| dc.description.abstract | Linear programming is mathematical programming developed to deal with optimization problems involving linear
equations in the objective and constraint functions. One of the basic assumptions in linear programming problems is the
certainty assumption. Assumption of certainty shows that all coefficients variable or decision variables in the model are
constants that are known with certainty. However, in real situations or problems, there may be uncertain coefficients or
decision variables. Based on the concept and theory of interval analysis, this uncertainty problem is anticipated by
making approximate values in intervals to develop linear interval programming. The development of interval linear
programming starts from linear programming with interval-shaped coefficients, both in the coefficient of the objective
function and the coefficient of the constraint function. It was subsequently developed into linear programming with
coefficients and decision variables in intervals, commonly known as interval linear programming. Until now, the
completion of interval linear programming is based on the calculation of the interval limit. The initial procedure for the
solution is to change the linear programming model with interval variables into two classical linear programming
models. Finally, the optimal solution in the form of intervals is obtained by constructing two models. This paper provides
an alternative solution to directly solve the linear interval programming problem without building it into two models.
The solution is done using the interval arithmetic approach, while the method used is the modified interior-point method. | en_US |
