Linear optimization: Sensibility analysis 3: Unterschied zwischen den Versionen
| [unmarkierte Version] | [unmarkierte Version] |
(Die Seite wurde neu angelegt: „ == Basic ==“) |
(→Basic) |
||
| Zeile 1: | Zeile 1: | ||
== Basic == | == Basic == | ||
| + | The sensitivity analysis is concerned with the effects and changes in the output data to the optimal solution. You can also ask this question a little differently: to what extent can size change without affecting the essential properties of the solution? | ||
| + | Finding the optimal solution to a linear programming model is important, but one can derive the Sensitivity Analysis important additional information from the linear model ( but there are much more information that can be read from an analysis .) There is a tremendous amount of sensitivity information, or information about what happens when data values are changed. | ||
| + | Other analysises you could only use under the condition that a constancy of the output data is given. However, such a stability of the output data does not exist in reality. | ||
| + | That is a problem because you usually can not modify any data simultaneously, only one size while keeping constant all the other ("ceteris paribus") is changed and asked: In what area can the size in question vary, without this, the solution loses its validity? | ||
| + | By "valid" here are the qualitative characteristics of a solution and not understood quantitatively. A solution is qualitatively different (structural) only by another if at least one pivot operation is needed to establish admissibility and / or optimality again if it was lost in the change. | ||
Version vom 27. Juni 2013, 23:01 Uhr
Basic
The sensitivity analysis is concerned with the effects and changes in the output data to the optimal solution. You can also ask this question a little differently: to what extent can size change without affecting the essential properties of the solution? Finding the optimal solution to a linear programming model is important, but one can derive the Sensitivity Analysis important additional information from the linear model ( but there are much more information that can be read from an analysis .) There is a tremendous amount of sensitivity information, or information about what happens when data values are changed. Other analysises you could only use under the condition that a constancy of the output data is given. However, such a stability of the output data does not exist in reality. That is a problem because you usually can not modify any data simultaneously, only one size while keeping constant all the other ("ceteris paribus") is changed and asked: In what area can the size in question vary, without this, the solution loses its validity? By "valid" here are the qualitative characteristics of a solution and not understood quantitatively. A solution is qualitatively different (structural) only by another if at least one pivot operation is needed to establish admissibility and / or optimality again if it was lost in the change.
