public void RRHeurStageI(string filename)
{
for (int i = 0; i < ntests; i++)
{
var algname = nameof(RRHeurStageI) + "20extra";
// formulation.AvailabilityHardConstraint = false;
var data = Data.ReadXml(dataPath + filename, "120", "4");
//var formulation = ProblemFormulation.UD2NoOverbook;
// formulation.AvailabilityHardConstraint = false;
// formulation.OverbookingAllowed = 0.35;
var ModelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
TuneGurobi = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
// UseRoomsAsTypes = true,
UseRoomsAsTypes = false,
RoomBoundForEachTimeSlot = true,
Seed = seeds[i],
};
var heuristics = new Heuristics(TimelimitStageI + 20, data, formulation, ModelParameters);
var sol = heuristics.Run();
Console.WriteLine(sol.AnalyzeSolution());
var objective = (int)Math.Round(sol.Objective);
var dataname = System.IO.Path.GetFileNameWithoutExtension(filename);
File.AppendAllText(@"c:\temp\heuristic.txt",
$"{algname};{dataname};{DateTime.Now};{i};{objective};{heuristics.totalseconds};{heuristics.roomtightseconds}\n");
}
}
public MultiTimeslotSolver(int timelimit, Data data, ProblemFormulation formulation, CCTModel.MIPModelParameters mipModelParameters)
{
Timelimit = timelimit;
this.data = data;
this.formulation = formulation;
this.MIPmodelParameters = mipModelParameters;
}
public void Relaxunavailability(string filename)
{
var algname = nameof(Relaxunavailability);
var data = Data.ReadXml(dataPath + filename, "120", "4");
for (int i = 0; i < 1; i++)
{
//var formulation = ProblemFormulation.UD2NoOverbook;
//Formulation.AvailabilityHardConstraint = false;
//Formulation.OverbookingAllowed = 0.35;
var ModelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
TuneGurobi = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
Seed = seeds[i],
};
formulation.AvailabilityHardConstraint = false;
var model = new CCTModel(data, formulation, ModelParameters);
var stopwatch = Stopwatch.StartNew();
model.Optimize(30);
stopwatch.Stop();
var objective = (int)Math.Round(model.Objective);
var dataname = System.IO.Path.GetFileNameWithoutExtension(filename);
File.AppendAllText(@"c:\temp\heuristic.txt",
$"{algname};{dataname};{DateTime.Now};{i};{objective};{(int) stopwatch.Elapsed.TotalSeconds};\n");
}
}
public void TestCurriculaConstraint()
{
var data = Data.ReadXml(dataPath + ITC_Comp05, "120", "4");
// var formulation = ProblemFormulation.UD2;
//Formulation.AvailabilityHardConstraint = false;
//Formulation.OverbookingAllowed = 0.35;
var ModelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
ConstraintPenalty = 50,
AbortSolverOnZeroPenalty = true,
};
var model = new CCTModel(data, formulation, ModelParameters);
var watch = Stopwatch.StartNew();
model.Optimize(30, 0.99);
var obj = model.ObjSoftCons;
while (watch.Elapsed.TotalSeconds < TimelimitStageI * 2)
{
model.SetQualConstraint(obj - 5);
model.Optimize(120);
obj = model.ObjSoftCons;
Console.WriteLine($"Objective: {obj}");
}
}
public void TestSolveStageI(string filename)
{
var algname = nameof(TestSolveStageI);
var data = Data.ReadXml(dataPath + filename, "120", "4");
for (int i = 0; i < ntests; i++)
{
//var formulation = ProblemFormulation.UD2NoOverbook;
//Formulation.AvailabilityHardConstraint = false;
//Formulation.OverbookingAllowed = 0.35;
var ModelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
TuneGurobi = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
Seed = seeds[i],
};
var model = new CCTModel(data, formulation, ModelParameters);
model.Optimize(TimelimitStageI);
var objective = (int)Math.Round(model.Objective);
File.AppendAllText(@"c:\temp\heuristic.txt",
$"{algname};{filename};{DateTime.Now.ToString()};{i};{objective}\n");
}
}
public Heuristics(int timelimit, Data data, ProblemFormulation formulation, CCTModel.MIPModelParameters mipModelParameters)
{
TimelimitStageI = timelimit;
this.data = data;
this.formulation = formulation;
this.MIPmodelParameters = mipModelParameters;
}
public void TestStageI(string filename)
{
var data = Data.ReadXml(dataPath + filename, "120", "4");
var formulation = ProblemFormulation.UD2NoOverbook;
var mippar = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
UseHallsConditions = true,
UseRoomHallConditions = true,
TuneGurobi = true,
SaveBoundInformation = true,
};
var model = new CCTModel(data, formulation, mippar);
//model.WriteModel(@"c:\temp\model.lp");
