private void ExtractSingletonLabelsFrom(SolutionCollector theSolutionCollector)
{
foreach (var variableTuple in this.orToolsCache.SingletonVariableMap)
{
var solverValue = theSolutionCollector.Value(0, variableTuple.Value.Item2);
var modelValue = ConvertSolverValueToModel(variableTuple.Value.Item1, solverValue);
var newValue = new SingletonVariableLabelModel(variableTuple.Value.Item1, new ValueModel(modelValue));
this.snapshot.AddSingletonLabel(newValue);
}
}
private void ExtractAggregateLabelsFrom(SolutionCollector theSolutionCollector)
{
foreach (var aggregateTuple in this.orToolsCache.AggregateVariableMap)
{
var newValueBindings = new List <ValueModel>();
var orVariables = aggregateTuple.Value.Item2;
foreach (var orVariable in orVariables)
{
var solverValue = theSolutionCollector.Value(0, orVariable);
var modelValue = ConvertSolverValueToModel(aggregateTuple.Value.Item1, solverValue);
newValueBindings.Add(new ValueModel(modelValue));
}
var newCompoundLabel = new AggregateVariableLabelModel(aggregateTuple.Value.Item1, newValueBindings);
this.snapshot.AddAggregateLabel(newCompoundLabel);
}
}
private void ExtractBucketLabelFrom(SolutionCollector solutionCollector, OrBucketVariableMap bucketVariableMap)
{
var bundleLabels = new List <BundleLabelModel>();
foreach (var bundleMap in bucketVariableMap.GetBundleMaps())
{
var variableLabels = new List <SingletonVariableLabelModel>();
foreach (var variableMap in bundleMap.GetVariableMaps())
{
var solverValue = solutionCollector.Value(0, variableMap.SolverVariable);
var modelValue = ConvertSolverValueToModel(bucketVariableMap.Bucket, solverValue);
variableLabels.Add(new SingletonVariableLabelModel(variableMap.ModelVariable, new ValueModel(modelValue)));
}
bundleLabels.Add(new BundleLabelModel(bucketVariableMap.Bucket.Bundle, variableLabels));
}
var bucketLabel = new BucketLabelModel(bucketVariableMap.Bucket, bundleLabels);
this.snapshot.AddBucketLabel(bucketLabel);
}
// We don't need helper functions here
// Csharp syntax is easier than C++ syntax!
private static void CPisFun(int kBase, int time_limit_param, bool print)
{
// Use some profiling and change the default parameters of the solver
SolverParameters solver_params = new SolverParameters();
// Change the profile level
solver_params.profile_level = SolverParameters.NORMAL_PROFILING;
// Constraint Programming engine
Solver solver = new Solver("CP is fun!", solver_params);
// Decision variables
IntVar c = solver.MakeIntVar(1, kBase - 1, "C");
IntVar p = solver.MakeIntVar(0, kBase - 1, "P");
IntVar i = solver.MakeIntVar(1, kBase - 1, "I");
IntVar s = solver.MakeIntVar(0, kBase - 1, "S");
IntVar f = solver.MakeIntVar(1, kBase - 1, "F");
IntVar u = solver.MakeIntVar(0, kBase - 1, "U");
IntVar n = solver.MakeIntVar(0, kBase - 1, "N");
IntVar t = solver.MakeIntVar(1, kBase - 1, "T");
IntVar r = solver.MakeIntVar(0, kBase - 1, "R");
IntVar e = solver.MakeIntVar(0, kBase - 1, "E");
// We need to group variables in a vector to be able to use
// the global constraint AllDifferent
IntVar[] letters = new IntVar[] { c, p, i, s, f, u, n, t, r, e };
// Check if we have enough digits
if (kBase < letters.Length)
{
throw new Exception("kBase < letters.Length");
}
// Constraints
solver.Add(letters.AllDifferent());
// CP + IS + FUN = TRUE
solver.Add(p + s + n + kBase * (c + i + u) + kBase * kBase * f ==
e + kBase * u + kBase * kBase * r + kBase * kBase * kBase * t);
SolutionCollector all_solutions = solver.MakeAllSolutionCollector();
