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 static long GeneratePartsForEachLevel(ProductStructureInput inputParameters,
ProductStructure productStructure, List <HashSet <long> > availableNodes, MasterTableArticleType articleTypes,
MasterTableUnit units, M_Unit[] unitCol, XRandom rng, int i, bool sampleWorkPlanLength,
TruncatedDiscreteNormal truncatedDiscreteNormalDistribution)
{
//Problem mit Algorithmus aus SYMTEP: bei ungünstigen Eingabeparametern gibt es auf manchen Fertigungsstufen keine Teile (0 Knoten)
//-> Es fehlt wohl Nebenbedingung, dass Anzahl an Teilen auf jeden Fertigungsstufe mindestens 1 sein darf
//-> Entsprechend wurde das hier angepasst
var nodeCount = Math.Max(1, Convert.ToInt64(Math.Round(
Math.Pow(inputParameters.ComplexityRatio / inputParameters.ReutilisationRatio, i - 1) *
inputParameters.EndProductCount)));
var nodesCurrentLevel = new Dictionary <long, Node>();
productStructure.NodesPerLevel.Add(nodesCurrentLevel);
var availableNodesOnThisLevel = new HashSet <long>();
availableNodes.Add(availableNodesOnThisLevel);
bool toPurchase, toBuild;
M_Unit unit = null;
M_ArticleType articleType;
if (i == 1)
{
toPurchase = false;
toBuild = true;
unit = units.PIECES;
articleType = articleTypes.PRODUCT;
}
else if (i == inputParameters.DepthOfAssembly)
{
toPurchase = true;
toBuild = false;
articleType = articleTypes.MATERIAL;
}
else
{
toPurchase = false;
toBuild = true;
unit = units.PIECES;
articleType = articleTypes.ASSEMBLY;
}
for (long j = 0; j < nodeCount; j++)
{
unit = GeneratePartsForCurrentLevel(inputParameters, unitCol, rng, i, sampleWorkPlanLength,
truncatedDiscreteNormalDistribution, availableNodesOnThisLevel, j, unit, articleType, toPurchase,
toBuild, nodesCurrentLevel);
}
return(nodeCount);
}
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 M_Unit GeneratePartsForCurrentLevel(ProductStructureInput inputParameters, M_Unit[] unitCol,
XRandom rng, int i, bool sampleWorkPlanLength, TruncatedDiscreteNormal truncatedDiscreteNormalDistribution,
HashSet <long> availableNodesOnThisLevel, long j, M_Unit unit, M_ArticleType articleType, bool toPurchase,
bool toBuild, Dictionary <long, Node> nodesCurrentLevel)
{
availableNodesOnThisLevel.Add(j);
if (i == inputParameters.DepthOfAssembly)
{
var pos = rng.Next(unitCol.Length);
unit = unitCol[pos];
}
var node = new Node
{
AssemblyLevel = i,
Article = new M_Article
{
Name = "Material " + i + "." + (j + 1),
ArticleTypeId = articleType.Id,
CreationDate = DateTime.Now,
DeliveryPeriod = 5,
UnitId = unit.Id,
Price = 10,
ToPurchase = toPurchase,
ToBuild = toBuild
}
};
nodesCurrentLevel[j] = node;
if (sampleWorkPlanLength && i != inputParameters.DepthOfAssembly)
{
node.WorkPlanLength = truncatedDiscreteNormalDistribution.Sample();
}
return(unit);
}
public void Test1()
{
var lintMax = Int32.MaxValue;
var longMax = Int64.MaxValue;
var doubleMax = Double.MaxValue;
System.Diagnostics.Debug.WriteLine(lintMax.ToString());
System.Diagnostics.Debug.WriteLine(longMax.ToString());
System.Diagnostics.Debug.WriteLine(doubleMax.ToString());
var faculty = new Faculty();
//var f1 = faculty.Calc(200);
//var f2 = faculty.Calc(20);
//var r = Math.Round(f2);
//var p1 = Math.Pow(100.537, 100);
//var p2 = Math.Pow(10.1, 20);
var x1 = Math.Round(Math.Pow(5.0 / 1.0, 19) * 50);
var x2 = Convert.ToInt64(x1);
var sum1 = Convert.ToInt64(0);
var sum2 = 0.0;
for (int i = 0; i < 20; i++)
{
var result = Convert.ToInt64(Math.Round(Math.Pow(5.0 / 1.0, i) * 50));
sum1 += result;
sum2 += result;
}
sum1 *= 5;
sum2 *= 5.0;
var x3 = Convert.ToInt64(Math.Round(sum2));
var x4 = Math.Round(5.4343454359);
var n1 = AlphabeticNumbering.GetAlphabeticNumbering(0);
var n2 = AlphabeticNumbering.GetAlphabeticNumbering(25);
var n3 = AlphabeticNumbering.GetAlphabeticNumbering(26);
var n4 = AlphabeticNumbering.GetAlphabeticNumbering(52);
var n5 = AlphabeticNumbering.GetAlphabeticNumbering(454);
var n6 = AlphabeticNumbering.GetAlphabeticNumbering(1);
var n7 = AlphabeticNumbering.GetAlphabeticNumbering(2);
var n8 = AlphabeticNumbering.GetAlphabeticNumbering(3);
var n11 = AlphabeticNumbering.GetNumericRepresentation(n1);
var n12 = AlphabeticNumbering.GetNumericRepresentation(n2);
var n13 = AlphabeticNumbering.GetNumericRepresentation(n3);
var n14 = AlphabeticNumbering.GetNumericRepresentation(n4);
var n15 = AlphabeticNumbering.GetNumericRepresentation(n5);
var n16 = AlphabeticNumbering.GetNumericRepresentation(n6);
var n17 = AlphabeticNumbering.GetNumericRepresentation(n7);
var n18 = AlphabeticNumbering.GetNumericRepresentation(n8);
var list1 = new List <TruncatedDiscreteNormal>();
var truncatedDiscreteNormalDistribution =
new TruncatedDiscreteNormal(9, 11, Normal.WithMeanVariance(5.0, 2.0));
list1.Add(truncatedDiscreteNormalDistribution);
list1.Add(truncatedDiscreteNormalDistribution);
list1.Add(truncatedDiscreteNormalDistribution);
list1.Add(truncatedDiscreteNormalDistribution);
var x5 = list1[1].Sample();
Assert.True(true);
}
