Abstract—Linear Programming Problems are mathematical models used to represent real life situations in the form of linear objective function and constraints various methods are available to solve linear programming problems. When formulating an LP model, systems analysts and researchers often include all possible constraints although some of them may not be binding at the optimal solution. The presence of redundant constraints does not alter the optimum solution(s), but may consume extra computational effort. Redundant constraints identification methods are applied for reducing computational effort in LP problems. But accuracy of the LP problems goes down due to this reduction of loops and constraints. To achieve optimality in accuracy and also in computational effort, we propose an algorithm, called, Hybrid Algorithm, it trains the constraint and parameter before applying the formal methodology.

Index Terms—Linear Programming , Redundant Constraints, Load Forecasting , Training Parameters.

Cite: L.R. Arvind Babu and B.Palaniappan, "Artificial Neural Network Based Hybrid Algorithmic Structure for Solving Linear Programming Problems," International Journal of Computer and Electrical Engineering vol. 2, no. 4, pp. 682-685, 2010.