// Add the interesting variables to the SolutionCollector
all_solutions.Add(letters);
// Decision Builder: hot to scour the search tree
DecisionBuilder db = solver.MakePhase(letters,
Solver.CHOOSE_FIRST_UNBOUND,
Solver.ASSIGN_MIN_VALUE);
// Add some time limit
SearchLimit time_limit = solver.MakeTimeLimit(time_limit_param);
solver.Solve(db, all_solutions, time_limit);
// Retrieve the solutions
int numberSolutions = all_solutions.SolutionCount();
Console.WriteLine("Number of solutions: " + numberSolutions);
if (print)
{
for (int index = 0; index < numberSolutions; ++index)
{
Console.Write("C=" + all_solutions.Value(index, c));
Console.Write(" P=" + all_solutions.Value(index, p));
Console.Write(" I=" + all_solutions.Value(index, i));
Console.Write(" S=" + all_solutions.Value(index, s));
Console.Write(" F=" + all_solutions.Value(index, f));
Console.Write(" U=" + all_solutions.Value(index, u));
Console.Write(" N=" + all_solutions.Value(index, n));
Console.Write(" T=" + all_solutions.Value(index, t));
Console.Write(" R=" + all_solutions.Value(index, r));
Console.Write(" E=" + all_solutions.Value(index, e));
Console.WriteLine();
}
}
// Save profile in file
solver.ExportProfilingOverview("profile.txt");
}
public void RunJobShopScheduling(String solverType)
{
Solver solver = new Solver(solverType);
if (solver == null)
{
Console.WriteLine("JobShop failed to create a solver " + solverType);
return;
}
// All tasks
Dictionary <string, IntervalVar> allTasks = new Dictionary <string, IntervalVar>();
// Creates jobs
for (int i = 0; i < allJobs.Count; i++)
{
for (int j = 0; j < machines.ElementAt(i).Count; j++)
{
IntervalVar oneTask = solver.MakeFixedDurationIntervalVar(0,
horizon,
processingTimes.ElementAt(i).ElementAt(j),
false,
"Job_" + i + "_" + j);
//Console.WriteLine("Job_" + i + "_" + j);
allTasks.Add("Job_" + i + "_" + j, oneTask);
}
}
// Create sequence variables and add disjuctive constraints
List <SequenceVar> allSequences = new List <SequenceVar>();
foreach (var machine in allMachines)
{
List <IntervalVar> machinesJobs = new List <IntervalVar>();
for (int i = 0; i < allJobs.Count; i++)
{
for (int k = 0; k < machines.ElementAt(i).Count; k++)
{
if (machines.ElementAt(i).ElementAt(k) == machine)
{
machinesJobs.Add(allTasks["Job_" + i + "_" + k]);
}
}
}
DisjunctiveConstraint disj = solver.MakeDisjunctiveConstraint(machinesJobs.ToArray(),
"machine " + machine);
allSequences.Add(disj.SequenceVar());
solver.Add(disj);
}
// Add conjunctive constraints
foreach (var job in allJobs)
{
for (int j = 0; j < machines.ElementAt(job).Count - 1; j++)
{
solver.Add(allTasks["Job_" + job + "_" + (j + 1)].StartsAfterEnd(allTasks["Job_" + job + "_" + j]));
}
}
// Set the objective
IntVar[] allEnds = new IntVar[jobsCount];
for (int i = 0; i < allJobs.Count; i++)
{
allEnds[i] = allTasks["Job_" + i + "_" + (machines.ElementAt(i).Count - 1)].EndExpr().Var();
}
// Objective: minimize the makespan (maximum end times of all tasks)
// of the problem.
IntVar objectiveVar = solver.MakeMax(allEnds).Var();
OptimizeVar objectiveMonitor = solver.MakeMinimize(objectiveVar, 1);
// Create search phases
DecisionBuilder sequencePhase = solver.MakePhase(allSequences.ToArray(), Solver.SEQUENCE_DEFAULT);
DecisionBuilder varsPhase = solver.MakePhase(objectiveVar, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
// The main decision builder (ranks all tasks, then fixes the
// objectiveVariable).
