public ORECalculate(bool usefullORE)
{
useFullORE = usefullORE;
oreDictionary = new OREDictionary();
solver = new SimplexSolver();
foreach (var kv in oreDictionary.ORE_Dictionary)
{
if (usefullORE || kv.Value.isMain)
{
int id = 0;
solver.AddVariable(kv.Key, out id);
solver.SetBounds(id, 0, Rational.PositiveInfinity);
kv.Value.id = id;
}
}
solver.AddRow("a", out a);
solver.AddRow("b", out b);
solver.AddRow("c", out c);
solver.AddRow("d", out d);
solver.AddRow("e", out e);
solver.AddRow("f", out f);
solver.AddRow("g", out g);
solver.AddRow("h", out h);
solver.AddRow("cost", out cost);
solver.AddGoal(cost, 1, true);
}
public void Generate()
{
// TODO: Int32 is overloading
SimplexSolver solver = new SimplexSolver();
mealPortions = new List <int>();
int Calories, cost, Proteins, Carbohydrates, Fats;
solver.AddRow("calories", out Calories);
solver.SetIntegrality(Calories, true);
solver.AddRow("proteins", out Proteins);
solver.SetIntegrality(Calories, true);
solver.AddRow("carbohydrates", out Carbohydrates);
solver.SetIntegrality(Calories, true);
solver.AddRow("fats", out Fats);
solver.SetIntegrality(Fats, true);
solver.AddRow("cost", out cost);
Random rand = new Random();
foreach (Meal m in this.meals)
{
int portions = new int();
solver.AddVariable(m.Name, out portions);
solver.SetBounds(portions, 0, 5);
solver.SetIntegrality(portions, true);
solver.SetCoefficient(Calories, portions, (int)m.Calories_Per_Portions);
int prots = ((int)(m.Proteins * m.Portion_Size / 100) - this.proteins) * (-1);
solver.SetCoefficient(Proteins, portions, prots);
int carbs = ((int)(m.Carbohydrates * m.Portion_Size / 100) - this.carbohydrates) * (-1);
solver.SetCoefficient(Carbohydrates, portions, carbs);
int fa = ((int)(m.Fats * m.Portion_Size / 100) - this.fats) * (-1);
solver.SetCoefficient(Fats, portions, fa);
solver.SetCoefficient(cost, portions, rand.Next(0, 100));
mealPortions.Add(portions);
}
solver.SetBounds(Calories, this.calories, this.calories);
//solver.SetBounds(Proteins, 0, int.MaxValue);
//solver.SetBounds(Carbohydrates, 0, int.MaxValue);
//solver.SetBounds(Fats, 0, int.MaxValue);
solver.AddGoal(cost, 1, true);
solver.Solve(new SimplexSolverParams());
for (int i = 0; i < this.mealPortions.Count; i++)
{
this.mealPortions[i] = (int)solver.GetValue(this.mealPortions[i]).ToDouble();
}
}
static void Main(string[] args)
{
SimplexSolver solver = new SimplexSolver();
int savid, vzvid;
solver.AddVariable("Saudi", out savid);
solver.AddVariable("Ven", out vzvid);
solver.SetBounds(savid, 0, 9000);
solver.SetBounds(vzvid, 0, 6000);
int c1rid, c2rid, c3rid, goalrid;
solver.AddRow("c1", out c1rid);
solver.AddRow("c2", out c2rid);
solver.AddRow("c3", out c3rid);
solver.AddRow("goal", out goalrid);
// add coefficients to constraint rows
solver.SetCoefficient(c1rid, savid, 0.3);
solver.SetCoefficient(c1rid, vzvid, 0.4);
solver.SetBounds(c1rid, 2000, Rational.PositiveInfinity);
solver.SetCoefficient(c2rid, savid, 0.4);
solver.SetCoefficient(c2rid, vzvid, 0.2);
solver.SetBounds(c2rid, 1500, Rational.PositiveInfinity);
solver.SetCoefficient(c3rid, savid, 0.2);
solver.SetCoefficient(c3rid, vzvid, 0.3);
