/// <summary>
/// Find a new pattern (use slave model to calc size contents of new pattern)
/// </summary>
/// <returns>True if found a new pattern, otherwise false</returns>
public bool FindNewPattern()
{
//try to find a new pattern which will eventually result in less total rolls
SetCurrentToSlaveModel();
Solution sol = _context.Solve();
Goal objective = sol.Goals.First();
Decision newPattern = sol.Decisions.First();
if (sol.Quality == SolverQuality.Optimal && objective.ToDouble() < _objLimit)
{
Console.WriteLine("\nFound a new {0}th pattern (based on prev shadow prices)", _patternCount + 1);
//Console.WriteLine(" ({0})",
// string.Join(", ",_shadowPrices.Select(pair => string.Format("{0}=>{1}",pair.Key, pair.Value)))
//);
foreach (Object[] patternRoll in newPattern.GetValues())
{
_patternRolls.Add(new PatternItemSize(id: _patternCount, width: Convert.ToInt32(patternRoll[1]), count: Convert.ToInt32(patternRoll[0])));
}
_patterns.Add(new CuttingPattern()
{
PatternID = _patternCount, Count = 0
});
_patternCount++;
return(true);
}
else
{
return(false);
}
}
private void button2_Click(object sender, EventArgs e)
{
Decision mixedStrategy = SBFollower.SBFF();
listBox2.Items.Clear();
listBox2.Items.Add(" ");
foreach (object[] value in mixedStrategy.GetValues())
{
listBox2.Items.Add(value[1].ToString() + " : " + ((double)value[0]).ToString("P2"));
listBox2.Items.Add("-------------------------------------------------");
}
}
private IDictionary <int, double> GetStartTimesByTask()
{
// The "start" decision has the start times for each *event*. Index 0 has the start and index 1 has the event.
// Recall that the event set was created using Rationals, so we must convert to int here.
var eventToStart = start.GetValues().ToDictionary(o => int.Parse(o[1].ToString()), o => (double)o[0]);
// Group the isActive decision by task.
var isActiveByTask = isActive.GetValues().GroupBy(o => o[1]);
// Find the first event where each task is active, and transform the output into (task, event) pairs.
var firstActiveByTask = isActiveByTask
.Select(g => g.First(a => (double)a[0] > 0.5))
.Select(o => new { TaskID = (int)((double)o[1]), EventID = int.Parse(o[2].ToString()) });
// Now we can use the (task, event) pairs to get a dictionary that maps task IDs to start dates.
return(firstActiveByTask.ToDictionary(f => f.TaskID, f => eventToStart[f.EventID]));
}
public static Tuple<ICollection<SubCalendarEvent>, double> Run(ICollection<SubCalendarEvent> ListOfElements, int BeginningAndEnd=0)
{
#if EnableHive
if (ListOfElements.Count < 3)
{
return new Tuple<ICollection<SubCalendarEvent>, double>(ListOfElements, 0);
}
CitiesData citiesData = new CitiesData(ListOfElements.ToList());
int totalNumberBees = 100;
int numberInactive = 20;
int numberActive = 50;
int numberScout = 30;
int maxNumberVisits = 50;
int maxNumberCycles = 20;
Hive hive = new Hive(totalNumberBees, numberInactive, numberActive, numberScout, maxNumberVisits, maxNumberCycles, citiesData, BeginningAndEnd);
bool doProgressBar = false;
hive.Solve(doProgressBar);
return hive.getBestPath();
#endif
#if LinearTSP
Coordinate[] data = new Coordinate[ListOfElements.Count];
Dictionary<int, SubCalendarEvent> DictOFData = new Dictionary<int,SubCalendarEvent>();
int NameIndex = 0;
foreach (SubCalendarEvent eachSubCalendarEvent in ListOfElements)
{
data[NameIndex] = new Coordinate(NameIndex, eachSubCalendarEvent);
DictOFData.Add(NameIndex++, eachSubCalendarEvent);
}
SolverContext context = SolverContext.GetContext();
Model model = context.CreateModel();
// ------------
// Parameters
Set city = new Set(Domain.IntegerNonnegative, "city");
Parameter dist = new Parameter(Domain.Real, "dist", city, city);
var arcs = from p1 in data
from p2 in data
select new Arc { City1 = p1.Name, City2 = p2.Name, Distance = p1.Distance(p2, data.Length) };
dist.SetBinding(arcs, "Distance", "City1", "City2");
model.AddParameters(dist);
// ------------
// Decisions
Decision assign = new Decision(Domain.IntegerRange(0, 1), "assign", city, city);
Decision rank = new Decision(Domain.RealNonnegative, "rank", city);
model.AddDecisions(assign, rank);
// ------------
// Goal: minimize the length of the tour.