Console.WriteLine("Solving");
model.Optimize(20);
model.DisplayObjectives();
var sol = new Solution(data, formulation);
sol.SetAssignments(model.GetAssignments());
// model.DisplaySoftConstraintViolations();
//var sol = new Solution(data,Formulation);
//sol.SetAssignments(model.GetAssignments());
//Console.WriteLine(sol.AnalyzeSolution());
//sol.Save(@"c:\temp\solution.ctt");
// File.AppendAllText(@"c:\temp\results.txt", $"30s;{filename};{model.Objective}\n");
Console.WriteLine("MWD;CC;obj;bnd");
Console.WriteLine(String.Join("\n", model.bounds.Select(t => $"{t.Item1};{t.Item2};{t.Item3};{t.Item4}")));
Console.WriteLine(sol.AnalyzeSolution());
}
public MultiObjectiveSolver(Data data, ProblemFormulation formulation, CCTModel.MIPModelParameters mipparameters)
{
Formulation = formulation;
_data = data;
MipParameters = mipparameters;
mipparameters.NSols = 100;
_model = new CCTModel(_data, Formulation, MipParameters);
}
public void Lexi_Cost_Soft([Values(
ITC_Comp01,
ITC_Comp05,
ITC_Comp06,
ITC_Comp08,
ITC_Comp12,
ITC_Comp11,
ITC_Comp18,
//more
ITC_Comp02,
ITC_Comp03,
ITC_Comp04,
ITC_Comp07
)] string filename, [Values(false)] bool SpecificQual)
{
var algoname = nameof(Lexi_Cost_Soft) + $"_SpecQual_{SpecificQual}";
// if (ConsoleToFile)
// SetConsoleOutputToFile(@"c:\temp\multiobjective\\logs\\" + filename + "\\" + algoname + ".log");
var data = Data.ReadXml(dataPath + filename, "120", "4");
// var newrooms = CreateRooms(data);
var newrooms = CreateRoomsFixedSize(data, 25, 1);
data.SetRoomList(newrooms);
var mipModelParameters = new CCTModel.MIPModelParameters()
{
TuneGurobi = true,
UseStageIandII = false,
// UseStageIandII = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
ConstraintPenalty = null,
UseRoomsAsTypes = true,
};
var solver = new MultiObjectiveSolver(data, _problemFormulation, mipModelParameters)
{
Timelimit = Timelimit,
ExtraTimeOnCornerPointsMultiplier = 8,
UseSpecificObjConstraints = SpecificQual,
};
Console.WriteLine("Algorithm: " + algoname);
DisplayParameterObject(_problemFormulation);
DisplayParameterObject(solver);
DisplayParameterObject(solver.MipParameters);
double minCost;
int maxSoftConsViol;
int minCostBound;
solver.CalcLexMinCostMaxSoft(out minCost, out maxSoftConsViol, out minCostBound);
Console.WriteLine($"MaxSoft: {maxSoftConsViol} MinCost: {minCost}");
}
public QualityRecoveringOptimizer(Data data, Solution solutionBefore, ProblemFormulation formulation, CCTModel.MIPModelParameters mipModelParameters)
{
this.data = data;
this.formulation = formulation;
this.MIPmodelParameters = mipModelParameters;
this.solutionBefore = solutionBefore;
model = new CCTModel(data, formulation, MIPmodelParameters);
model.SetProximityOrigin(solutionBefore._assignments.ToList());
model.SetMipHints(solutionBefore._assignments.ToList());
}
public void PenaltiesScaledByCourseSizes(string filename)
{
var data = Data.ReadXml(dataPath + filename, "120", "4");
//var formulation = ProblemFormulation.UD2NoOverbook;
//Formulation.AvailabilityHardConstraint = false;
//Formulation.OverbookingAllowed = 0.35;
var ModelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
TuneGurobi = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
ScalePenaltyWithCourseSize = true,
};
var model = new CCTModel(data, formulation, ModelParameters);
model.Optimize(TimelimitStageI * 2);
File.AppendAllText(@"c:\temp\heuristic.txt", $"{nameof(PenaltiesScaledByCourseSizes)};{filename};{model.Objective}\n");
}
// [TestCaseSource(nameof(TestMultiObjTestBed))]
public void WeightedSumTest([Values(
ITC_Comp05,
ITC_Comp18)] string filename)
{
var algoname = nameof(WeightedSumTest);
if (ConsoleToFile)
{
SetConsoleOutputToFile(@"c:\temp\multiobjective\\logs\\" + filename + "\\" + algoname + ".log");
}
var data = Data.ReadXml(dataPath + filename, "120", "4");
// var newrooms = CreateRooms(data);
var newrooms = CreateRoomsFixedSize(data, 25, 1);
data.SetRoomList(newrooms);
var mipModelParameters = new CCTModel.MIPModelParameters()
{
TuneGurobi = true,
UseStageIandII = false,
// UseStageIandII = true,
UseRoomsAsTypes = true,
};
//var problemFormulation = ProblemFormulation.EverythingZero;
//problemFormulation.BadTimeslotsWeight = 1;
var solver = new MultiObjectiveSolver(data, _problemFormulation, mipModelParameters)
{
Timelimit = 120,//Timelimit,
ExtraTimeOnCornerPointsMultiplier = 1,
UseEpsilonMethod = false,
Steps = 3
};