solver.SetBounds(c3rid, 500, Rational.PositiveInfinity);
// add objective (goal) to model and specify minimization (==true)
solver.SetCoefficient(goalrid, savid, 20);
solver.SetCoefficient(goalrid, vzvid, 15);
solver.AddGoal(goalrid, 1, true);
solver.Solve(new SimplexSolverParams());
Console.WriteLine("SA {0}, VZ {1}, C1 {2}, C2 {3}, C3 {4}, Goal {5}",
solver.GetValue(savid).ToDouble(), solver.GetValue(vzvid).ToDouble(),
solver.GetValue(c1rid).ToDouble(), solver.GetValue(c2rid).ToDouble(),
solver.GetValue(c3rid).ToDouble(), solver.GetValue(goalrid).ToDouble());
Console.ReadLine();
}
public void Get()
{
SimplexSolver solver = new SimplexSolver();
double[] estimatedProfitOfProjectX = new double[] { 1, 1.8, 1.6, 0.8, 1.4 };
double[] capitalRequiredForProjectX = new double[] { 6, 12, 10, 4, 8 };
double availableCapital = 20;
int[] chooseProjectX = new int[5];
int profit;
solver.AddRow("profit", out profit);
solver.AddGoal(profit, 0, false);
int expenditure;
solver.AddRow("expenditure", out expenditure);
solver.SetBounds(expenditure, 0, availableCapital);
for (int i = 0; i < 5; i++)
{
solver.AddVariable(string.Format("project{0}", i), out chooseProjectX[i]);
solver.SetBounds(chooseProjectX[i], 0, 1);
solver.SetIntegrality(chooseProjectX[i], true);
solver.SetCoefficient(profit, chooseProjectX[i], estimatedProfitOfProjectX[i]);
solver.SetCoefficient(expenditure, chooseProjectX[i], capitalRequiredForProjectX[i]);
}
SimplexSolverParams param = new SimplexSolverParams();
param.MixedIntegerGenerateCuts = true;
solver.Solve(param);
Console.WriteLine(solver.MipResult);
for (int i = 0; i < 5; i++)
{
Console.WriteLine("Project {0} is {1} selected.", i, solver.GetValue(chooseProjectX[i]) == 1 ? "" : "not ");
}
Console.WriteLine("The estimated total profit is: ${0} million.", (double)solver.GetValue(profit).ToDouble());
Console.WriteLine("The total expenditure is: ${0} million.", solver.GetValue(expenditure).ToDouble());
}
/// <summary>
/// Funkcja z pomocą której następuje uruchomienie solvera i wykonanie obliczeń optymalizujących cenę
/// </summary>
/// <param name="alloys">Lista stopów biorących udział w procesie wytapiania</param>
/// <param name="smelt">Wytop który chcemy uzyskać</param>
private async void calculate_price(List <Alloy> alloys, List <Smelt> smelt)
{
int[] tabVar = new int[alloys.Count]; //il stopow w wytopie
int[] tabEq = new int[31]; //il pierwiastkow + war + f. celu
try
{
//warunek nieujemnosci zmiennych
for (int i = 0; i < tabVar.Count(); i++)
{
solver.AddVariable(alloys.ElementAt(i).name, out tabVar[i]);
solver.SetBounds(tabVar[i], 0, Rational.PositiveInfinity);
}
//E(Aij*Xij>bi) i=1,2,..,n
for (int i = 0; i < tabEq.Count() - 2; i++)
{
solver.AddRow("r" + i.ToString(), out tabEq[i]);
for (int j = 0; j < selectedAlloys.Count; j++)
{
//zmienna, kazda zawartosc pierwiastka x jego wsp. parowania
solver.SetCoefficient(tabEq[i], tabVar[j], selectedAlloys.ElementAt(j).tabOfElements[i] * (1 - smelt.ElementAt(0).evoporation[i] / 100));
}
solver.SetBounds(tabEq[i], smelt.ElementAt(0).min_Norm[i] * smelt.ElementAt(0).Weight, smelt.ElementAt(0).max_Norm[i] * smelt.ElementAt(0).Weight);
