/**
* This func generate all possible pattern to be fit in one stock len (or remain stock len)
* from the @param{curOrderIndex} to end of orderSets
*/
public static List <List <BarSet> > calPossibleCutsFor1Stock(int curOrderIndex, List <BarSet> orderSets, double stockLen)
{
List <List <BarSet> > possiblePatterns = new List <List <BarSet> >();
bool canCut = checkCanCut(orderSets, stockLen);
if (!canCut)
{
// Create array to let father func put bar in
possiblePatterns.Add(new List <BarSet>());
return(possiblePatterns);
}
if (curOrderIndex == orderSets.Count)
{
return(possiblePatterns);
}
BarSet curOrderSet = orderSets[curOrderIndex];
int maxBarNum = minInt((int)(stockLen / curOrderSet.len), curOrderSet.num);
for (int nBar = 0; nBar <= maxBarNum; nBar++)
{
// Clone current order except the one at current index
List <BarSet> remainOrderSets = new List <BarSet>();
remainOrderSets.AddRange(orderSets);
remainOrderSets[curOrderIndex] = new BarSet(curOrderSet.len, curOrderSet.num - nBar);
List <List <BarSet> > subPatterns = calPossibleCutsFor1Stock(
curOrderIndex + 1, remainOrderSets, stockLen - curOrderSet.len * nBar);
int barNum = nBar;
foreach (List <BarSet> pttrn in subPatterns)
{
pttrn.Insert(0, new BarSet(curOrderSet.len, barNum));
}
possiblePatterns.AddRange(subPatterns);
}
return(possiblePatterns);
}
/**
* Solve most of pattern by linear programming
* @returns 1st value is optimized pattern, 2nd value is the remain orders that haven't been solved
*/
public static Pair <Dictionary <List <BarSet>, int>, List <BarSet> > solveByLinearProgramming(List <BarSet> orderSets, double stockLen)
{
int nOrder = orderSets.Count;
// Convert to Lp solve-friendly format: [arrayLen, x1, x2, 3...]
double[] odLenVec = new double[nOrder + 1]; // order length vector
double[] odNumVec = new double[nOrder + 1]; // order number vector
odLenVec[0] = nOrder;
odNumVec[0] = nOrder;
for (int i = 0; i < nOrder; i++)
{
BarSet barSet = orderSets[i];
odLenVec[i + 1] = barSet.len;
odNumVec[i + 1] = barSet.num;
}
double[][] patternMat = genPatternMatrix(odLenVec, odNumVec, stockLen);
double[] minPatternNums = new double[nOrder];
// Lpsolve.Init(".");
int iter;
for (iter = 0; iter < MAX_ITER; iter++)
{
;
// Solve Linear Programming problem
double[][] lpRst = calLP(patternMat, odNumVec);
int solFlag = (int)lpRst[0][0];
if (solFlag != (int)Lpsolve.lpsolve_return.OPTIMAL)
{
Console.WriteLine("Can't solve anymore");
break;
}
minPatternNums = lpRst[1];
double[] dualCostVector = lpRst[2];
// Solve Knapsack problem
double[][] ksRst = calKnapsack(dualCostVector, odLenVec, stockLen);
double reducedCost = ksRst[0][0];
double[] newPattern = ksRst[1];
// TODO: use native optimized variable instead
if (reducedCost <= 1.000000000001)
{ // epsilon threshold due to double value error
Console.WriteLine("Optimized");
break;
}
// Cal leaving column
int[] lcRst = calLeavingColumn(patternMat, newPattern, minPatternNums);
int lcSolFlag = lcRst[0];
int leavingColIndex = lcRst[1];
// Console.WriteLine("Leaving column index: {0}", leavingColIndex);
if (lcSolFlag != (int)Lpsolve.lpsolve_return.OPTIMAL)
{
Console.WriteLine("Can't solve anymore");
break;
}
// Save new pattern
for (int r = 0; r < patternMat.Length; r++)
{
patternMat[r][leavingColIndex] = newPattern[r];
}
}
if (iter == MAX_ITER)
{
Console.WriteLine("Maximum of iter reached! Solution is not quite optimized");
}
// ------------ Round up to keep integer part of result ----------------
double[] remainOdNumVec = new double[odNumVec.Length];
odNumVec.CopyTo(remainOdNumVec, 0);
for (int r = 0; r < patternMat.Length; r++)
{
double[] row = patternMat[r];
for (int c = 1; c < row.Length; c++)
{
remainOdNumVec[r + 1] -= row[c] * Math.Floor(minPatternNums[c]);
}
}
// Console.WriteLine("Leftovers: {0}", string.Join(", ", remainOdNumVec));
// Optimized for the rest of pattern by BruteForce
List <BarSet> remainOrderSets = new List <BarSet>();
for (int r = 1; r < remainOdNumVec.Length; r++)
{
if (remainOdNumVec[r] > 0)
{
remainOrderSets.Add(new BarSet(odLenVec[r], (int)remainOdNumVec[r]));
}
}
// Prepare result
Dictionary <List <BarSet>, int> rstMap = new Dictionary <List <BarSet>, int>();
for (int c = 1; c < nOrder + 1; c++)
{ // column
if ((int)minPatternNums[c] == 0)
{ // if number of this pattern is zero, skip
continue;
}
List <BarSet> pattern = new List <BarSet>();
for (int r = 0; r < nOrder; r++)
{ // row
if (patternMat[r][c] > 0d)
{
pattern.Add(new BarSet(odLenVec[r + 1], (int)patternMat[r][c]));
}
}
rstMap.Add(pattern, (int)minPatternNums[c]);
}
return(new Pair <Dictionary <List <BarSet>, int>, List <BarSet> >(rstMap, remainOrderSets));
}