DecisionBuilder mainPhase = solver.Compose(sequencePhase, varsPhase);
SolutionCollector collector = solver.MakeLastSolutionCollector();
collector.Add(allSequences.ToArray());
collector.Add(objectiveVar);
foreach (var i in allMachines)
{
SequenceVar sequence = allSequences.ElementAt(i);
long sequenceCount = sequence.Size();
for (int j = 0; j < sequenceCount; j++)
{
IntervalVar t = sequence.Interval(j);
collector.Add(t.StartExpr().Var());
collector.Add(t.EndExpr().Var());
}
}
// Search.
bool solutionFound = solver.Solve(mainPhase, null, objectiveMonitor, null, collector);
if (solutionFound)
{
//The index of the solution from the collector
const int SOLUTION_INDEX = 0;
Assignment solution = collector.Solution(SOLUTION_INDEX);
string solLine = "";
string solLineTasks = "";
Console.WriteLine("Time Intervals for Tasks\n");
List <List <TimeSpan> > tuplesSolution = new List <List <TimeSpan> >();
for (int m = 0; m < this.machinesCount; m++)
{
//Console.WriteLine("MachineCount: " + this.machinesCount);
solLine = "Machine " + m + " :";
solLineTasks = "Machine " + m + ": ";
SequenceVar seq = allSequences.ElementAt(m);
int[] storedSequence = collector.ForwardSequence(SOLUTION_INDEX, seq);
foreach (int taskIndex in storedSequence)
{
//Console.WriteLine("taskIndex: " + taskIndex);
IntervalVar task = seq.Interval(taskIndex);
solLineTasks += task.Name() + " ";
//Console.WriteLine("Len: " + storedSequence.Length);
}
// First GreenTime
//TimeSpan timeToAdd = tuplesSolution.First().Last();
TimeSpan timeEndBucket = this.bucketInfo.EndBucket;
TimeSpan timeStartBucket = this.bucketInfo.StartBucket;
int solutionSize = tuplesSolution.Count;
bool isEnd = false;
List <int> list_id = jobIds.ElementAt(m);
// Adding GreenTime to Solution
while (timeStartBucket.CompareTo(timeEndBucket) < 0)
{
foreach (int taskIndex in storedSequence)
{
IntervalVar task = seq.Interval(taskIndex);
var startValue = TimeSpan.FromSeconds(collector.Value(0, task.StartExpr().Var()));
var endValue = TimeSpan.FromSeconds(collector.Value(0, task.EndExpr().Var()));
TimeSpan greenTime = endValue.Subtract(startValue);
TimeSpan timeEnd;
timeEnd = timeStartBucket.Add(greenTime);
List <TimeSpan> tuple = new List <TimeSpan>();
tuple.Add(timeStartBucket);
if (timeEndBucket.CompareTo(timeEnd) < 0)
{
timeEnd = timeEndBucket;
isEnd = true;
}
tuple.Add(timeEnd);
tuplesSolution.Add(tuple);
if (taskIndex + 1 < list_id.Count() && list_id.ElementAt(taskIndex) == list_id.ElementAt(taskIndex + 1))
{
timeStartBucket = timeStartBucket.Add(TimeSpan.FromSeconds(this.cycleTime));
}
else
{
timeStartBucket = timeEnd;
}
if (isEnd)
{
break;
}
}
}
//
// Saving the Solution to a XML file
//
JobShop.save(m, tuplesSolution);
//solLine += "\n";
//solLineTasks += "\n";