}
solver.AddRow("weightCond", out tabEq[29]); //war masowy
solver.AddRow("cost", out tabEq[30]); //f celu
for (int i = 0; i < alloys.Count; i++)
{
solver.SetCoefficient(tabEq[29], tabVar[i], 1);
if (alloys.ElementAt(i).Price == 0)
{
solver.SetCoefficient(tabEq[30], tabVar[i], 1);
}
else
{
solver.SetCoefficient(tabEq[30], tabVar[i], alloys.ElementAt(i).Price);
}
}
solver.SetBounds(tabEq[29], smelt.ElementAt(0).Weight, smelt.ElementAt(0).Weight);
solver.AddGoal(tabEq[30], 1, true);
//po obliczeniach pokaż ekran z wynikami
await Navigation.PushAsync(new ProcessResults(solver, alloys, smelt, SwitchPriceWeight.IsToggled));
}
catch (Exception ex)
{
await DisplayAlert("Error", "Cos w solverze nie tak:\n" + ex.ToString(), "OK");
}
}
/// <summary>
/// Solves a sample (randomly generated?) cutting stock problem.
/// Given a bolt of cloth of fixed width, and demand for cut strips of the cloth, determine the min "loss" cut patterns to use and how many
/// of them.
/// Loss is defined as the scrap thrown away.
/// It is acceptable to have extra cut widths made. They do not contribute to the cost. (this may be unrealistic in the real world)
/// Solver runs by 1st creating an enumeration of possible cut patterns using a CspSolver, then choosing between the patterns and selecting a qty of the patterns such that the
/// amount of scrap is minimized and all demand is met using the SimplexSolver MIP code.
///
/// In an industrial case, there would likely be more constraints in the generation of the cut patterns. There can be other restrictions such as "these can't be done together"
/// or "these MUST be done together (matching pattern or color?)". This can easily be added to the CspSolver model.
/// Also, there are likely other characteristics of the cuts or the master problem which would need adaptations.
///
/// Further, the limit on the columns generated is implemented in a very arbitrary order. It is more likely that some ordering of the
/// value of the columns is needed. In most industrial occurances, the dual variables from the LP relaxation would likely be used to
/// guide the generation of columns in an interative fasion rather than a one-time shot at the beginning.
///
/// YMMV
/// </summary>
public static void ShortCuttingStock()
{
Console.WriteLine("*** Short Cutting Stock ***");
int NumItems = 5; // how many cut widths to generate
int ClothWidth = 40; // width of the stock to cut the widths from
double efficiency = 0.7; // reject cut patterns less than this % used of the clothwidth
int maxPatterns = 100; // max # of patterns to generate
bool verbose = true; // set this to true if you want some (useful?) output
bool saveMpsFile = false; // set this to true if you want it to save an mps file in c:\\temp\\cutstock.mps
int itemSizeMin = 5; // minimum size for random generation of cut
int itemSizeMax = 10; // maximum size for random generation of cut
int itemDemandMin = 10; // minimum random demand for each cut
int itemDemandMax = 40; // maximum random demand for each cut
int seed = 12447; // use System.DateTime.Now.Millisecond; instead if you want a random problem.
