Linear programming has two major limitations from its applications point of view:
(i) single objective function, and
(ii) same unit of measurement of various resources.
The LP model has a single objective function to be optimized such as profit maximization, cost minimization, etc.
However, in actual practice, the decision-maker may not strive for a single objective.
That is, the decision maker may desire to get simultaneous solutions to a complex system of competing objectives.
The solution of any LP problem is based on the cardinal value (the number that expresses exact amount such as, 1, 2, 3, …) such as profit or cost, whereas GP allows an ordinal solution.
Since it may not be possible to obtain information about the value or cost of a goal (the specific numerical target value desired to achieve) or a subgoal, therefore their upper and lower limits are determined.
Usually, priority of a desired goal is assigned and then these priorities are ranked in an ordinal sequence.
Whenever there are multiple units of measurement goals, LP incorporates only one of these goals in the objective function and treats the remaining goals as constraints.
Since the optimal solution must satisfy all the constraints, this implies that
(a) the several goals within the constraining equations are of equal importance, and
(b) these goals have absolute priority over the goal incorporated into the objective function.
In goal programming, goals are given an ordinal ranking in terms of their contribution or importance to the organization.