private void InitCollections(List <TimelinessTimePeriod1> timelinessTimePeriods)
{
if (timelinessTimePeriods != null)
{
if (timelinessTimePeriods.Count > 0)
{
//make the optimization array of uniqueopcomp, feasibleopcomp
MakeOrderedTOCs(timelinessTimePeriods);
if (this.TOpComps == null || this.WOpComps == null)
{
this.ErrorMessage = Errors.GetMessage("RESOURCESTOCKS_NOPOWERORNONPOWERINPUTS");
}
}
else
{
this.ErrorMessage = Errors.GetMessage("RESOURCESTOCKS_NOPOWERORNONPOWERINPUTS");
}
}
else
{
this.ErrorMessage = Errors.GetMessage("RESOURCESTOCKS_NOPOWERORNONPOWERINPUTS");
}
}
public void RunOptimization(
List <TimelinessTimePeriod1> timelinessTimePeriods)
{
InitCollections(timelinessTimePeriods);
if (this.ErrorMessage == string.Empty)
{
try
{
//initial collections of TP.OpComps (with each OpComp holding feasible OpComp.TimelinessOpComps)
random = new Random(0);
// total cost index for problem definition
double[][] problemData = MakeProblemData(timelinessTimePeriods);
//state represents a solution int array where the first index is the feasibleOpComp
//and the second index is the uniqueOpComp
//(uniqueOpComp - feasibleOpComp combos that are not possible must have a zero total cost)
int[] state = RandomState(problemData);
double energy = Energy(state, problemData);
//best state is the least cost vector of 1 unique opcomp and 1 feasible opcomp
int[] bestState = state;
double bestEnergy = energy;
int[] adjState;
double adjEnergy;
int iteration = 0;
//the higher these are, the longer the algorithm runs
//the longer the algo runs the more optimal the solution
int maxIteration = 1000000;
double currTemp = 10000.0;
// cooling rate
double alpha = 0.995;
while (iteration < maxIteration && currTemp > 0.0001)
{
//adjacent state doesn't allow nonfeasible selections (w,t = 0)
adjState = AdjacentState(state, problemData);
adjEnergy = Energy(adjState, problemData);
if (adjEnergy < bestEnergy)
{
bestState = adjState;
bestEnergy = adjEnergy;
}
// [0, 1.0)
double p = random.NextDouble();
if (AcceptanceProb(energy, adjEnergy, currTemp) > p)
{
state = adjState;
energy = adjEnergy;
}
// cool down; annealing schedule
currTemp = currTemp * alpha;
++iteration;
}
//temperature has cooled to (almost) zero at final iteration
MakeBestMachineryCombos(timelinessTimePeriods, bestState, problemData);
}
catch (Exception ex)
{
this.ErrorMessage = string.Concat(Errors.GetMessage("RESOURCESTOCKS_NOSIMULATEDANNEALING"),
ex.ToString());
}
}
}
public void RunOptimization(
TimelinessOpComp1 initialTimelinessOCStock, List <Machinery1Input> machineryInputs)
{
BestMachineryCombos = new Dictionary <Machinery1Input, Machinery1Input>();
try
{
random = new Random(0);
this.ErrorMessage = string.Empty;
this.PowerInputs = machineryInputs.FindAll(m => m.FuelCost > 0);
this.NonPowerInputs = machineryInputs.FindAll(m => m.FuelCost <= 0);
if (this.PowerInputs.Count > 0 && this.NonPowerInputs.Count > 0)
{
// total cost index for problem definition
double[][] problemData = MakeProblemData(initialTimelinessOCStock);
//state represents a solution int array where the index is the nonpowerimplement and the value is the powerimplement
//(powerinput - implement combos that are not possible must have a zero total cost)
int[] state = RandomState(problemData);
double energy = Energy(state, problemData);
//best state is the least cost vector of 1 power input and 1 non power inputs
int[] bestState = state;
double bestEnergy = energy;
int[] adjState;
double adjEnergy;
int iteration = 0;
//the higher these are, the longer the algorithm runs
//the longer the algo runs the more optimal the solution
int maxIteration = 1000000;
double currTemp = 10000.0;
// cooling rate
double alpha = 0.995;
while (iteration < maxIteration && currTemp > 0.0001)
{
adjState = AdjacentState(state, problemData);
adjEnergy = Energy(adjState, problemData);
if (adjEnergy < bestEnergy)
{
bestState = adjState;
bestEnergy = adjEnergy;
}
// [0, 1.0)
double p = random.NextDouble();
if (AcceptanceProb(energy, adjEnergy, currTemp) > p)
{
state = adjState;
energy = adjEnergy;
}
// cool down; annealing schedule
currTemp = currTemp * alpha;
++iteration;
}
//temperature has cooled to (almost) zero at final iteration