if (verbose)
{
System.Console.WriteLine(String.Format("Random seed={0}\tmaxWidth={1}", seed, ClothWidth));
}
Random rand = new Random(seed);
int[] cuts = new int[NumItems];
int[] demand = new int[NumItems];
// item weights and demands
for (int cnt = 0; cnt < NumItems; cnt++)
{
cuts[cnt] = rand.Next(itemSizeMin, itemSizeMax);;
demand[cnt] = rand.Next(itemDemandMin, itemDemandMax);
if (verbose)
{
System.Console.WriteLine(String.Format("item[{0}]\tweight={1}\tdemand={2}", cnt, cuts[cnt], demand[cnt]));
}
}
List <int[]> patterns;
SolveKnapsack(maxPatterns, cuts, ClothWidth, efficiency, out patterns);
SimplexSolver solver2 = new SimplexSolver();
int vId = 0;
int[] usage = new int[patterns.Count];
// construct rows that make sure that the demand is met for each kind of cut
for (int cnt = 0; cnt < NumItems; cnt++)
{
solver2.AddRow(String.Format("item{0}", cnt), out vId);
solver2.SetBounds(vId, demand[cnt], Rational.PositiveInfinity);
}
int patCnt = 0;
if (verbose)
{
System.Console.WriteLine(String.Format("Generated {0} patterns", patterns.Count));
}
// set usage coeffs (A matrix entries) -- put the patterns as columns in the MIP.
Dictionary <int, int> patIdForCol = new Dictionary <int, int>();
foreach (int[] pattern in patterns)
{
int pId = 0;
String varName = String.Format("Pattern{0}", patCnt);
solver2.AddVariable(varName, out pId);
patIdForCol[pId] = patCnt;
solver2.SetIntegrality(pId, true);
solver2.SetBounds(pId, 0, Rational.PositiveInfinity);
for (int cnt = 0; cnt < NumItems; cnt++)
{
solver2.SetCoefficient(cnt, pId, pattern[cnt]); // set the coefficient in the matrix
// accumulate the quantity used for this pattern. It will be used to figure out the scrap later.
usage[patCnt] += pattern[cnt] * cuts[cnt];
}
patCnt++;
}
// set objective coeffs. --- the cost is the scrap
solver2.AddRow("Scrap", out vId);
for (int cnt = 0; cnt < patterns.Count; cnt++)
{
int colId = solver2.GetIndexFromKey(String.Format("Pattern{0}", cnt));
solver2.SetCoefficient(vId, colId, (ClothWidth - usage[cnt]));
}
solver2.AddGoal(vId, 0, true);
// invoke the IP solver.
SimplexSolverParams parms = new SimplexSolverParams();
parms.MixedIntegerGenerateCuts = true;
parms.MixedIntegerPresolve = true;
if (saveMpsFile)
{
MpsWriter writer = new MpsWriter(solver2);
using (TextWriter textWriter = new StreamWriter(File.OpenWrite("c:\\temp\\cutstock.mps")))
{
writer.WriteMps(textWriter, true);
}
}
solver2.Solve(parms);
if (solver2.LpResult == LinearResult.Optimal &&
solver2.MipResult == LinearResult.Optimal)
{
//Rational[] solutionVals = solver2.GetValues();
int goalIndex = 0;
// output if desired.
if (verbose)
{
System.Console.WriteLine("Solver complete, printing cut plan.");
foreach (int cnt in solver2.VariableIndices)
{
Rational val = solver2.GetValue(cnt);
if (val != 0)
{
if (solver2.IsGoal(cnt))
{
goalIndex = cnt;
System.Console.WriteLine(String.Format("Goal:{0}\t: {1}\t", val, solver2.GetKeyFromIndex(cnt)));
}
else if (solver2.IsRow(cnt))
{
System.Console.WriteLine(String.Format("{0}:\tValue= {1}\t", solver2.GetKeyFromIndex(cnt), val));
}
else
{
System.Console.Write(String.Format("{0}\tQuantity={1}:\t", solver2.GetKeyFromIndex(cnt), val));
for (int cnt2 = 0; cnt2 < NumItems; cnt2++)
{
System.Console.Write(String.Format("{0} ", patterns[patIdForCol[cnt]][cnt2]));
}