Console.WriteLine("Algorithm: " + algoname);
DisplayParameterObject(_problemFormulation);
DisplayParameterObject(solver);
DisplayParameterObject(solver.MipParameters);
var pareto = solver.Run();
WriteSol(@"c:\temp\multiobjective\", filename, data, _problemFormulation, algoname, DateTime.Now.ToString(), pareto);
}
// [TestCaseSource(nameof(TestMultiObjTestBed))]
public void MultiObj3index([Values(
ITC_Comp02)] string filename)
{
var algoname = nameof(MultiObj3index) + "3idx";
if (ConsoleToFile)
{
SetConsoleOutputToFile(@"c:\temp\multiobjective\\logs\\" + filename + "\\" + algoname + ".log");
}
var data = Data.ReadXml(dataPath + filename, "120", "4");
// var newrooms = CreateRooms(data);
var newrooms = CreateRoomsFixedSize(data, 25, 1);
data.SetRoomList(newrooms);
var mipModelParameters = new CCTModel.MIPModelParameters()
{
TuneGurobi = false, //notice
//UseStageIandII = false,
UseStageIandII = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
ConstraintPenalty = null,
UseRoomsAsTypes = true,
};
var solver = new MultiObjectiveSolver(data, ProblemFormulation.UD2, mipModelParameters)
{
Timelimit = 60,
EpsilonOnQuality = false,
ExtraTimeOnCornerPointsMultiplier = 1,
};
Console.WriteLine("Algorithm: " + algoname);
DisplayParameterObject(_problemFormulation);
DisplayParameterObject(solver);
DisplayParameterObject(solver.MipParameters);
var pareto = solver.RunExhaustiveMethod();
WriteSol(@"c:\temp\multiobjective\", filename, data, _problemFormulation, algoname, DateTime.Now.ToString(), pareto);
}
public void TestSolveStageIandII(string filename)
{
var data = Data.ReadXml(dataPath + filename, "120", "4");
//var formulation = ProblemFormulation.UD2NoOverbook;
//Formulation.AvailabilityHardConstraint = false;
//Formulation.OverbookingAllowed = 0.35;
var ModelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
TuneGurobi = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
};
var model = new CCTModel(data, formulation, ModelParameters);
model.Optimize(TimelimitStageI);
//todo make test. use fixing as in multi
File.AppendAllText(@"c:\temp\heuristic.txt", $"{nameof(TestSolveStageI)};{filename};{model.Objective}\n");
}
public void MaximizeRoomUtilization(string filename)
{
var data = Data.ReadXml(dataPath + filename, "120", "4");
Console.WriteLine($"Dataset: {data.Name}");
Console.WriteLine($"Courses: {data.Courses.Count} Lectures: {data.Courses.Sum(c => c.Lectures)}");
//DisplayRoomStats("original",data.Rooms.ToDictionary(r => r, r => 1), data);
// var newrooms = CreateRooms(data);
var newrooms = CreateRoomsFixedSize(data, 25, 1);
data.SetRoomList(newrooms);
Console.WriteLine("\nLectures:\n");
Console.WriteLine("size,hours");
foreach (var coursesizes in data.Courses.GroupBy(c => c.NumberOfStudents).OrderBy(c => c.Key))
{
Console.WriteLine($"{coursesizes.Key},{coursesizes.Sum(s => s.Lectures)}");
}
var formulation = ProblemFormulation.MinimizeRoomCost;
var par = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
UseHallsConditions = true,
UseRoomHallConditions = true,
TuneGurobi = false,
UseRoomsAsTypes = true,
};
var model = new CCTModel(data, formulation, par);
model.Optimize();
DisplayRoomStats("optimized", model.GetUsedRooms(), data);
}
public void TestLocalBranchingConstraint(bool usecurr, int N)
{
var data = Data.ReadXml(dataPath + ITC_Comp05, "120", "4");
//var formulation = ProblemFormulation.UD2;
//Formulation.AvailabilityHardConstraint = false;
//Formulation.OverbookingAllowed = 0.35;
var modelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = false
};
var model = new CCTModel(data, formulation, modelParameters);
model.Optimize(30 * 3, 0.99);
model.SetProxConstraint(N);
var watch = Stopwatch.StartNew();
while (watch.Elapsed.TotalSeconds < TimelimitStageI)
{
model.SetProximityOrigin(usecurr);
model.Optimize(15);
}
}
public void TestSolveStageIRoomRelaxedAndFixed(string filename)
{
for (int i = 0; i < ntests; i++)
{
var algname = nameof(TestSolveStageIRoomRelaxedAndFixed) + "roomrelaxontimeslot";
// formulation.AvailabilityHardConstraint = false;
var data = Data.ReadXml(dataPath + filename, "120", "4");
//var formulation = ProblemFormulation.UD2NoOverbook;
// formulation.AvailabilityHardConstraint = false;
// formulation.OverbookingAllowed = 0.35;
var ModelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
TuneGurobi = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