//Console.WriteLine(solLineTasks);
//Console.WriteLine(solLine);
}
}
else
{
Console.WriteLine("No solution found!");
}
}
public void RunJobShopScheduling(String solverType)
{
Solver solver = new Solver(solverType);
if (solver == null)
{
Console.WriteLine("JobShop failed to create a solver " + solverType);
return;
}
// All tasks
Dictionary <string, IntervalVar> allTasks = new Dictionary <string, IntervalVar>();
// Creates jobs
for (int i = 0; i < allJobs.Count; i++)
{
for (int j = 0; j < machines.ElementAt(i).Count; j++)
{
IntervalVar oneTask = solver.MakeFixedDurationIntervalVar(0,
horizon,
processingTimes.ElementAt(i).ElementAt(j),
false,
"Job_" + i + "_" + j);
allTasks.Add("Job_" + i + "_" + j, oneTask);
}
}
// Create sequence variables and add disjuctive constraints
List <SequenceVar> allSequences = new List <SequenceVar>();
foreach (var machine in allMachines)
{
List <IntervalVar> machinesJobs = new List <IntervalVar>();
for (int i = 0; i < allJobs.Count; i++)
{
for (int k = 0; k < machines.ElementAt(i).Count; k++)
{
if (machines.ElementAt(i).ElementAt(k) == machine)
{
machinesJobs.Add(allTasks["Job_" + i + "_" + k]);
}
}
}
DisjunctiveConstraint disj = solver.MakeDisjunctiveConstraint(machinesJobs.ToArray(),
"machine " + machine);
allSequences.Add(disj.SequenceVar());
solver.Add(disj);
}
// Add conjunctive constraints
foreach (var job in allJobs)
{
for (int j = 0; j < machines.ElementAt(job).Count - 1; j++)
{
solver.Add(allTasks["Job_" + job + "_" + (j + 1)].StartsAfterEnd(allTasks["Job_" + job + "_" + j]));
}
}
// Set the objective
IntVar[] allEnds = new IntVar[jobsCount];
for (int i = 0; i < allJobs.Count; i++)
{
allEnds[i] = allTasks["Job_" + i + "_" + (machines.ElementAt(i).Count - 1)].EndExpr().Var();
}
// Objective: minimize the makespan (maximum end times of all tasks)
// of the problem.
IntVar objectiveVar = solver.MakeMax(allEnds).Var();
OptimizeVar objectiveMonitor = solver.MakeMinimize(objectiveVar, 1);
// Create search phases
DecisionBuilder sequencePhase = solver.MakePhase(allSequences.ToArray(), Solver.SEQUENCE_DEFAULT);
DecisionBuilder varsPhase = solver.MakePhase(objectiveVar, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
// The main decision builder (ranks all tasks, then fixes the
// objectiveVariable).
DecisionBuilder mainPhase = solver.Compose(sequencePhase, varsPhase);
SolutionCollector collector = solver.MakeLastSolutionCollector();
collector.Add(allSequences.ToArray());
collector.Add(objectiveVar);
foreach (var i in allMachines)
{
SequenceVar sequence = allSequences.ElementAt(i);
long sequenceCount = sequence.Size();
for (int j = 0; j < sequenceCount; j++)
{
IntervalVar t = sequence.Interval(j);
collector.Add(t.StartExpr().Var());
collector.Add(t.EndExpr().Var());
}
}
// Search.
