// formulate and generate the solution, if necessary
private void solve() {
if (solved) return;
// min obj.x s.t. a.x=b, lb <= x <= ub
// x is energy use per block for size hours, b is slack var per block.
// Block is a shift, or portion of shift with constant price.
// For multi-hour blocks, energy use is evenly distributed across hours
// after solution.
Date start = new Date();
int shifts = needs.length;
// Create blocks that break on both shift boundaries and tariff price
// boundaries.
ShiftBlock[] blocks = makeBlocks(shifts);
int columns = blocks.length;
int blockIndex = -1;
double[] obj = new double[columns + shifts];
double[][] a = new double[shifts][columns + shifts];
double[] b = new double[shifts];
double[] lb = new double[columns + shifts];
double[] ub = new double[columns + shifts];
int column = 0;
double cumulativeMin = 0.0; // this is the primary constraint
// construct the problem
for (int i = 0; i < shifts; i++) {
// one iteration per shift
// double kwh =
// needs[i].getEnergyNeeded() + needs[i].getMaxSurplus();
// for (int j = 0; j < needs[i].getDuration(); j++) {
while ((blockIndex < blocks.length - 1)
&& (blocks[blockIndex + 1].getShiftEnergy() == needs[i])) {
blockIndex += 1;
// one iteration per block within a shift
// fill in objective function
obj[column] = blocks[blockIndex].getCost();
lb[column] = 0.0;
ub[column] =
(needs[i].getEnergyNeeded() + needs[i].getMaxSurplus())
* (double) blocks[blockIndex].getDuration()
/ needs[i].getDuration();
column += 1;
// construct cumulative usage constraints
// a[i][column] = -1.0;
// time = time.plus(TimeService.HOUR);
}
// fill a row up to column
for (int j = 0; j < column; j++) {
a[i][j] = -1.0;
}
// b vector - one entry per constraint
double need = needs[i].getEnergyNeeded();
if (needs[i].getMaxSurplus() < 0.0) need += needs[i].getMaxSurplus();
cumulativeMin += need;
b[i] = -cumulativeMin;
// fill in slack values, one per constraint
obj[columns + i] = 0.0;
a[i][columns + i] = 1.0;
lb[columns + i] = 0.0;
// upper bound is max possible energy for shift
ub[columns + i] = (needs[i].getEnergyNeeded() + needs[i].getMaxSurplus());
}
// run the optimization
LPOptimizationRequest or = new LPOptimizationRequest();
log.debug("Obj: " + Arrays.toString(obj));
or.setC(obj);
log.debug("a:");
for (int i = 0; i < a.length; i++) log.debug(Arrays.toString(a[i]));
or.setA(a);
log.debug("b: " + Arrays.toString(b));
or.setB(b);
or.setLb(lb);
log.debug("ub: " + Arrays.toString(ub));
or.setUb(ub);
or.setTolerance(1.0e-2);
LPPrimalDualMethod opt = new LPPrimalDualMethod();
opt.setLPOptimizationRequest(or);
try {
int returnCode = opt.optimize();
if (returnCode != OptimizationResponse.SUCCESS) {
log.error(getName() + "bad optimization return code " + returnCode);
}
double[] sol = opt.getOptimizationResponse().getSolution();
Date end = new Date();
log.info("Solution time: " + (end.getTime() - start.getTime()));
log.debug("Solution = " + Arrays.toString(sol));
recordSolution(sol, blocks);
} catch (Exception e) {
log.error(e.toString());
}
// we call it solved whether or not the solution was successful
solved = true;
}