// UseRoomsAsTypes = true,
UseRoomsAsTypes = false,
RoomBoundForEachTimeSlot = true,
Seed = seeds[i],
};
var heuristics = new Heuristics(TimelimitStageI, data, formulation, ModelParameters);
var sol = heuristics.Run();
Console.WriteLine(sol.AnalyzeSolution());
//sol = heuristics.SolveStageII();
//Console.WriteLine(sol.AnalyzeSolution());
//var model = RoomRelaxAndTight(data, ModelParameters);
var objective = (int)Math.Round(sol.Objective);
File.AppendAllText(@"c:\temp\heuristic.txt",
$"{algname};{filename};{DateTime.Now.ToString()};{i};{objective}\n");
}
}
// [TestCaseSource(nameof(TestMultiObjTestBed))]
public void MultiCornerPointsTest([Values(
/* ITC_Comp01,
* ITC_Comp05,
* ITC_Comp06,
* ITC_Comp08,
* ITC_Comp12,
* ITC_Comp11,
* ITC_Comp18
*/
ITC_Comp01,
ITC_Comp02,
ITC_Comp03,
ITC_Comp04,
ITC_Comp05,
ITC_Comp06,
ITC_Comp07,
ITC_Comp08,
ITC_Comp09,
ITC_Comp10,
ITC_Comp11,
ITC_Comp12,
ITC_Comp13,
ITC_Comp14,
ITC_Comp15,
ITC_Comp16,
ITC_Comp17,
ITC_Comp18,
ITC_Comp19,
ITC_Comp20,
ITC_Comp21
)] string filename,
[Values(true)] bool EpsilonOnQuality,
[Values(true)] bool SpecificQual)
{
var algoname = nameof(MultiCornerPointsTest) + $"EpsOnQual_{EpsilonOnQuality}_SpecQual_{SpecificQual}_4timeinstart";
if (ConsoleToFile)
{
SetConsoleOutputToFile(@"c:\temp\multiobjective\\logs\\" + filename + "\\" + algoname + ".log");
}
var data = Data.ReadXml(dataPath + filename, "120", "4");
// var newrooms = CreateRooms(data);
var newrooms = CreateRoomsFixedSize(data, 25, 1);
data.SetRoomList(newrooms);
var mipModelParameters = new CCTModel.MIPModelParameters()
{
TuneGurobi = true,
UseStageIandII = false,
// UseStageIandII = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
ConstraintPenalty = 50,
UseRoomsAsTypes = true,
};
var solver = new MultiObjectiveSolver(data, _problemFormulation, mipModelParameters)
{
Timelimit = Timelimit,
Steps = 0,
EpsilonOnQuality = EpsilonOnQuality,
UseSpecificObjConstraints = false,
ExtraTimeOnCornerPointsMultiplier = 2,
};
Console.WriteLine("Algorithm: " + algoname);
DisplayParameterObject(_problemFormulation);
DisplayParameterObject(solver);
DisplayParameterObject(solver.MipParameters);
var pareto = solver.Run();
WriteSol(@"c:\temp\multiobjective\", filename, data, _problemFormulation, algoname, DateTime.Now.ToString(), pareto);
}
//[TestCaseSource(nameof(TestDatasetsITC2007))]
public void QualityRecovering([Values(
QualityRecoveringOptimizer.DisruptionTypes.CurriculumFourCoursesInsert,
QualityRecoveringOptimizer.DisruptionTypes.RandomRoomRandomdayRemoved,
QualityRecoveringOptimizer.DisruptionTypes.OneTimeslotUnavailable,
QualityRecoveringOptimizer.DisruptionTypes.OneAssignmentUnavailbility
)] QualityRecoveringOptimizer.DisruptionTypes disruption, [Values(
ITC_Comp01,
ITC_Comp02,
ITC_Comp03,
ITC_Comp04,
ITC_Comp05,
ITC_Comp06,
ITC_Comp07,
ITC_Comp08,
ITC_Comp09,
ITC_Comp10,
ITC_Comp11,
ITC_Comp12,
ITC_Comp13,
ITC_Comp14,
ITC_Comp15,
ITC_Comp16,
ITC_Comp17,
ITC_Comp18,
ITC_Comp19,
ITC_Comp20,
ITC_Comp21
)] string filename, [Values(1)] int seed)
{
var disruptionname = disruption;
// if (ConsoleToFile)
// SetConsoleOutputToFile(@"c:\temp\minimumpertubation\\logs\\" + filename + "\\" + disruptionname + ".log");
seed = GetRealSeed(filename, seed);
var data = Data.ReadXml(dataPath + filename, "120", "4");
var name = Path.GetFileNameWithoutExtension(filename);
var modelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = true,
};
var solutionBefore = new Solution(data, formulation);
solutionBefore.Read(dataPath + @"ITC2007\sol\" + name + "-UD2" + ".sol");
Console.WriteLine(solutionBefore.AnalyzeSolution());
var minimumPertubation = new QualityRecoveringOptimizer(data, solutionBefore, formulation, modelParameters)
{
Timelimit = 60 * 60 * 10,
ExtraPerubations = 100,
MaxTotalPertubations = 20,
SolPerPertubation = 1,
PerturbationObjectEqual = true,
};
string entitiesdisrupted;
var affected = minimumPertubation.Disrupt(disruption, seed, out entitiesdisrupted);
RunPertubationTest(seed, minimumPertubation, solutionBefore, disruptionname.ToString(), name, affected, entitiesdisrupted);
}
public void Lex_soft_cost_withTimeslot(string filename)
{
var algname = nameof(Lex_soft_cost_withTimeslot);
// formulation.AvailabilityHardConstraint = false;
var data = Data.ReadXml(dataPath + filename, "120", "4");
var newrooms = CreateRoomsFixedSize(data, 25, 5);
data.SetRoomList(newrooms);