bool solutionFound = solver.Solve(mainPhase, null, objectiveMonitor, null, collector);
if (solutionFound)
{
//The index of the solution from the collector
const int SOLUTION_INDEX = 0;
Assignment solution = collector.Solution(SOLUTION_INDEX);
string solLine = "";
string solLineTasks = "";
Console.WriteLine("Time Intervals for Tasks\n");
for (int m = 0; m < this.machinesCount; m++)
{
//Console.WriteLine("MachineCount: " + this.machinesCount);
solLine = "Machine " + m + " :";
solLineTasks = "Machine " + m + ": ";
SequenceVar seq = allSequences.ElementAt(m);
int[] storedSequence = collector.ForwardSequence(SOLUTION_INDEX, seq);
foreach (int taskIndex in storedSequence)
{
//Console.WriteLine("taskIndex: " + taskIndex);
IntervalVar task = seq.Interval(taskIndex);
solLineTasks += task.Name() + " ";
//Console.WriteLine("Len: " + storedSequence.Length);
}
foreach (int taskIndex in storedSequence)
{
IntervalVar task = seq.Interval(taskIndex);
string solTemp = "[" + collector.Value(0, task.StartExpr().Var()) + ",";
solTemp += collector.Value(0, task.EndExpr().Var()) + "] ";
solLine += solTemp;
}
//solLine += "\n";
solLineTasks += "\n";
//Console.WriteLine(solLineTasks);
Console.WriteLine(solLine);
}
}
else
{
Console.WriteLine("No solution found!");
}
}
public static void Solve(FlexibleJobShopData data, bool chatty = false)
{
// Compute horizon
var horizon = data.JobList.Sum(
j => j.TaskList.Sum(
t => t.Durations.Max()));
// ----- Create all intervals and vars -----
/*
* // Use some profiling and change the default parameters of the solver
* SolverParameters solverParams = new SolverParameters();
* // Change the profile level
* solverParams.profile_level = SolverParameters.NORMAL_PROFILING;
* // Constraint programming engine
* Solver solver = new Solver("JobShop", solverParams);
*/
// Constraint programming engine
Solver solver = new Solver($"FlexibleJobShop: {data.Name}");
// Initialize dictionaries to hold references to task intervals
var tasksByJobId = new Dictionary <uint, List <TaskAlternative> >();
foreach (var job in data.JobList)
{
tasksByJobId[job.Id] = new List <TaskAlternative>(job.TaskList.Count);
}
var tasksByMachineId = new Dictionary <uint, IntervalVarVector>();
foreach (var machine in data.MachineList)
{
tasksByMachineId[machine.Id] = new IntervalVarVector();
}
// Creates all individual interval variables and collect in dictionaries
foreach (var job in data.JobList)
{
foreach (var task in job.TaskList)
{
var alternative = new TaskAlternative(job.Id);
tasksByJobId[job.Id].Add(alternative);
var activeVariables = new IntVarVector();
var hasAlternatives = task.AlternativesCount > 1;
for (int alt = 0; alt < task.AlternativesCount; alt++)
{
var machine = task.Machines[alt];
var duration = task.Durations[alt];
var name = $"{task.Name}; Alternative {alt}: {machine.Name}, Duration {duration}";
IntervalVar interval = solver.MakeFixedDurationIntervalVar(0, horizon, duration, hasAlternatives, name);
alternative.Add(interval);
tasksByMachineId[machine.Id].Add(interval);
if (hasAlternatives)
{
activeVariables.Add(interval.PerformedExpr().Var());
}
}
alternative.AlternativeVar = solver.MakeIntVar(0, task.AlternativesCount - 1, task.Name);
if (hasAlternatives)
{
solver.Add(solver.MakeMapDomain(alternative.AlternativeVar, activeVariables));
}
}
}
// ----- Create model -----
// Create precedences inside jobs
foreach (var taskAltList in tasksByJobId.Values)
{
for (int i = 0; i < taskAltList.Count - 1; i++)
{
TaskAlternative currentTaskAlt = taskAltList[i];
TaskAlternative nextTaskAlt = taskAltList[i + 1];
foreach (var alt1 in currentTaskAlt)
{
foreach (var alt2 in nextTaskAlt)
{
solver.Add(solver.MakeIntervalVarRelation(
alt2, Solver.STARTS_AFTER_END, alt1));
}
}
}
}
// Collect alternative variables.
IntVarVector alternativeVariableVec = new IntVarVector();
foreach (var taskAltList in tasksByJobId.Values)
{
foreach (var taskAlt in taskAltList)
{
if (!taskAlt.AlternativeVar.Bound())
{
alternativeVariableVec.Add(taskAlt.AlternativeVar);
}
}
}
// Add disjunctive constraints on unary resources, and create
// sequence variables. A sequence variable is a dedicated variable
// whose job is to sequence interval variables.
SequenceVarVector allSequences = new SequenceVarVector();
foreach (var machine in data.MachineList)
{
DisjunctiveConstraint disjCt = solver.MakeDisjunctiveConstraint(
tasksByMachineId[machine.Id], machine.Name);
solver.Add(disjCt);
allSequences.Add(disjCt.SequenceVar());
}
// Create array of end_times of jobs
IntVarVector endsVec = new IntVarVector();
foreach (var taskAltList in tasksByJobId.Values)
{
TaskAlternative lastTaskAlt = taskAltList.Last();
foreach (var alt in lastTaskAlt)
{
endsVec.Add(alt.SafeEndExpr(-1).Var());
}
}
// Objective: minimize the makespan (maximum end times of all tasks)
// of the problem.
