DecisionsConvolutions.cs

usingSystem.Collections.Generic;
usingSystem.Linq;

namespaceAlgorithms.Other;

/// <summary>
/// Almost all real complex decision-making task is described by more than one criterion.
/// There are different methods to select the best decisions from the defined set of decisions.
/// This class contains implementations of the popular convolution methods: linear and maxmin.
/// </summary>
publicstaticclassDecisionsConvolutions
{
/// <summary>
/// This method implements the linear method of decision selection. It is based on
/// the calculation of the "value" for each decision and the selection of the most
/// valuable one.
/// </summary>
/// <param name="matrix">Contains a collection of the criteria sets.</param>
/// <param name="priorities">Contains a set of priorities for each criterion.</param>
/// <returns>The most effective decision that is represented by a set of criterias.</returns>
publicstaticList<decimal>Linear(List<List<decimal>>matrix,List<decimal>priorities)
{
vardecisionValues=newList<decimal>();

foreach(vardecisioninmatrix)
{
decimalsum=0;
for(inti=0;i<decision.Count;i++)
{
sum+=decision[i]*priorities[i];
}

decisionValues.Add(sum);
}

decimalbestDecisionValue=decisionValues.Max();
intbestDecisionIndex=decisionValues.IndexOf(bestDecisionValue);

returnmatrix[bestDecisionIndex];
}

/// <summary>
/// This method implements maxmin method of the decision selection. It is based on
/// the calculation of the least criteria value and comparison of decisions based
/// on the calculated results.
/// </summary>
/// <param name="matrix">Contains a collection of the criteria sets.</param>
/// <param name="priorities">Contains a set of priorities for each criterion.</param>
/// <returns>The most effective decision that is represented by a set of criterias.</returns>
publicstaticList<decimal>MaxMin(List<List<decimal>>matrix,List<decimal>priorities)
{
vardecisionValues=newList<decimal>();

foreach(vardecisioninmatrix)
{
decimalminValue=decimal.MaxValue;
for(inti=0;i<decision.Count;i++)
{
decimalresult=decision[i]*priorities[i];
if(result<minValue)
{
minValue=result;
}
}

decisionValues.Add(minValue);
}

decimalbestDecisionValue=decisionValues.Max();
intbestDecisionIndex=decisionValues.IndexOf(bestDecisionValue);

returnmatrix[bestDecisionIndex];
}
}