| Provider: | PyXMCDA |
|---|---|
| Version: | 1.0 |
Perform ACUTA computation on provided inputs. Decision Maker’s Preferences can be provided using either a global ranking of reference alternatives, or the pair-wise alternatives preferences and indifferences. Outputs optimal valueFunctions along their valuesErrors.
The service generate discrete functions for criteria with qualitative scales. Number of segments will be still be used to compute a continuous marginal utility function. It is therefore advised to use (n - 1) as the number of segments (with n the number of labels of the qualitative scale), while using incremented integer values in such scale definition (e.g. { 1, 2, 3,…, n}). Labels of the scale are used as the abscissa.
This service raises an error if the solution found does not give the same ranking of reference alternatives than in inputs (even with application of the values errors, called sigma in (Munda, 2005).
Also it outputs the UTA* optimum solution if analytic center computation fails.
The implementation and indexing conventions are based on:
Munda, G. (2005). UTA Methods. In Multiple criteria decision analysis: State of the art surveys (pp 297-343). Springer, New York, NY.
N.B: This service uses the python module Pulp for representing and solving UTA problems.
(For outputs, see below)
Optional: yes, enabled by default
The alternatives. Only used to exclude inactive alternatives from computations (all are considered if not provided).
Optional: yes, enabled by default
The criteria. Only used to exclude inactive criteria from computations (all are considered if not provided).
The performance table.
Optional: yes, enabled by default
Optional: yes, enabled by default
Optional: yes, enabled by default
Optional: yes, enabled by default
Number of segments per marginal utility function.
Parameters of the method
Parameter values can be defined via the GUI or the XMCDA file, by default via GUI.
Name: Inputs alternatives
How to provide alternatives hierarchy
Name: Discrimination threshold
Discrimination threshold value.
Name: Monotonicity threshold
Monotonicity threshold value.
Name: Analytic center epsilon
Absolute tolerance for analytic center computation (closeness to optimal solution).
Name: Newton step coefficient
Coefficient applied to compute newton steps in analytic center computation.
Name: Significative figures
Number of significative figures in outputs values.
Name: Absolute tolerance
Float absolute tolerance used when computing Kendall tau.
Name: Solver
Which solver is used.
Tag: programParameters
Code:
<programParameters>
<!-- %N -->
<programParameter id="discrimination_threshold" name="Discrimination threshold">
<values>
<value>
<real>%2</real>
</value>
</values>
</programParameter>
<programParameter id="monotonicity_threshold" name="Monotonicity threshold">
<values>
<value>
<real>%3</real>
</value>
</values>
</programParameter>
<programParameter id="ac_epsilon" name="Analytic center epsilon">
<values>
<value>
<real>%7</real>
</value>
</values>
</programParameter>
<programParameter id="newton_step_coeff" name="Newton step coefficient">
<values>
<value>
<real>%8</real>
</value>
</values>
</programParameter>
<programParameter id="significative_figures" name="Significative figures">
<values>
<value>
<integer>%5</integer>
</value>
</values>
</programParameter>
<programParameter id="atol" name="Absolute tolerance">
<values>
<value>
<real>%6</real>
</value>
</values>
</programParameter>
<programParameter id="solver" name="Solver">
<values>
<value>
<label>%4</label>
</value>
</values>
</programParameter>
</programParameters>
Optimal value functions found by the service.
Status messages.
For further technical details on the web service underlying this program, have a look at its documentation here.