IntVar objectiveVar = solver.MakeMax(endsVec).Var();
OptimizeVar objectiveMonitor = solver.MakeMinimize(objectiveVar, 1);
// ----- Search monitors and decision builder -----
// This decision builder will assign all alternative variables.
DecisionBuilder alternativePhase = solver.MakePhase(alternativeVariableVec, Solver.CHOOSE_MIN_SIZE, Solver.ASSIGN_MIN_VALUE);
// This decision builder will rank all tasks on all machines.
DecisionBuilder sequencePhase = solver.MakePhase(allSequences, Solver.SEQUENCE_DEFAULT);
// After the ranking of tasks, the schedule is still loose and any
// task can be postponed at will. But, because the problem is now a PERT
// (http://en.wikipedia.org/wiki/Program_Evaluation_and_Review_Technique),
// we can schedule each task at its earliest start time. This is
// conveniently done by fixing the objective variable to its
// minimum value.
DecisionBuilder objectivePhase = solver.MakePhase(objectiveVar, Solver.CHOOSE_FIRST_UNBOUND, Solver.ASSIGN_MIN_VALUE);
// The main decision builder (ranks all tasks, then fixes the
// objective_variable).
DecisionBuilder mainPhase = solver.Compose(alternativePhase, sequencePhase, objectivePhase);
// Search log
const int kLogFrequency = 1000000;
SearchMonitor searchLog = solver.MakeSearchLog(kLogFrequency, objectiveMonitor);
const long FLAGS_time_limit_in_ms = 1000 * 60 * 20;
SearchLimit limit = null;
if (FLAGS_time_limit_in_ms > 0)
{
limit = solver.MakeTimeLimit(FLAGS_time_limit_in_ms);
}
SolutionCollector collector = solver.MakeLastSolutionCollector();
collector.AddObjective(objectiveVar);
collector.Add(alternativeVariableVec);
collector.Add(allSequences);
foreach (var taskVec in tasksByMachineId.Values)
{
foreach (var task in taskVec)
{
collector.Add(task.StartExpr().Var());
}
}
// ----- Search -----
bool solutionFound = solver.Solve(mainPhase, searchLog, objectiveMonitor, limit, collector);
if (solutionFound)
{
// The index of the solution from the collector
const int SOLUTION_INDEX = 0;
Assignment solution = collector.Solution(SOLUTION_INDEX);
Console.WriteLine();
uint machineIdx = 0;
foreach (var seq in allSequences)
{
machineIdx++;
var taskSeq = collector.ForwardSequence(SOLUTION_INDEX, seq);
Console.WriteLine($"{seq.Name()}:");
Console.WriteLine(" Tasks: " + string.Join(", ", taskSeq));
//foreach (var taskIndex in storedSequence)
//{
// IntervalVar task = sequence.Interval(taskIndex);
// long startMin = solution.StartMin(task);
// long startMax = solution.StartMax(task);
// if (startMin == startMax)
// {
// Console.WriteLine($"Task {task.Name()} starts at {startMin}.");
// }
// else
// {
// Console.WriteLine($"Task {task.Name()} starts between {startMin} and {startMax}.");
// }
//}
Console.WriteLine();
Console.Write(" Starting times:");
foreach (var s in taskSeq)
{
Console.Write(" " + collector.Value(0, tasksByMachineId[machineIdx][s].StartExpr().Var()).ToString());
}
Console.WriteLine();
}
//var x = tasksByMachineId[1][0].StartExpr().Var();
//var xValStr = collector.Value(0, x).ToString();
Console.WriteLine("objective function value = " + solution.ObjectiveValue()); //TEMP
}
// Save profile in file
//solver.ExportProfilingOverview("profile.txt");
// Done
solver.EndSearch();
}