MakeBestMachineryCombos(bestState, problemData);
}
else
{
this.ErrorMessage = Errors.GetMessage("RESOURCESTOCKS_NOPOWERORNONPOWERINPUTS");
}
}
catch (Exception ex)
{
this.ErrorMessage = string.Concat(Errors.GetMessage("RESOURCESTOCKS_NOSIMULATEDANNEALING"),
ex.ToString());
}
}
public async Task <bool> SetObservedMathResult(string mathType, string distType,
string mathSubType, double mostLikely, double[] obsTs)
{
bool bHasSet = false;
//this is observed data rather than randomly sampled data
StringBuilder sb = new StringBuilder();
if (obsTs.Count() > 0)
{
//xy array of 10 points on cdf
//var array = qTs.ToArray();
Array.Sort(obsTs);
int i00 = 0;
int i10 = (int)(obsTs.Length * .10);
int i20 = (int)(obsTs.Length * .20);
int i30 = (int)(obsTs.Length * .30);
int i40 = (int)(obsTs.Length * .40);
int i50 = (int)(obsTs.Length * .50);
int i60 = (int)(obsTs.Length * .60);
int i70 = (int)(obsTs.Length * .70);
int i80 = (int)(obsTs.Length * .80);
int i90 = (int)(obsTs.Length * .90);
int i100 = (int)(obsTs.Length - 1);
sb.AppendLine(Errors.GetMessage("STATS_DESC5"));
sb.AppendLine(string.Concat(
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i00]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i10]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i20]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i30]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i40]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i50]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i60]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i70]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i80]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i90]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(obsTs, obsTs[i100]).ToString("N2", CultureInfo.InvariantCulture)));
sb.AppendLine(string.Concat(
obsTs[i00].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
obsTs[i10].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
obsTs[i20].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
obsTs[i30].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
obsTs[i40].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
obsTs[i50].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
obsTs[i60].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
obsTs[i70].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
obsTs[i80].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
obsTs[i90].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
obsTs[i100].ToString("F4", CultureInfo.InvariantCulture)));
sb.AppendLine(Errors.GetMessage("STATS_DESC3"));
sb.AppendLine(Errors.GetMessage("STATS_DESC2"));
//qT is the result of math and stored in sixth col
var stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(obsTs);
sb.AppendLine(string.Concat(stats.Count.ToString(), Constants.CSV_DELIMITER, obsTs.Sum().ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
stats.Mean.ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.Median(obsTs).ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
stats.StandardDeviation.ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER, stats.Variance.ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
stats.Minimum.ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER, stats.Maximum.ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER));
string sLowerCI = string.Concat(Errors.GetMessage("LOWER"), this.CILevel.ToString(), Errors.GetMessage("CI_PCT"));
string sUpperCI = string.Concat(Errors.GetMessage("UPPER"), this.CILevel.ToString(), Errors.GetMessage("CI_PCT"));
this.IndicatorQT.QTM = stats.Mean;
this.IndicatorQT.QTL = stats.Mean - CalculatorHelpers.GetConfidenceInterval(this.CILevel, stats.Count, stats.StandardDeviation);
this.IndicatorQT.QTU = stats.Mean + CalculatorHelpers.GetConfidenceInterval(this.CILevel, stats.Count, stats.StandardDeviation);
this.IndicatorQT.QTLUnit = sLowerCI;
this.IndicatorQT.QTUUnit = sUpperCI;
}
else
{
sb.AppendLine(Errors.MakeStandardErrorMsg("DATAURL_BADDATA"));
}
if (this.IndicatorQT.MathResult.ToLower().StartsWith("http"))
{
bool bHasSaved = await CalculatorHelpers.SaveTextInURI(
Params.ExtensionDocToCalcURI, sb.ToString(), this.IndicatorQT.MathResult);
