithms. An illustrative numerical example is given to clarify the main results developed in the paper.

Large scale problems stochastic programming quadratic programming bi–level programming.

References (7)

  1. 1 The Decomposition Algorithm for Linear Programs [DOI]
  2. 2 An Algorithm for Finding All Vertices of Convex Polyhedral Sets [DOI]
  3. 3 Interactive approach to bi-level integer multi-objective fractional programming problem [DOI]
  4. 4 SOLVING BI-LEVEL PROGRAMMING PROBLEMS ON USING GLOBAL CRITERION METHOD WITH AN INTERVAL APPROACH
  5. 5 Fuzzy linear fractional bi-level multi-objective programming problems [DOI]
  6. 6 Chance Constrained Linear Plus Linear Fractional Bi-level Programming Problem [DOI]
  7. 7 An Algorithm for Solving Bi-Level Integer Linear Fractional Programming Problem based on Fuzzy Approach

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