System.Console.WriteLine(String.Format("\tUsage:{0} / {2} efficiency={1}%", usage[cnt - NumItems], (int)(100 * (double)usage[cnt - NumItems] / (double)ClothWidth), ClothWidth));
}
}
}
System.Console.WriteLine(String.Format("Total scrap={0}", solver2.GetSolutionValue(goalIndex)));
}
}
else
{
System.Console.WriteLine("Generated problem is infeasible. It is likely that more generated columns are needed.");
}
Console.WriteLine();
}
public List <Bag.Item> LinearProgramming(List <Item> items)
{
List <Bag.Item> choosenItems = new List <Bag.Item>();
int itemsCount = items.Count;
double capacity = MaxWeightAllowed;
SimplexSolver solver = new SimplexSolver();
//double[] estimatedProfitOfProjectX = new double[] { 1, 1.8, 1.6, 0.8, 1.4 };
//double[] capitalRequiredForProjectX = new double[] { 6, 12, 10, 4, 8 };
//double availableCapital = 20;
//int[] chooseProjectX = new int[5];
double[] values = new double[itemsCount];
double[] weights = new double[itemsCount];
for (int i = 0; i < itemsCount; i++)
{
values[i] = items[i].Value;
weights[i] = items[i].Weight;
}
int[] chosenItems = new int[itemsCount];
//int profit;
//solver.AddRow("profit", out profit);
//solver.AddGoal(profit, 0, false);
int MaximumValue;
solver.AddRow("MaximumValue", out MaximumValue);
solver.AddGoal(MaximumValue, 0, false);
//int expenditure;
//solver.AddRow("expenditure", out expenditure);
//solver.SetBounds(expenditure, 0, availableCapital);
int MaximumWeight;
solver.AddRow("MaximumWeight", out MaximumWeight);
solver.SetBounds(MaximumWeight, 0, capacity);
//for (int i = 0; i < 5; i++)
//{
// solver.AddVariable(string.Format("project{0}", i),
// out chooseProjectX[i]);
// solver.SetBounds(chooseProjectX[i], 0, 1);
// solver.SetIntegrality(chooseProjectX[i], true);
// solver.SetCoefficient(profit, chooseProjectX[i],
// estimatedProfitOfProjectX[i]);
// solver.SetCoefficient(expenditure, chooseProjectX[i],
// capitalRequiredForProjectX[i]);
//}
for (int i = 0; i < itemsCount; i++)
{
solver.AddVariable(string.Format("Item{0}", i),
out chosenItems[i]);
solver.SetBounds(chosenItems[i], 0, 1);
solver.SetIntegrality(chosenItems[i], true);
solver.SetCoefficient(MaximumValue, chosenItems[i],
values[i]);
solver.SetCoefficient(MaximumWeight, chosenItems[i],
weights[i]);
}
//SimplexSolverParams param = new SimplexSolverParams();
//param.MixedIntegerGenerateCuts = true;
//solver.Solve(param);
SimplexSolverParams param = new SimplexSolverParams();
param.MixedIntegerGenerateCuts = true;
solver.Solve(param);
//Console.WriteLine(solver.MipResult);
//for (int i = 0; i < 5; i++)
//{
// Console.WriteLine("Project {0} is {1} selected.", i,
// solver.GetValue(chooseProjectX[i]) == 1 ? "" : "not ");
//}
//Console.WriteLine("The estimated total profit is: ${0} million.",
// (double)solver.GetValue(profit).ToDouble());
//Console.WriteLine("The total expenditure is: ${0} million.",
// solver.GetValue(expenditure).ToDouble());
//string result = "";
for (int i = 0; i < itemsCount; i++)
{
if (solver.GetValue(chosenItems[i]) == 1)
{
//result += "Selected Item " + i + " ";
choosenItems.Add(items[i]);
}
}
//result += " value "+(double)solver.GetValue(MaximumValue).ToDouble()+ " weight "+ solver.GetValue(MaximumWeight).ToDouble();
return(choosenItems);
}
public SimplexSolver PrepareSimplexSolver(int maxAgents, out Dictionary <Models.Shift, int> shiftsExt, out int vidGoal)