if (!string.IsNullOrEmpty(Params.ExtensionDocToCalcURI.ErrorMessage))
{
this.IndicatorQT.MathResult += Params.ExtensionDocToCalcURI.ErrorMessage;
//done with errormsg
Params.ExtensionDocToCalcURI.ErrorMessage = string.Empty;
}
}
else
{
this.IndicatorQT.MathResult = sb.ToString();
}
bHasSet = true;
return(bHasSet);
}
public async Task <bool> SetRange(double mostLikelyEstimate, double lowEstimate, double highEstimate,
double[] data, List <double> cdf = null)
{
bool bHasSet = false;
StringBuilder sb = new StringBuilder();
if (data == null)
{
sb.AppendLine("This indicator does not have the properties needed for descriptive statistics.");
}
if (data.Count() == 0)
{
sb.AppendLine("This indicator does not have the properties needed for descriptive statistics.");
}
//default has no range of values
double lowRange = lowEstimate;
double highRange = highEstimate;
double mostLikely = mostLikelyEstimate;
string mostLikelyUnit = string.Empty;
string lowUnit = string.Empty;
string highUnit = string.Empty;
bool bNeedsCDF = false;
var stats = new MathNet.Numerics.Statistics.DescriptiveStatistics(data);
string sLowerCI = string.Concat(Errors.GetMessage("LOWER"), this.CILevel.ToString(), Errors.GetMessage("CI_PCT"));
string sUpperCI = string.Concat(Errors.GetMessage("UPPER"), this.CILevel.ToString(), Errors.GetMessage("CI_PCT"));
//some data has all zeros and does not use sampled data
if (stats.Mean != 0)
{
mostLikely = stats.Mean;
mostLikelyUnit = "mean";
lowRange = mostLikely - CalculatorHelpers.GetConfidenceInterval(this.CILevel, stats.Count, stats.StandardDeviation);
lowUnit = sLowerCI;
highRange = mostLikely + CalculatorHelpers.GetConfidenceInterval(this.CILevel, stats.Count, stats.StandardDeviation);
highUnit = sUpperCI;
bNeedsCDF = true;
}
if (bNeedsCDF)
{
//csv strings use f4 not n4
sb.AppendLine(Errors.GetMessage("STATS_DESC1"));
sb.AppendLine(Errors.GetMessage("STATS_DESC2"));
sb.AppendLine(string.Concat(stats.Count.ToString(), ", ", data.Sum().ToString("F4", CultureInfo.InvariantCulture), ", ",
stats.Mean.ToString("F4", CultureInfo.InvariantCulture), ", ",
MathNet.Numerics.Statistics.Statistics.Median(data).ToString("F4", CultureInfo.InvariantCulture), ", ",
stats.StandardDeviation.ToString("F4", CultureInfo.InvariantCulture), ", ", stats.Variance.ToString("F4", CultureInfo.InvariantCulture), ", ",
stats.Minimum.ToString("F4", CultureInfo.InvariantCulture), ", ", stats.Maximum.ToString("F4", CultureInfo.InvariantCulture), ", "));
//xy array of 10 points on cdf
var array = data.ToArray();
Array.Sort(array);
int i00 = 0;
int i10 = (int)(array.Length * .10);
int i20 = (int)(array.Length * .20);
int i30 = (int)(array.Length * .30);
int i40 = (int)(array.Length * .40);
int i50 = (int)(array.Length * .50);
int i60 = (int)(array.Length * .60);
int i70 = (int)(array.Length * .70);
int i80 = (int)(array.Length * .80);
int i90 = (int)(array.Length * .90);
int i100 = (int)(array.Length - 1);
sb.AppendLine(Errors.GetMessage("STATS_DESC4"));
sb.AppendLine(string.Concat(
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i00]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i10]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i20]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i30]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i40]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i50]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i60]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i70]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i80]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i90]).ToString("N2", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
MathNet.Numerics.Statistics.Statistics.EmpiricalCDF(data, array[i100]).ToString("N2", CultureInfo.InvariantCulture)));
sb.AppendLine(string.Concat(
array[i00].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
array[i10].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
array[i20].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
array[i30].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
array[i40].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
array[i50].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
