Difference: BlendingModels (1 vs. 5)

Revision 52019-11-11 - MichaelOSullivan

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  -- MichaelOSullivan - 20 Feb 2008
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Revision 42008-02-23 - MichaelOSullivan

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Revision 32008-02-21 - MichaelOSullivan

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  Finally, there are a number of restrictions on the inputs (e.g., supply of the input) and the outputs (e.g., the output must be composed of 50% of a given input), each of which becomes a constraint in the linear programme.
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Blending/Mixing Case Studies
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Blending/Mixing Case Studies

 

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Revision 22008-02-20 - MichaelOSullivan

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  Finally, there are a number of restrictions on the inputs (e.g., supply of the input) and the outputs (e.g., the output must be composed of 50% of a given input), each of which becomes a constraint in the linear programme.
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Blending/Mixing Case Studies

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 -- MichaelOSullivan - 20 Feb 2008

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Revision 12008-02-20 - MichaelOSullivan

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Blending or Mixing Models

One common type of problem solved by linear programming is to determine the optimal blend or mix of inputs to produce outputs with the right properties. These problems have a list of inputs and a list of outputs. The decision variables are:

\[ x_{ij} = \text{amount of input $i$ to blend/mix into output $j$} \]

There is a cost (profit) associated with blending/mixing the inputs into the outputs:

\[ c_{ij} = \text{unit cost of blending/mixing input $i$ into output $j$} \]

Finally, there are a number of restrictions on the inputs (e.g., supply of the input) and the outputs (e.g., the output must be composed of 50% of a given input), each of which becomes a constraint in the linear programme.

-- MichaelOSullivan - 20 Feb 2008

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