{
var SX = new SimplexSolver();
var maxRq = HalfHourRequirements.Max(x => x.RequiredForce);
var sumRq = HalfHourRequirements.Sum(x => x.RequiredForce);
shiftsExt = null;
////goal: total no of agents
//int ridgoal;
//SX.AddRow(key: "goal", vid: out ridgoal);
//SX.SetBounds(ridgoal, lower: 0, upper: maxAgents); //total agents must range from 0 to max agents available
//SX.AddGoal(vid: ridgoal, pri: 1, fMinimize: true); //minimize total number of agents for that day
//goal: total no of agents, minimize overtime
int ridgoal;
SX.AddRow(key: "goal", vid: out ridgoal);
SX.SetBounds(ridgoal, lower: 0, upper: Rational.PositiveInfinity); // upper: maxAgents); //total agents must range from 0 to max agents available
SX.AddGoal(vid: ridgoal, pri: 2, fMinimize: true); //minimize total number of agents for that day
//goal2: Minimize excess force
int ridexcess;
SX.AddRow(key: "excess", vid: out ridexcess);
SX.SetBounds(ridexcess, lower: 0, upper: sumRq); //allow excess from 0 to sum of all requirements
SX.AddGoal(vid: ridexcess, pri: 1, fMinimize: true); //try to minimize excess time
var shiftsX = new Dictionary <Models.Shift, int>();
int i = 0;
Shifts.ForEach(x => {
int vid;
SX.AddVariable(key: string.Format("{0}. {1} {2:hh}:{2:mm} - {3:hh}:{3:mm}", i + 1, x.Name, x.Start, x.End), vid: out vid);
//the no of agents on each shift may not exceed to max requirements
SX.SetBounds(vid: vid, lower: 0, upper: maxRq);
shiftsX.Add(x, vid);
//SX.SetCoefficient(vidRow: ridgoal, vidVar: vid, num: 1); //normal weight: all shifts are the same
SX.SetCoefficient(vidRow: ridgoal, vidVar: vid, num: x.Duration.Hours <= 8 ? 1 : 10); //weights per shift: overtime = *10 more expensive than normal shifts
i++;
});
//Constraint - Agents from every active shift on every half hour must be >= requirement for that half hour
HalfHourRequirements.ForEach(hh =>
{
List <int> vidShiftsActive = new List <int>();
foreach (var entry in shiftsX)
{
if (entry.Key.IncludesHalfHour(hh.Start))
{
vidShiftsActive.Add(entry.Value);
}
}
//add constraint for sum of force of active shifts on that halfhour
//if we need agents but no shifts exists for a halfhour, do not add a constraint
if (vidShiftsActive.Count > 0)
{
int ridHalf;
SX.AddRow(key: string.Format("{0:hh}:{0:mm} [{1}]", hh.Start, hh.RequiredForce), vid: out ridHalf);
//specify whichs shifts contributes to half hour's total force
vidShiftsActive.ForEach(vidShift =>
{
SX.SetCoefficient(vidRow: ridHalf, vidVar: vidShift, num: 1);
SX.SetCoefficient(vidRow: ridexcess, vidVar: vidShift, num: 1);
});
//each 30' span, must have at least as many required agents
SX.SetBounds(vid: ridHalf, lower: hh.RequiredForce, upper: Rational.PositiveInfinity);
}
});
shiftsExt = shiftsX;
vidGoal = ridgoal;
return(SX);
}
public static RecipeGraph FromLibrary(Library library, IEnumerable <Item> inputs, IEnumerable <ItemAmount> outputs, Func <Item, double> costFunction)
{
if (library == null)
{
throw new ArgumentNullException("library");
}
if (inputs == null)
{
throw new ArgumentNullException("inputs");
}
if (outputs == null)
{
throw new ArgumentNullException("outputs");
}
if (costFunction == null)
{
throw new ArgumentNullException("costFunction");
}
var solver = new SimplexSolver();
var itemRows = new Dictionary <Item, int>();
