The assumption that is necessary for a linear programming model to be appropriate and that ensures that the value of the objective function and the total resources used can be found by summing the objective function contribution and the resources used for all decision variables is known as

a. additivity.
b. divisibility.
c. negativity.
d. proportionality.

A. Additivity

Explanation:

Additivity simply means that the values of an objective function and total resources used can be found by adding all the contributions made by the objective functions and the decision variables of all resources used. That is, it assumes that the overall of an objective function is found by adding the contribution of each objective function to the overall. In additivity, interaction between variables doesnt exist.

Additivity.

Explanation:

Linear programming is defined as a process by which optimisation of a system of linear contraints and a linear objective function occurs.

Linear programming is based on four assumptions: additivity, certainty, proportionality, and divisibility.

Additivity assumption of linear programming states that the total profit of a linear function is made up of summation of profit of each product seperately. In a similar way the total resources used can be found by summing the objective function contribution and the resources used for all decision variables.