Goal goal = model.AddGoal("TourLength", GoalKind.Minimize,
Model.Sum(Model.ForEach(city, i => Model.ForEachWhere(city, j => dist[i, j] * assign[i, j], j => i != j))));
// ------------
// Enter and leave each city only once.
int N = data.Length;
model.AddConstraint("assign_1",
Model.ForEach(city, i => Model.Sum(Model.ForEachWhere(city, j => assign[i, j],
j => i != j)) == 1));
model.AddConstraint("assign_2",
Model.ForEach(city, j => Model.Sum(Model.ForEachWhere(city, i => assign[i, j], i => i != j)) == 1));
// Forbid subtours (Miller, Tucker, Zemlin - 1960...)
model.AddConstraint("no_subtours",
Model.ForEach(city,
i => Model.ForEachWhere(city,
j => rank[i] + 1 <= rank[j] + N * (1 - assign[i, j]),
j => Model.And(i != j, i >= 1, j >= 1)
)
)
);
Solution solution = context.Solve();
double Cost = goal.ToDouble();
List<SubCalendarEvent> OptimizedSubCalEvents = new List<SubCalendarEvent>();
var tour = from p in assign.GetValues() where (double)p[0] > 0.9 select p;
foreach (var i in tour.ToArray())
{
int MyIndex =Convert.ToInt32(i[2]);
OptimizedSubCalEvents.Add(DictOFData[MyIndex]);
//Console.WriteLine(i[1] + " -> " + );
}
context.ClearModel();
return new Tuple<ICollection<SubCalendarEvent>, double>(OptimizedSubCalEvents, Cost);
#endif
}
static void Main(string[] args)
{
SolverContext context = SolverContext.GetContext();
Model model = context.CreateModel();
// ------------
// Parameters
Set city = new Set(Domain.IntegerNonnegative, "city");
Parameter dist = new Parameter(Domain.Real, "dist", city, city);
var arcs = from p1 in data
from p2 in data
select new Arc { City1 = p1.Name, City2 = p2.Name, Distance = p1.Distance(p2) };
dist.SetBinding(arcs, "Distance", "City1", "City2");
model.AddParameters(dist);
// ------------
// Decisions
Decision assign = new Decision(Domain.IntegerRange(0, 1), "assign", city, city);
Decision rank = new Decision(Domain.RealNonnegative, "rank", city);
model.AddDecisions(assign, rank);
// ------------
// Goal: minimize the length of the tour.
Goal goal = model.AddGoal("TourLength", GoalKind.Minimize,
Model.Sum(Model.ForEach(city, i => Model.ForEachWhere(city, j => dist[i, j] * assign[i, j], j => i != j))));
// ------------
// Enter and leave each city only once.
int N = data.Length;
model.AddConstraint("assign1",
Model.ForEach(city, i => Model.Sum(Model.ForEachWhere(city, j => assign[i, j],
j => i != j)) == 1));
model.AddConstraint("assign2",
Model.ForEach(city, j => Model.Sum(Model.ForEachWhere(city, i => assign[i, j], i => i != j)) == 1));
// Forbid subtours (Miller, Tucker, Zemlin - 1960...)
model.AddConstraint("no_subtours",
Model.ForEach(city,
i => Model.ForEachWhere(city,
j => rank[i] + 1 <= rank[j] + N * (1 - assign[i, j]),
j => Model.And(i != j, i >= 1, j >= 1)
)
)
);
Solution solution = context.Solve();
// Retrieve solution information.
Console.WriteLine("Cost = {0}", goal.ToDouble());
Console.WriteLine("Tour:");
var tour = from p in assign.GetValues() where (double)p[0] > 0.9 select p[2];
foreach (var i in tour.ToArray())
{
Console.Write(i + " -> ");
}
Console.WriteLine();
Console.WriteLine("Press enter to continue...");
Console.ReadLine();
}