var recipeVars = new Dictionary <Recipe, int>();
var wasteGoals = new Dictionary <Item, ILinearGoal>();
foreach (var item in library.Items)
{
int id;
solver.AddRow(item, out id);
itemRows.Add(item, id);
solver.SetBounds(id, 0, Rational.PositiveInfinity);
if (!inputs.Contains(item))
{
wasteGoals.Add(item, solver.AddGoal(id, 1, true));
}
}
// Bound output to requested values
foreach (var itemAmount in outputs)
{
var item = itemAmount.Item;
var amount = itemAmount.Amount;
solver.SetBounds(itemRows[item], amount, amount);
}
foreach (var recipe in library.Recipes)
{
int id;
solver.AddVariable(recipe, out id);
recipeVars.Add(recipe, id);
solver.SetBounds(id, 0, Rational.PositiveInfinity);
foreach (var input in recipe.Ingredients)
{
solver.SetCoefficient(itemRows[input.Item], id, -input.Amount);
}
foreach (var output in recipe.Results)
{
solver.SetCoefficient(itemRows[output.Item], id, output.Amount);
}
}
// Add input minimize goals
foreach (var item in inputs)
{
var row = itemRows[item];
solver.SetBounds(row, Rational.NegativeInfinity, 0);
solver.AddGoal(row, (int)(10000 * costFunction(item)), false);
}
solver.Solve(new SimplexSolverParams());
if (solver.SolutionQuality != Microsoft.SolverFoundation.Services.LinearSolutionQuality.Exact)
{
throw new InvalidOperationException("Cannot solve problem");
}
List <Tuple <Recipe, double> > usedRecipes = new List <Tuple <Recipe, double> >();
foreach (var recipe in library.Recipes)
{
var value = (double)solver.GetValue(recipeVars[recipe]);
if (value > 0)
{
usedRecipes.Add(new Tuple <Recipe, double>(recipe, value));
}
}
var sortedRecipes = usedRecipes.SortTopological((a, b) => a.Item1.Results.Select((i) => i.Item).Intersect(b.Item1.Ingredients.Select((i) => i.Item)).Any(), true);
List <SourceStep> inputSteps = new List <SourceStep>();
List <SinkStep> outputSteps = new List <SinkStep>();
List <SinkStep> wasteSteps = new List <SinkStep>();
Dictionary <Item, FlowStep> itemSteps = new Dictionary <Item, FlowStep>();
List <FlowStep> flowSteps = new List <FlowStep>();
List <TransformStep> transformSteps = new List <TransformStep>();
foreach (var item in library.Items)
{
var value = (double)solver.GetValue(itemRows[item]);
if (value > 0)
{
var sink = new SinkStep(new ItemAmount(item, value / 2));
if (outputs.Select((o) => o.Item).Contains(item))
{
outputSteps.Add(sink);
}
else
{
wasteSteps.Add(sink);
}
itemSteps.Add(item, sink);
}
else if (value < 0)
{
var source = new SourceStep(new ItemAmount(item, -value));
inputSteps.Add(source);
itemSteps.Add(item, source);
}
}
foreach (var recipe in sortedRecipes)
{
foreach (var result in recipe.Item1.Results)
{
var item = result.Item;
if (!itemSteps.ContainsKey(item))
{
var flowstep = new FlowStep(new ItemAmount(item, 0));
itemSteps.Add(item, flowstep);
flowSteps.Add(flowstep);
}
}
}
foreach (var recipe in sortedRecipes)
{
var previous = recipe.Item1.Ingredients.Select((i) => i.Item);
var step = new TransformStep(recipe.Item1, recipe.Item2);
foreach (var item in previous)
{
var prev = itemSteps[item];
step.Previous.Add(prev);
}
foreach (var amount in recipe.Item1.Results)
{
var item = amount.Item;
itemSteps[item].Previous.Add(step);
itemSteps[item].Item += amount * recipe.Item2;
}
transformSteps.Add(step);
}
return(new RecipeGraph(wasteSteps, inputSteps, outputSteps, flowSteps, transformSteps));
}