array[i60].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
array[i70].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
array[i80].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
array[i90].ToString("F4", CultureInfo.InvariantCulture), Constants.CSV_DELIMITER,
array[i100].ToString("F4", CultureInfo.InvariantCulture)));
if (cdf == null)
{
cdf = new List <double>();
}
cdf.Add(array[i00]);
cdf.Add(array[i10]);
cdf.Add(array[i20]);
cdf.Add(array[i30]);
cdf.Add(array[i40]);
cdf.Add(array[i50]);
cdf.Add(array[i60]);
cdf.Add(array[i70]);
cdf.Add(array[i80]);
cdf.Add(array[i90]);
cdf.Add(array[i100]);
}
else
{
sb.AppendLine(Errors.GetMessage("STATS_DESC6"));
}
//doubles need restricted digits
mostLikely = Math.Round(mostLikely, 4);
lowRange = Math.Round(lowRange, 4);
highRange = Math.Round(highRange, 4);
string sD1Unit = "low";
string sD2Unit = "high";
this.IndicatorQT.QTD1 = lowEstimate;
if (this.IndicatorQT.QTD1Unit == string.Empty || this.IndicatorQT.QTD1Unit == Constants.NONE)
{
this.IndicatorQT.QTD1Unit = sD1Unit;
}
this.IndicatorQT.QTD2 = highEstimate;
if (this.IndicatorQT.QTD2Unit == string.Empty || this.IndicatorQT.QTD2Unit == Constants.NONE)
{
this.IndicatorQT.QTD2Unit = sD2Unit;
}
//computed results
this.IndicatorQT.QTM = mostLikely;
if (this.IndicatorQT.QTMUnit == string.Empty || this.IndicatorQT.QTMUnit == Constants.NONE)
{
this.IndicatorQT.QTMUnit = mostLikelyUnit;
}
this.IndicatorQT.QTL = lowRange;
if (this.IndicatorQT.QTLUnit == string.Empty || this.IndicatorQT.QTLUnit == Constants.NONE)
{
this.IndicatorQT.QTLUnit = lowUnit;
}
this.IndicatorQT.QTU = highRange;
if (this.IndicatorQT.QTUUnit == string.Empty || this.IndicatorQT.QTUUnit == Constants.NONE)
{
this.IndicatorQT.QTUUnit = highUnit;
}
if (this.IndicatorQT.MathResult.ToLower().StartsWith("http"))
{
bool bHasSaved = await CalculatorHelpers.SaveTextInURI(
Params.ExtensionDocToCalcURI, sb.ToString(), this.IndicatorQT.MathResult);
if (!string.IsNullOrEmpty(Params.ExtensionDocToCalcURI.ErrorMessage))
{
this.IndicatorQT.MathResult += Params.ExtensionDocToCalcURI.ErrorMessage;
//done with errormsg
Params.ExtensionDocToCalcURI.ErrorMessage = string.Empty;
}
}
else
{
this.IndicatorQT.MathResult = sb.ToString();
}
bHasSet = true;
return(bHasSet);
}
public void RunOptimization(List <TimelinessTimePeriod1> timelinessTimePeriods)
{
//prepare the initial, ordered collections of opcomps
InitCollections(timelinessTimePeriods);
if (this.ErrorMessage == string.Empty)
{
try
{
//state holds feasible opcomps for each uniqueopcomp
//[1][4][20][15][10][7] = means the first unique opcomp
//uses the feasible opcomp with an index of 1
//= this.InitialOpComps[0].FeasibleOpComps[1]
int u = 0;
int f = 0;
//init this.CurrentStateOpComps total costs properties
double totalCurrentStateCost = SetTotalCostsForCurrentState();
//init this.BestOpComps total costs properties
SetBestState();
double totalBestStateCost = SetTotalCostsForBestState();
//all feasible combinations are looped through
//each time a new feasible is checked, a new timeliness cost
//is added to it when its date must be changed
//best state is least cost for the current full array
u = 0;
bool bHasNewFixedUniqueOpComp = false;
foreach (var uniqueOpComp in this.InitialOpComps)
{
if (uniqueOpComp.TimelinessOpComps != null)
{
f = 0;
foreach (var feasibleOpComp in uniqueOpComp.TimelinessOpComps)
{
//add feasibleopcomp to currentopcomps
totalCurrentStateCost = SetTotalCostsForCurrentState(u, f);
totalBestStateCost = SetTotalCostsForBestState();
if (totalCurrentStateCost < totalBestStateCost && totalCurrentStateCost > 0)
{
//new least cost combination
//copy this.CurrentStateOpComps to this.BestOpComps
SetBestState();
bHasNewFixedUniqueOpComp = true;
}
if (bHasNewFixedUniqueOpComp)
{
//every time the best state is changed
//recheck all combos, keeping the new feasibleOpComp fixed
ChangeBestStateUsingFixedUniqueOpComp(u);
}
bHasNewFixedUniqueOpComp = false;
f++;
}
}
u++;
}
//set the solution collection
MakeBestMachineryCombos(timelinessTimePeriods);
}
catch (Exception ex)
{
this.ErrorMessage = string.Concat(Errors.GetMessage("RESOURCESTOCKS_NOMACH1SELECTSCHEDULE"),
ex.ToString());
}
}
}