var par = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
UseHallsConditions = true,
UseRoomHallConditions = true,
TuneGurobi = false,
UseRoomsAsTypes = false,
};
var costmodel = new CCTModel(data, ProblemFormulation.MinimizeRoomCost, par);
costmodel.Optimize();
data.SetRoomList(costmodel.GetUsedRooms().Where(kv => kv.Value > 0).Select(kv => kv.Key).ToList());
//var formulation = ProblemFormulation.UD2NoOverbook;
// formulation.AvailabilityHardConstraint = false;
// formulation.OverbookingAllowed = 0.35;
var ModelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
TuneGurobi = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
// UseRoomsAsTypes = true,
UseRoomsAsTypes = false,
RoomBoundForEachTimeSlot = true,
};
var model = new CCTModel(data, _problemFormulation, ModelParameters);
model.Optimize(300);
var userooms = model.GetUsedRooms();
Console.WriteLine($"Used rooms: {userooms.Sum(kv => kv.Value)} over {userooms.Count} ");
// model.PenalizeRoomUsedMoreThanOnce(100);
model.PenalizeRoomUsedMoreThanOnce(1, true);
model.SetObjective(0, 0, 0.001);
model.SetProximityOrigin();
model.SetQualConstraint(model.ObjSoftCons);
// model.SetProxConstraint(20);
model.Fixsol(true);
model.Optimize();
model.Fixsol(false);
model.Optimize(900);
model.DisplayObjectives();
var sol = new Solution(data, ProblemFormulation.MinimizeRoomCost);
sol.SetAssignments(model.GetAssignments());
Console.WriteLine(sol.AnalyzeSolution());
File.AppendAllText(@"c:\temp\heuristic.txt", $"{algname};{filename};{model.ObjSoftCons}\n");
}
public void MultiObjOneObjetive([Values(
ITC_Comp01,
ITC_Comp02,
ITC_Comp03,
ITC_Comp04,
ITC_Comp05,
ITC_Comp06,
ITC_Comp07,
ITC_Comp08,
ITC_Comp09,
ITC_Comp10,
ITC_Comp11,
ITC_Comp12,
ITC_Comp13,
ITC_Comp14,
ITC_Comp15,
ITC_Comp16,
ITC_Comp17,
ITC_Comp18,
ITC_Comp19,
ITC_Comp20,
ITC_Comp21
)]
string filename,
[Values(ProblemFormulation.Objective.MinimumWorkingDaysWeight,
ProblemFormulation.Objective.CurriculumCompactnessWeight,
ProblemFormulation.Objective.BadTimeslots,
ProblemFormulation.Objective.StudentMinMaxLoadWeight)]
ProblemFormulation.Objective objective)
{
//ProblemFormulation.Objective objective = ProblemFormulation.Objective.BadTimeslots;
var algoname = nameof(MultiObjOneObjetive) + "_" + Enum.GetName(objective.GetType(), objective);
if (ConsoleToFile)
{
SetConsoleOutputToFile(@"c:\temp\multiobjective\\logs\\" + filename + "\\" + algoname + ".log");
}
var data = Data.ReadXml(dataPath + filename, "120", "4");
//var newrooms = CreateRooms(data);
var newrooms = CreateRoomsFixedSize(data, 25, 1);
data.SetRoomList(newrooms);
var problemFormulation = ProblemFormulation.EverythingZero;
problemFormulation.SetWeight(objective, 1);
//problemFormulation.AvailabilityHardConstraint = false;
var mipModelParameters = new CCTModel.MIPModelParameters()
{
TuneGurobi = true,
UseStageIandII = false,
// UseStageIandII = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
//ConstraintPenalty = null, //1,
ConstraintPenalty = 50,
UseRoomsAsTypes = true,
};
var solver = new MultiObjectiveSolver(data, problemFormulation, mipModelParameters)
{
Timelimit = Timelimit,
EpsilonOnQuality = true,
EpsilonRelaxing = false,
};
Console.WriteLine("Algorithm: " + algoname);
DisplayParameterObject(problemFormulation);
DisplayParameterObject(solver);
DisplayParameterObject(solver.MipParameters);
var pareto = solver.Run();
WriteSol(@"c:\temp\multiobjective\", filename, data, problemFormulation, algoname, DateTime.Now.ToString(), pareto);
}
public void MultiObjectiveRoomStabilityFixedTime(string filename)
{
var algoname = nameof(MultiObjectiveRoomStabilityFixedTime);
if (ConsoleToFile)
{
SetConsoleOutputToFile(@"c:\temp\multiobjective\\logs\\" + filename + "\\" + algoname + ".log");
}
var data = Data.ReadXml(dataPath + filename, "120", "4");
var newrooms = CreateRoomsFixedSize(data, 25, 3);
data.SetRoomList(newrooms);
var formulation = ProblemFormulation.MinimizeRoomCost;
// formulation.OverbookingAllowed = 0.25;
var startmodel = new CCTModel(data, formulation, new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
TuneGurobi = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
});
startmodel.Optimize(300);
var fixedTimeSlotAssignments = startmodel.GetAssignments();
var stageIsol = new Solution(data, formulation);
stageIsol.SetAssignments(fixedTimeSlotAssignments);
Console.WriteLine("Assignments of timeslots:");
