Linear optimization: Parametrical objective function 4: Unterschied zwischen den Versionen

Version vom 26. Juni 2013, 17:05 Uhr

Parametrical objective function is a special part of linear optimization. The foundation of parametrical optimization is the sensitivity analysis. Compared to the sensitivity analysis the Parametrical objective function makes a statement about large changes in the input data.

Basic Knowledge

To include large changes in the input data you have to add a new variable "${\displaystyle q}$". For simple cases you just summate the new variable "${\displaystyle q}$" multiplicated with a constant vektor " ${\displaystyle \beta }$ " to the objective function. In this case the constant vektor is the value which change the input parameter "${\displaystyle c}$".

The objectiv function is now with a parameter:

Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): c(q)=c+q* ${\displaystyle \beta }$

Thereby there is a new optimization problem which can be solved.

${\displaystyle P(q)}$${\displaystyle =}$${\displaystyle (c+}$${\displaystyle q}$${\displaystyle \beta )}$${\displaystyle ^{T}x}$ Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): \rightarrow

${\displaystyle max!}$

${\displaystyle Ax=b}$

${\displaystyle x}$ Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): \ge

${\displaystyle 0}$

Exemplification

From this tableau you can read out the profit function with a variable inside:

Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): G(q)= q*x_1+500x_2-36000

Transformed into the Simplex-Tableau: