public void TestSolveComp05ToOptimalityUD2()
{
var data = Data.ReadXml(dataPath + ITC_Comp05, "120", "4");
var formulation = ProblemFormulation.UD2;
var sol = new Solution(data, formulation);
sol.Read(dataPath + @"ITC2007\comp05-UD2.sol");
var model = new CCTModel(data, formulation);
model.ModelParameters.SaveBoundInformation = true;
model.MipStart(sol._assignments.ToList());
//model.WriteModel(@"c:\temp\model.lp");
Console.WriteLine("Solving");
model.Optimize(300);
sol.SetAssignments(model.GetAssignments());
Console.WriteLine(sol.AnalyzeSolution());
Assert.IsTrue(sol.IsFeasible, "solution should be feasible");
// Assert.AreEqual(0,sol.Objective);
Console.WriteLine("MWD;CC;obj;bnd");
Console.WriteLine(String.Join("\n", model.bounds.Select(t => $"{t.Item1};{t.Item2};{t.Item3};{t.Item4}")));
}
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 => $"{t.Item1};{t.Item2};{t.Item3};{t.Item4}")));
Console.WriteLine(sol.AnalyzeSolution());
}
public void TestRoomUnsuitabilityConstraint()
{
var data = Data.ReadXml(dataPath + ITC_Comp05, "120", "4");
//var formulation = ProblemFormulation.UD2;
var formulation = ProblemFormulation.UD2;
formulation.UnsuitableRoomsWeight = 1;
formulation.BadTimeslotsWeight = 1;
// formulation.StudentMinMaxLoadWeight = 1;
// formulation.OverbookingAllowed = 0.15;
var model = new CCTModel(data, formulation);
Console.WriteLine("Solving");
model.WriteModel(@"c:\temp\model.lp");
model.Optimize(900);
model.WriteModel(@"c:\temp\solution.sol");
var sol = new Solution(data, formulation);
sol.SetAssignments(model.GetAssignments());
Console.WriteLine(sol.AnalyzeSolution());
Assert.IsTrue(sol.IsFeasible, "solution should be feasible");
// Assert.AreEqual(5, sol.Objective);
}
private void RunWieghtedSumMetod(int minSoftConsViol, int maxSoftConsViol, double minCost, double maxCost)
{
List <MultiResult> pareto;
var queue = new Queue <Tuple <int, int, int, int> >();
queue.Enqueue(Tuple.Create(minSoftConsViol, maxSoftConsViol, (int)minCost, (int)maxCost));
for (int i = 0; i < Steps; i++)
{
if (queue.Count == 0)
{
Console.WriteLine("Queue empty.. breaking");
break;
}
var point = queue.Dequeue();
Console.WriteLine($"Point: {point}");
var softconstweight = point.Item4 - point.Item3;
var costweight = point.Item2 - point.Item1;
if (costweight < 0.1 || softconstweight < 0.1)
{
Console.WriteLine("no slope.. skipping");
continue;
}
_model.SetObjective(softconstweight, costweight);
Console.WriteLine($"Slope set to: {(double) softconstweight/costweight : #.##}");
var solfound = _model.Optimize(Timelimit, MIPGap);
Console.WriteLine($"LastSolution:\n(cost,softobj) = {_model.ObjCost} , {_model.ObjSoftCons}");
if (solfound)
{
queue.Enqueue(Tuple.Create(point.Item1, _model.ObjSoftCons, (int)_model.ObjCost, point.Item4));
queue.Enqueue(Tuple.Create(_model.ObjSoftCons, point.Item2, point.Item3, (int)_model.ObjCost));
// Maybe remove dominated solutions.
// _model.ObjBound <= cost*costweight + softobj*softobjweigh
var costBound = (int)Math.Floor(_model.ObjBound / costweight);
var softobjBound = (int)Math.Floor(_model.ObjBound / softconstweight);
_multiResults.Add(new MultiResult(_model.ObjCost, _model.ObjSoftCons, costBound, softobjBound,
_model.GetUsedRooms(),
_model.GetAssignments(), null, null, (int)_stopwatch.Elapsed.TotalSeconds));
}
AddAllSolutions(_model);
}
}
public Solution SolveStageII()
{
var model = new CCTModel(data, formulation, new CCTModel.MIPModelParameters()
{
UseStageIandII = true,
});
model.SetAndFixSolution(_solution._assignments.ToList());
//model.MipStart(_solution._assignments.ToList());
model.Optimize(1200);
_solution.SetAssignments(model.GetAssignments());
return(_solution);
}
public Solution RunLargeCoursesFirst()
{
var stopwatch = Stopwatch.StartNew();
var cutlimit = 0.1;
var ncourses = 10;//(int) 0.1*data.Courses.Count;
var newcourses = data.Courses.OrderByDescending(c => c.NumberOfStudents).Take(ncourses).ToList();
var remcourse = data.Courses.Except(newcourses).ToList();
var newdata = new Data(data.Courses, data.Lecturers, data.Rooms, data.Curricula, data.TimeSlots, data.Days, data.Periods, data.MinimumPeriodsPerDay, data.MaximumPeriodsPerDay);
newdata.CleanRemovedCourses(remcourse);
Console.WriteLine($"Solving with {newcourses.Count} ({(double) newcourses.Count/data.Courses.Count : 0.00%})");
var modelpre = new CCTModel(newdata, formulation, MIPmodelParameters);
modelpre.Optimize();
var ass = modelpre.GetAssignments();
var model = new CCTModel(data, formulation, MIPmodelParameters);
// model.SetAndFixSolution(ass);
model.Optimize();
// model.Optimize(timetotighten+50,cutoff:500);
// model.ModelParameters.AbortSolverOnZeroPenalty = true;
var tighttimer = Stopwatch.StartNew();
model.Optimize(Timetotighten + 900);
tighttimer.Stop();
model.DisplayObjectives();
_solution = new Solution(data, formulation);
_solution.SetAssignments(model.GetAssignments());
Console.WriteLine($"Unavailableused: {model.GetUnavailableused()} ({(double)model.GetUnavailableused() / data.Courses.Sum(c => c.Lectures):0.00%})");
//timers
stopwatch.Stop();
tighttimer.Stop();
totalseconds = (int)stopwatch.Elapsed.TotalSeconds;
roomtightseconds = (int)tighttimer.Elapsed.TotalSeconds;
return(_solution);
}
public Solution Run()
{
var stopwatch = Stopwatch.StartNew();
var model = new CCTModel(data, formulation, MIPmodelParameters);
model.Optimize(TimelimitStageI - Timetotighten);
var userooms = model.GetUsedRooms();
Console.WriteLine($"Used rooms: {userooms.Sum(kv => kv.Value)} over {userooms.Count} ");
Console.WriteLine($"OveruseOnTimeslots: {model.GetOverUseOfRooms()} ");
// model.PenalizeRoomUsedMoreThanOnce(100);
model.PenalizeRoomUsedMoreThanOnce(10, true);
model.SetObjective(0, 0, 0.01);
//model.SetObjective(0, 0, 0);
//model.SetMipHintToCurrent();
model.SetProximityOrigin();
model.SetQualConstraint(model.ObjSoftCons);
model.Fixsol(true);
model.Optimize();
model.Fixsol(false);
// model.Optimize(timetotighten+50,cutoff:500);
model.ModelParameters.AbortSolverOnZeroPenalty = true;
var tighttimer = Stopwatch.StartNew();
model.Optimize(Timetotighten + 300);
tighttimer.Stop();
model.DisplayObjectives();
_solution = new Solution(data, formulation);
_solution.SetAssignments(model.GetAssignments());
//timers
stopwatch.Stop();
tighttimer.Stop();
totalseconds = (int)stopwatch.Elapsed.TotalSeconds;
roomtightseconds = (int)tighttimer.Elapsed.TotalSeconds;
return(_solution);
}
public void TestSolve()
{
var data = Data.ReadXml(dataPath + ITC_Comp04, "120", "4");
var formulation = ProblemFormulation.UD2;
formulation.StudentMinMaxLoadWeight = 1;
formulation.RoomStabilityWeight = 0;
formulation.OverbookingAllowed = 0.5;
var model = new CCTModel(data, formulation);
//model.WriteModel(@"c:\temp\model.lp");
Console.WriteLine("Solving");
model.Optimize(300, 0.40);
// model.DisplaySoftConstraintViolations();
var sol = new Solution(data, formulation);
sol.SetAssignments(model.GetAssignments());
Console.WriteLine(sol.AnalyzeSolution());
sol.Save(@"c:\temp\solution.ctt");
}
public Solution RunUnavailbility()
{
var stopwatch = Stopwatch.StartNew();
var model = new CCTModel(data, formulation, MIPmodelParameters);
model.Optimize(TimelimitStageI - Timetotighten);
Console.WriteLine($"Unavailableused: {model.GetUnavailableused()} ({(double) model.GetUnavailableused()/data.Courses.Sum(c => c.Lectures):0.00%})");
model.PenalizeUnavailability(10);
// model.SetObjective(0, 0, 0.01);
// model.SetObjective(0, 0, 0);
//model.SetMipHintToCurrent();
model.SetProximityOrigin();
// model.SetQualConstraint(model.ObjSoftCons);
model.Fixsol(true);
model.Optimize();
model.Fixsol(false);
// model.Optimize(timetotighten+50,cutoff:500);
// model.ModelParameters.AbortSolverOnZeroPenalty = true;
var tighttimer = Stopwatch.StartNew();
model.Optimize(Timetotighten + 900);
tighttimer.Stop();
model.DisplayObjectives();
_solution = new Solution(data, formulation);
_solution.SetAssignments(model.GetAssignments());
Console.WriteLine($"Unavailableused: {model.GetUnavailableused()} ({(double)model.GetUnavailableused() / data.Courses.Sum(c => c.Lectures):0.00%})");
//timers
stopwatch.Stop();
tighttimer.Stop();
totalseconds = (int)stopwatch.Elapsed.TotalSeconds;
roomtightseconds = (int)tighttimer.Elapsed.TotalSeconds;
return(_solution);
}
public void TestSolveComp01ToOptimalityUD2()
{
var data = Data.ReadXml(dataPath + ITC_Comp01, "120", "4");
var formulation = ProblemFormulation.UD2;
var model = new CCTModel(data, formulation);
Console.WriteLine("Solving");
model.WriteModel(@"c:\temp\model.lp");
model.Optimize();
model.WriteModel(@"c:\temp\solution.sol");
var sol = new Solution(data, formulation);
sol.SetAssignments(model.GetAssignments());
Console.WriteLine(sol.AnalyzeSolution());
Assert.IsTrue(sol.IsFeasible, "solution should be feasible");
Assert.AreEqual(5, sol.Objective);
}
public List <Tuple <int, Solution, int> > Run()
{
var before = (int)solutionBefore.Objective;
model.SetObjective(0, 0, 1);
//model.Fixsol(false);
//model.SetProxConstraint(10);
var feasible = model.Optimize(Timelimit);
if (!feasible)
{
return(new List <Tuple <int, Solution, int> >());
}
var minperb = (int)model.Objective;
//Find best objective
model.SetObjective(1, 0, 0);
var sol = new List <Tuple <int, Solution, int> >();
var currentObjective = 0;
for (var pertubations = minperb; pertubations <= minperb + ExtraPerubations && pertubations <= MaxTotalPertubations; pertubations++)
{
Console.WriteLine($"pertubations = {pertubations}");
model.SetProxConstraint(pertubations);
if (PerturbationObjectEqual)
{
model.SetProcConstraintSenseEqual(true);
}
// if (RemoveCurrentSolutionAfteritt) model.b
//model.Optimize();
int sols;
for (sols = 0; sols < SolPerPertubation; sols++)
{
if (!model.Optimize(Timelimit))
{
if (AbortSolverWhenNoImprovement)
{
break; //infeasibles
}
else
{
continue;
}
}
Console.WriteLine("new solution");
//constraint on objective function
// if (model.ObjSoftCons == prev) break;
currentObjective = model.ObjSoftCons;
model.SetQualConstraint(currentObjective);
var solututionAfter = new Solution(data, formulation);
solututionAfter.SetAssignments(model.GetAssignments());
Console.WriteLine($"Before: {before}\n" +
$"After: {model.ObjSoftCons}\n" +
$"Perbs: {pertubations}");
Console.WriteLine(solutionBefore.AnalyzeSolution());
Console.WriteLine(solututionAfter.AnalyzeSolution());
var solution = new Solution(data, formulation);
solution.SetAssignments(model.GetAssignments());
solution.AnalyzeSolution();
sol.Add(Tuple.Create(pertubations, solution, model.RunTime));
DisplayPertubations(solutionBefore._assignments.ToList(), solututionAfter._assignments.ToList());
//to pertubate more.
model.RemoveCurrentSolution();
}
if (sols == 0)
{
break; //previous pertubations didnt find anything
}
model.SetQualConstraint(currentObjective - 1);
}
return(sol);
}
public List <Tuple <int, Solution, int, int> > Run()
{
var starttime = DateTime.Now;
var before = (int)solutionBefore.Objective;
model.SetObjective(0, 0, 1);
//model.Fixsol(false);
//model.SetProxConstraint(10);
var feasible = model.Optimize(Timelimit);
if (!feasible)
{
return(new List <Tuple <int, Solution, int, int> >());
}
var minperb = (int)model.Objective;
//Find best objective
model.SetObjective(1, 0, 0);
var sol = new List <Tuple <int, Solution, int, int> >();
var currentObjective = 0;
var maxPerturbations = Math.Max(minperb + ExtraPerubations, MaxTotalPertubations);
for (var pertubations = minperb; pertubations <= maxPerturbations; pertubations++)
{
Console.WriteLine($"pertubations = {pertubations}");
model.SetProxConstraint(pertubations);
if (PerturbationObjectEqual)
{
model.SetProcConstraintSenseEqual(true);
}
// if (RemoveCurrentSolutionAfteritt) model.b
//model.Optimize();
int sols;
for (sols = 0; sols < SolPerPertubation; sols++)
{
var timeleft = TotalTimelimit - (int)(DateTime.Now - starttime).TotalSeconds;
if (!model.Optimize(timeleft))
{
if (AbortSolverWhenNoImprovement)
{
break; //infeasibles
}
else
{
continue;
}
}
Console.WriteLine("new solution");
//constraint on objective function
// if (model.ObjSoftCons == prev) break;
currentObjective = model.ObjSoftCons;
model.SetQualConstraint(currentObjective);
var solututionAfter = new Solution(data, formulation);
solututionAfter.SetAssignments(model.GetAssignments());
Console.WriteLine($"Before: {before}\n" +
$"After: {model.ObjSoftCons}\n" +
$"Perbs: {pertubations}");
Console.WriteLine(solutionBefore.AnalyzeSolution());
Console.WriteLine(solututionAfter.AnalyzeSolution());
var solution = new Solution(data, formulation);
solution.SetAssignments(model.GetAssignments());
solution.AnalyzeSolution();
sol.Add(Tuple.Create(pertubations, solution, model.RunTime, (int)Math.Ceiling(model.ObjBound)));
DisplayPertubations(solutionBefore._assignments.ToList(), solututionAfter._assignments.ToList());
//to pertubate more.
model.RemoveCurrentSolution();
}
if (sols == 0)
{
break; //previous pertubations didnt find anything
}
if (AbortWhenPreviousSolutionValueIsObtained && currentObjective == (int)solutionBefore.Objective)
{
break;
}
if ((int)(DateTime.Now - starttime).TotalSeconds > TotalTimelimit)
{
Console.WriteLine("total timelimit reached");
break;
}
//overall timelimit?
model.SetQualConstraint(currentObjective - 1);
}
LastRuntimeSeconds = (int)(DateTime.Now - starttime).TotalSeconds;
return(sol);
}
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");
}
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);
}
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");
}