Console.WriteLine(stageIsol.AnalyzeSolution());
// var newrooms = CreateRooms(data,5);
// var newrooms = CreateRoomsFixedSize(data, 20);fr
var problemFormulation = ProblemFormulation.EverythingZero;
problemFormulation.RoomStabilityWeight = 1;
//problemFormulation.OverbookingAllowed = 1;
var mipModelParameters = new CCTModel.MIPModelParameters()
{
TuneGurobi = true,
UseStageIandII = true,
// UseStageIandII = true,
UseHallsConditions = false,
UseRoomHallConditions = false,
NSols = 100,
ConstraintPenalty = 50,
UseRoomsAsTypes = false,
};
var solver = new MultiObjectiveSolver(data, problemFormulation, mipModelParameters)
{
Timelimit = Timelimit,
MIPGap = 0.0,
Steps = 10,
LocalBranching = 0,
EpsilonOnQuality = false,
FixedTimeSlotAssignments = fixedTimeSlotAssignments
};
Console.WriteLine("Algorithm: " + algoname);
DisplayParameterObject(problemFormulation);
DisplayParameterObject(solver);
DisplayParameterObject(solver.MipParameters);
var pareto = solver.Run();
WriteSol(@"c:\temp\multiobjective\", filename, data, problemFormulation, algoname, DateTime.Now.ToString(), pareto);
}
// [TestCaseSource(nameof(TestMultiObjTestBed))]
public void MultiObjSolveWeighted([Values(
ITC_Comp01,
ITC_Comp02,
ITC_Comp03,
ITC_Comp04,
ITC_Comp05,
ITC_Comp06,
ITC_Comp07,
ITC_Comp08,
ITC_Comp09,
ITC_Comp10,
ITC_Comp11,
ITC_Comp12,
ITC_Comp13,
ITC_Comp14,
ITC_Comp15,
ITC_Comp16,
ITC_Comp17,
ITC_Comp18,
ITC_Comp19,
ITC_Comp20,
ITC_Comp21)] string filename,
[Values(false)] bool classSizeWeightedPenalty)
{
var algoname = nameof(MultiObjSolveEpsilon) + $"Weighted_classsizepenalty_{classSizeWeightedPenalty}";
if (ConsoleToFile)
{
SetConsoleOutputToFile(@"c:\temp\multiobjective\\logs\\" + filename + "\\" + algoname + ".log");
}
var data = Data.ReadXml(dataPath + filename, "120", "4");
// var newrooms = CreateRooms(data);
var newrooms = CreateRoomsFixedSize(data, 25, 1);
data.SetRoomList(newrooms);
var mipModelParameters = new CCTModel.MIPModelParameters()
{
TuneGurobi = true,
UseStageIandII = false,
// UseStageIandII = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
ConstraintPenalty = 50,
UseRoomsAsTypes = true,
ScalePenaltyWithCourseSize = classSizeWeightedPenalty,
};
var solver = new MultiObjectiveSolver(data, _problemFormulation, mipModelParameters)
{
Timelimit = Timelimit,
UseEpsilonMethod = false,
ExtraTimeOnCornerPointsMultiplier = 2,
DoubleSweep = false,
};
Console.WriteLine("Algorithm: " + algoname);
DisplayParameterObject(_problemFormulation);
DisplayParameterObject(solver);
DisplayParameterObject(solver.MipParameters);
var pareto = solver.Run();
WriteSol(@"c:\temp\multiobjective\", filename, data, _problemFormulation, algoname, DateTime.Now.ToString(), pareto);
}
public void TopSolutionsAnalysis(string filename)
{
var name = Path.GetFileNameWithoutExtension(filename);
var data = Data.ReadXml(dataPath + filename, "120", "4");
var formulation = ProblemFormulation.UD2NoOverbook;
var courestime = new Dictionary <Course, Dictionary <TimeSlot, int> >();
var penaltyforcourse = new Dictionary <Course, int>();
foreach (var course in data.Courses)
{
courestime.Add(course, new Dictionary <TimeSlot, int>());
foreach (var timeSlot in data.TimeSlots)
{
courestime[course].Add(timeSlot, 0);
}
penaltyforcourse[course] = 0;
}
var bestsol = new List <Assignment>();
var penMWD = new List <int>();
var penCC = new List <int>();
for (var i = 0; i < 10; i++)
{
var sol = new Solution(data, formulation);
sol.Read(dataPath + @"ITC2007\sol\" + name + "-UD2-" + i + ".sol");
sol.AnalyzeSolution();
foreach (var assignment in sol._assignments)
{
courestime[assignment.Course][assignment.TimeSlot]++;
}
foreach (var course in data.Courses)
{
penaltyforcourse[course] += sol.PenaltyForCourse[course];
}
penMWD.Add((int)sol.MinimumWorkingDays);
penCC.Add((int)sol.CurriculumCompactness);
if (i == 0)
{
bestsol = sol._assignments.ToList();
}
}
const int minhint = 7;
var maxhint = 7;
var noise = 0.5;
const bool fixhints = true;
var random = new Random(62);
foreach (var course in data.Courses)
{
foreach (var ts in data.TimeSlots)
{
// if (random.Next(10) < courestime[course][ts]) courestime[course][ts] = 1;
// else courestime[course][ts] = 0;
if (courestime[course][ts] < minhint)
{
courestime[course][ts] = 0;
}
if (courestime[course][ts] > maxhint)
{
courestime[course][ts] = 0;
}
//result should be made to double.
