Abstract
This chapter starts with the formulation of a case containing enough information to illustrate the construction of the decision matrix and problem solving. Probably the most valuable feature of this example is the thorough analysis performed when using several objective functions. The idea is to demonstrate the wealth of information that can be extracted from the model and how it can detect if there have been shortcomings in establishing the mathematical model. It also reveals how the information provided can help the DM in making clear that some concepts are worth reviewing in the light of information that cast a doubt about early stage concepts. It finalizes with analysis of how weights assigned to the diverse objectives may affect the solution.
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- 1.
In fluidized bed boilers the SO2, from the sulphur present in the fuel, combines with limestone and transforms into gypsum, which is collected with the ashes. This system effectively eliminates the SO2, NOx, and dust and generates an additional benefit, which is the production of commercial quality gypsum.
- 2.
Refers to the Kyoto Protocol on Climate Change, an international agreement aimed at reducing emissions of gases causing global warming.
- 3.
This very important economic concept indicates the opportunity that is lost when a resource – in this case the electrical energy which is available for a certain use and is not purchased – is applied to other uses.
- 4.
Shadow prices values are automatically produced when using the Solver, and are found in the ‘Sensitivity’ tab.
- 5.
This proportionality can take place because we are in linear programming. Consequently, when varying the surface of the area in one unit, from 300 km2 to 301 KM2, the objective function will increase by 0.207 MW. Since this is such a small quantity to visualize, a variation of 10 units is adopted, and the area goes then from 300 km2 to 310 km2. This procedure would not be valid if criteria were not linear, because then the 0.207 value would be legitimate only in one point and it could not be possible to extrapolate linearly.
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Munier, N. (2011). Features in Formulating and Solving Decision Problems – Sensitivity Analysis. In: A Strategy for Using Multicriteria Analysis in Decision-Making. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1512-7_5
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DOI: https://doi.org/10.1007/978-94-007-1512-7_5
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