Following are the **disadvantages of linear programming**.

**Linearity of relations:** A primary requirement of linear programming
is that the objective function and every constraint must be linear.
However, in real life situations, several business and industrial
problems are nonlinear in nature.

**Single objective:** Linear programming takes into account a
single objective only, i.e., profit maximization or cost minimization.
However, in today's dynamic business environment, there is no single
universal objective for all organizations.

**Certainty:** Linear Programming assumes that the values of
co-efficient of decision variables are known with certainty.
Due to this restrictive assumption, linear programming cannot be applied
to a wide variety of problems where values of the coefficients are
probabilistic.

**Constant parameters:** Parameters appearing in LP are assumed
to be constant, but in practical situations it is not so.

**Divisibility:** In linear programming, the decision variables
are allowed to take non-negative integer as well as fractional values.
However, we quite often face situations where the planning models
contain integer valued variables. For instance, trucks in a fleet,
generators in a powerhouse, pieces of equipment, investment alternatives
and there are a myriad of other examples. Rounding off the solution
to the nearest integer will not yield an optimal solution. In such
cases, linear programming techniques cannot be used.