// if(courestime[course][ts] > 0) courestime[course][ts] += random.Next(-3,3);
//courestime[course][ts] *= 1 + (int) Math.Round(noise*(2*random.NextDouble()-1));
}
}
var hints = courestime.SelectMany(c => c.Value.Where(cv => cv.Value > 0).Select(cv => Tuple.Create(c.Key, cv.Key, cv.Value))).ToList();
var currtime = new Dictionary <Curriculum, Dictionary <TimeSlot, int> >();
foreach (var curr in data.Curricula)
{
currtime.Add(curr, new Dictionary <TimeSlot, int>());
foreach (var timeSlot in data.TimeSlots)
{
currtime[curr].Add(timeSlot, courestime.Where(c => curr.Courses.Contains(c.Key)).Sum(kv => kv.Value[timeSlot]));
}
}
var chints = currtime.SelectMany(c => c.Value.Select(cv => Tuple.Create(c.Key, cv.Key, cv.Value))).ToList();
Console.WriteLine($"Timeslots: {data.TimeSlots.Count}");
Console.WriteLine($"MWD: avg: {penMWD.Average()} - {string.Join(", ", penMWD)} ");
Console.WriteLine($"CC: avg: {penCC.Average()} - {string.Join(", ", penCC)} ");
foreach (var course in data.Courses.OrderByDescending(c => penaltyforcourse[c]))
{
Console.WriteLine($"\nCourse: {course}\n" +
$"Size: {course.NumberOfStudents}\n" +
$"Lectures: {course.Lectures}\n" +
$"MWD: {course.MinimumWorkingDays}\n" +
$"TSAvaialble:{data.TimeSlots.Count - course.UnavailableTimeSlots.Count}\n" +
$"Unavail: {string.Join(",", course.UnavailableTimeSlots)}\n" +
$"TotalPenalty: {penaltyforcourse[course]}");
foreach (var valuePair in courestime[course].Where(kv => kv.Value > 0).OrderByDescending(kv => kv.Value))
{
Console.WriteLine(valuePair.Key + ": " + valuePair.Value);
}
}
Console.WriteLine("\n\nBy prio: ");
foreach (var hint in hints.Where(h => h.Item3 > 0).OrderByDescending(h => h.Item3))
{
Console.WriteLine(hint.Item1.ID + "_" + hint.Item2 + ": " + hint.Item3);
}
Console.WriteLine("\n\nHistogram: ");
Console.WriteLine("total lectures: " + data.Courses.Sum(c => c.Lectures));
for (var i = 1; i <= 10; i++)
{
Console.WriteLine($"{i}\t{hints.Count(h => h.Item3 == i)}");
}
Console.WriteLine("Unavailabilites\tSame");
foreach (var course in data.Courses)
{
Console.WriteLine($"{course.UnavailableTimeSlots.Count}\t{(double) courestime[course].Sum(t => t.Value)/course.Lectures : 0.00}");
}
Console.WriteLine("Penal\tSame");
foreach (var course in data.Courses)
{
Console.WriteLine($"{penaltyforcourse[course]}\t{(double) courestime[course].Sum(t => t.Value)/course.Lectures : 0.00}");
}
Console.WriteLine("Size\tSame");
foreach (var course in data.Courses)
{
Console.WriteLine($"{course.NumberOfStudents}\t{(double) courestime[course].Sum(t => t.Value)/course.Lectures : 0.00}");
}
// return;
//Console.WriteLine("\n\nCurriculumPatterns");
//foreach (var hint in chints.Where(h => h.Item3 > 0).OrderByDescending(h => h.Item3))
//{
// Console.WriteLine(hint.Item1.ID + "_" + hint.Item2 + ": " + hint.Item3);
//}
//Console.WriteLine("\n\nHistogram: ");
//Console.WriteLine("total curr_lectures: " + data.Curricula.Sum(c => c.Lectures));
//for (var i = 1; i <= 10; i++)
//{
// Console.WriteLine($"{i}\t{chints.Count(h => h.Item3 == i)}");
//}
var ModelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = false,
TuneGurobi = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
// Seed = seeds[i],
};
var model = new CCTModel(data, formulation, ModelParameters);
// model.SetMipHints(chints);
Console.WriteLine($"Adding {hints.Count} hints. Total letures: {data.Courses.Sum(c => c.Lectures)} ({(double) hints.Count / data.Courses.Sum(c => c.Lectures) :0.00%})");
model.SetMipHints(hints, fixhints);
// model.MipStart(bestsol);
var r = model.Optimize(300);
// model.Optimize(60*60*3);
var ass = model.GetAssignments();
var finalsol = new Solution(data, formulation);
finalsol.SetAssignments(ass);
finalsol.Save(@"C:\temp\finalsolStageI_" + name + ".sol");
var ModelParameters2 = new CCTModel.MIPModelParameters()
{
UseStageIandII = true,
TuneGurobi = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
// Seed = seeds[i],
};
var model2 = new CCTModel(data, formulation, ModelParameters2);
model2.SetAndFixSolution(ass);
model2.Optimize(300);
ass = model2.GetAssignments();
finalsol.SetAssignments(ass);
finalsol.Save(@"C:\temp\finalsolStageII_" + name + ".sol");
}
// [TestCase(ITC_Comp05,1)]
/*
* [TestCase(ITC_Comp05, 2)]
* [TestCase(ITC_Comp05, 3)]
* [TestCase(ITC_Comp05, 4)]
* [TestCase(ITC_Comp05, 5)]
* [TestCase(ITC_Comp05, 6)]
* [TestCase(ITC_Comp05, 7)]
* [TestCase(ITC_Comp05, 8)]
* [TestCase(ITC_Comp05, 11)]
* [TestCase(ITC_Comp05, 12)]
* [TestCase(ITC_Comp05, 13)]
* [TestCase(ITC_Comp05, 14)]
* [TestCase(ITC_Comp05, 15)]
* [TestCase(ITC_Comp05, 16)]
* [TestCase(ITC_Comp05, 17)]
*/
public void CCfirstHeuristic30s(string filename, int seed) //
{
// var seed = 0;
for (int ia = 1; ia < 5; ia++)
{
seed = ia;
var data = Data.ReadXml(dataPath + filename, "120", "4");
// var newrooms = CreateRooms(data);
// data.SetRoomList(newrooms);
var problemFormulation = ProblemFormulation.UD2NoOverbook;
problemFormulation.MinimumWorkingDaysWeight = 0;
// problemFormulation.CurriculumCompactnessWeight = 0;
var par = new CCTModel.MIPModelParameters()
{
TuneGurobi = true,
UseStageIandII = false,
UseHallsConditions = true,
UseRoomHallConditions = true,
UseRoomsAsTypes = false,
Seed = seed,
};
var model = new CCTModel(data, problemFormulation, par);
model.SetObjective(1, 0, 0);
model.Optimize(TimelimitStageI);
model.DisplayObjectives();
var result = new Dictionary <double, double>();
//maybe find better proximity that reflects currciulumcompactness better.
