Whenever a model builder models large scale linear programming
problems (LPP),inclusion of structural redundancies in constraints due to inadvert
ency is common. Redundancy may occur in the formulation phase because of bad
source data or to avoid the risk of omitting some relevant constraints while
modelling a problem. The presence of redundant constraints is common situation
that occurs in large LP formulation. These embedded redundant constraints when
present in the model can play havoc with LP solution procedures and greatly
increase solution effort. In 2001, Ilya Ioslovich suggested an approach to identify
the redundant constraints in LPP with help of one of the constraint. This constraint
is said to be most restrictive constraint. The most restrictive constraint is
identified after solving m sub LP problems. It takes lot of computational effort.In
this paper a new approach was proposed for reducing time and more data
manipulation by selecting a restrictive constraint in linear programming problems
to identify the redundant constraints. The proposed algorithm is implemented
using computer programming language C. The computational results are
presented and analyzed with various size of large scale and netlib problems.