private void Prepare(TransitionMatrix transitionMatrix, TransitionMatrixInput inputTransitionMatrix, XRandom rng)
{
_matrixSize = inputTransitionMatrix.WorkingStations.Count;
TruncatedDiscreteNormal unifyingDistribution = null;
//darf lowerBound (also Mindestdauer einer Operation) 0 sein? -> wenn 0 selten vorkommt (also z.B. Zeiteinheit nicht Minuten, sondern Sekunden sind), dann ok
if (inputTransitionMatrix.GeneralMachiningTimeParameterSet != null)
{
var normalDistribution = Normal.WithMeanVariance(
inputTransitionMatrix.GeneralMachiningTimeParameterSet.MeanMachiningTime,
inputTransitionMatrix.GeneralMachiningTimeParameterSet.VarianceMachiningTime,
rng.GetRng());
unifyingDistribution = new TruncatedDiscreteNormal(0, null, normalDistribution);
}
var workingStations = inputTransitionMatrix.WorkingStations.ToArray();
for (var i = 0; i < _matrixSize; i++)
{
TruncatedDiscreteNormal truncatedDiscreteNormalDistribution;
if (unifyingDistribution != null)
{
truncatedDiscreteNormalDistribution = unifyingDistribution;
}
else
{
var machiningTime = workingStations[i].MachiningTimeParameterSet;
var normalDistribution = Normal.WithMeanVariance(machiningTime.MeanMachiningTime,
machiningTime.VarianceMachiningTime, rng.GetRng());
truncatedDiscreteNormalDistribution = new TruncatedDiscreteNormal(0, null, normalDistribution);
}
_machiningTimeDistributions.Add(truncatedDiscreteNormalDistribution);
}
if (inputTransitionMatrix.ExtendedTransitionMatrix)
{
_matrixSize++;
}
for (var i = 0; i < _matrixSize; i++)
{
var row = new List <KeyValuePair <int, double> >();
_cumulatedProbabilities.Add(row);
for (var j = 0; j < _matrixSize; j++)
{
row.Add(new KeyValuePair <int, double>(j, transitionMatrix.Pi[i, j]));
}
row.Sort(delegate(KeyValuePair <int, double> o1, KeyValuePair <int, double> o2)
{
if (o1.Value > o2.Value)
{
return(-1);
}
return(1);
});
}
}
private void GenerateParts(ProductStructureInput inputParameters, ProductStructure productStructure,
List <HashSet <long> > availableNodes, MasterTableArticleType articleTypes, MasterTableUnit units,
M_Unit[] unitCol, XRandom rng)
{
bool sampleWorkPlanLength = inputParameters.MeanWorkPlanLength != null &&
inputParameters.VarianceWorkPlanLength != null;
TruncatedDiscreteNormal truncatedDiscreteNormalDistribution = null;
if (sampleWorkPlanLength)
{
truncatedDiscreteNormalDistribution = new TruncatedDiscreteNormal(1, null,
Normal.WithMeanVariance((double)inputParameters.MeanWorkPlanLength,
(double)inputParameters.VarianceWorkPlanLength, rng.GetRng()));
}
for (var i = 1; i <= inputParameters.DepthOfAssembly; i++)
{
productStructure.NodesCounter += GeneratePartsForEachLevel(inputParameters, productStructure, availableNodes,
articleTypes, units, unitCol, rng, i, sampleWorkPlanLength, truncatedDiscreteNormalDistribution);
}
}
private static void DeterminationOfEdgeWeights(ProductStructureInput inputParameters, ProductStructure productStructure, XRandom rng)
{
var logNormalDistribution = LogNormal.WithMeanVariance(inputParameters.MeanIncomingMaterialAmount,
Math.Pow(inputParameters.StdDevIncomingMaterialAmount, 2), rng.GetRng());
foreach (var edge in productStructure.Edges)
{
edge.Weight = logNormalDistribution.Sample();
}
}