//Check which curriculum that can be moved without introducting penalties.
model.SetProximityOrigin(true);
problemFormulation.MinimumWorkingDaysWeight = 5;
for (var i = 15; i <= 15; i++)
{
// model.SetObjective(0, 0, i);
model.SetProxConstraint(i);
model.AddMinimumWorkingDaysCost();
// model.FixCurricula(true);
model.Optimize();
model.DisplayObjectives();
model.SetObjective(1, 0, 0);
model.Fixsol(true);
model.Optimize();
model.Fixsol(false);
model.DisplayObjectives();
result[i] = model.Objective;
}
Console.WriteLine("Results:");
Console.WriteLine("i;obj");
foreach (var r in result)
{
Console.WriteLine($"{r.Key};{r.Value}");
}
var best = result.Min(r => r.Value);
File.AppendAllText(@"c:\temp\heuristic.txt", $"{nameof(CCfirstHeuristic30s)};{filename};{best}\n");
// model.FixCurricula(false);
// maybe also save multiple minimum curriculum solutions
}
}
// [TestCaseSource(nameof(TestMultiObjTestBed))]
public void MultiObjSolveEpsilon([Values(
ITC_Comp01,
ITC_Comp02,
ITC_Comp03,
ITC_Comp04,
ITC_Comp05,
ITC_Comp06,
ITC_Comp07,
ITC_Comp08,
ITC_Comp09,
ITC_Comp10,
ITC_Comp11,
ITC_Comp12,
ITC_Comp13,
ITC_Comp14,
ITC_Comp15,
ITC_Comp16,
ITC_Comp17,
ITC_Comp18,
ITC_Comp19,
ITC_Comp20,
ITC_Comp21
)] string filename,
[Values(false, true)] bool EpsilonOnQuality,
[Values(false)] bool EpsilonRelaxing,
[Values(false)] bool DoubleSweep)
{
var algoname = nameof(MultiObjSolveEpsilon) + $"EpsOnQual_{EpsilonOnQuality}_EpsRelax_{EpsilonRelaxing}_doublesweep_{DoubleSweep}";
if (ConsoleToFile)
{
SetConsoleOutputToFile(@"c:\temp\multiobjective\\logs\\" + filename + "\\" + algoname + ".log");
}
var data = Data.ReadXml(dataPath + filename, "120", "4");
// var newrooms = CreateRooms(data);
var newrooms = CreateRoomsFixedSize(data, 25, 1);
data.SetRoomList(newrooms);
var mipModelParameters = new CCTModel.MIPModelParameters()
{
TuneGurobi = true,
UseStageIandII = false,
// UseStageIandII = true,
UseHallsConditions = true,
UseRoomHallConditions = true,
ConstraintPenalty = 50,
UseRoomsAsTypes = true,
ScalePenaltyWithCourseSize = false,
};
var solver = new MultiObjectiveSolver(data, _problemFormulation, mipModelParameters)
{
Timelimit = (DoubleSweep ? Timelimit / 2 : Timelimit),
EpsilonOnQuality = EpsilonOnQuality,
EpsilonRelaxing = EpsilonRelaxing,
ExtraTimeOnCornerPointsMultiplier = 2,
DoubleSweep = DoubleSweep,
Steps = 10, //TODO: only zero steps
};
Console.WriteLine("Algorithm: " + algoname);
DisplayParameterObject(_problemFormulation);
DisplayParameterObject(solver);
DisplayParameterObject(solver.MipParameters);
var pareto = solver.Run();
WriteSol(@"c:\temp\multiobjective\", filename, data, _problemFormulation, algoname, DateTime.Now.ToString(), pareto);
}
//[TestCaseSource(nameof(TestDatasetsITC2007))]
public void MinimumPertubationsAssignments([Values(
ITC_Comp01, ITC_Comp05, ITC_Comp10, ITC_Comp12, ITC_Comp14, ITC_Comp18
/*
* ITC_Comp01,
* ITC_Comp02,
* ITC_Comp03,
* ITC_Comp04,
* ITC_Comp05,
* ITC_Comp06,
* ITC_Comp07,
* ITC_Comp08,
* ITC_Comp09,
* ITC_Comp10,
* ITC_Comp11,
* ITC_Comp12,
* ITC_Comp13,
* ITC_Comp14,
* ITC_Comp15,
* ITC_Comp16,
* ITC_Comp17,
* ITC_Comp18,
* ITC_Comp19,
* ITC_Comp20,
* ITC_Comp21*/
)] string filename, [Values(1)] int seed)
{
var data = Data.ReadXml(dataPath + filename, "120", "4");
var name = Path.GetFileNameWithoutExtension(filename);
var modelParameters = new CCTModel.MIPModelParameters()
{
UseStageIandII = true,
};
var solutionBefore = new Solution(data, formulation);
solutionBefore.Read(dataPath + @"ITC2007\sol\" + name + "-UD2" + ".sol");
solutionBefore.AnalyzeSolution();
for (var n = 0; n < 100; n++)
{
if (n > solutionBefore._assignments.Count)
{
break;
}
var disruptionname = nameof(MinimumPertubationsAssignments) + n;
var minimumPertubation = new QualityRecoveringOptimizer(data, solutionBefore, formulation, modelParameters)
{
Timelimit = 60 * 60,
ExtraPerubations = 0,
SolPerPertubation = 1
};
var affected = minimumPertubation.DisruptAssignmentUnavailable(seed, n);
RunPertubationTest(seed, minimumPertubation, solutionBefore, disruptionname, name, affected, "");
}
}