public void TestTimeSeriesMonitorAutoUpdate()
{
var env = new Simulation();
var stat = new TimeSeriesMonitor(env);
env.Process(StatProcess(env, stat));
env.Run();
stat.Reset();
env.Process(StatProcess(env, stat));
env.Run();
}
private IEnumerable <Event> StatProcess(Simulation env, TimeSeriesMonitor stat)
{
stat.UpdateTo(3);
yield return(env.TimeoutD(1));
stat.UpdateTo(1);
yield return(env.TimeoutD(1));
Assert.Equal(3, stat.Max);
Assert.Equal(2, stat.Mean);
Assert.Equal(1, stat.Current);
Assert.Equal(4, stat.Area);
Assert.Equal(2, stat.TotalTimeD);
Assert.Equal(1, stat.Variance);
}
public HotstorageSimulation(Settings set)
{
settings = set;
sim = new PseudoRealtimeSimulation(DateTime.UtcNow, settings.Seed);
ServiceLevel = new SampleMonitor("SerivceLevel");
LeadTimes = new SampleMonitor("LeadTimes");
Tardiness = new SampleMonitor("Tardiness");
BufferUtilization = new TimeSeriesMonitor(sim, "BufferUtilization");
CraneUtilization = new TimeSeriesMonitor(sim, "CraneUtilization");
HandoverUtilization = new TimeSeriesMonitor(sim, "HandoverUtilization");
UpstreamUtilization = new TimeSeriesMonitor(sim, "UpstreamUtilization");
lastScheduleSequence = int.MinValue;
World = new World {
Production = new Stack {
Id = 0, MaxHeight = settings.ProductionMaxHeight, BottomToTop = new List <Block>()
},
Buffers = Enumerable.Range(1, settings.BufferCount).Select(x => new Stack {
Id = x, MaxHeight = settings.BufferMaxHeight, BottomToTop = new List <Block>()
}).ToList(),
Handover = new Handover()
{
Id = settings.BufferCount + 1, Ready = false
},
Now = new TimeStamp(),
Crane = new Crane {
Id = 0, LocationId = 0, Schedule = new CraneSchedule()
{
Moves = new List <CraneMove>()
}, Load = null, GirderPosition = 0.0, HoistPosition = 1.0
},
KPIs = new Performance(),
ObservationData = new Uncertainties(),
InvalidMoves = new List <CraneMove>()
};
crane = new Resource(sim, capacity: 1)
{
Utilization = CraneUtilization
};
handover = new Resource(sim, capacity: 1)
{
Utilization = HandoverUtilization
};
upstream = new Resource(sim, capacity: 1)
{
Utilization = UpstreamUtilization
};
InitializeWorldState();
}
public void Simulate(int rseed = 42)
{
completedOrders = 0;
env = new Simulation(randomSeed: rseed);
env.Log("== Kanban controlled production system ==");
kanban = new Resource(env, capacity: 15);
server = new Resource(env, capacity: 1);
// In this sample stockStat is tracked manually in the process
// it would also possible to track kanban's utilization and obtain
// the stock as capacity * (1 - util.mean)
stockStat = new TimeSeriesMonitor(env, name: "Kanbans in stock", collect: true);
env.Process(Source());
env.Run(TimeSpan.FromDays(180));
Console.WriteLine("Kanbans in stock: {0} ; {1:F1}±{2:F1} ; {3} (Min;Mean±StdDev;Max) kanbans ", stockStat.Min, stockStat.Mean, stockStat.StdDev, stockStat.Max);
Console.WriteLine("Produced kanbans: {0:N0}", completedOrders);
Console.WriteLine(stockStat.Summarize(binWidth: 1));
}
public void Simulate(int repetitions = 5)
{
var lambda = 1 / OrderArrivalTime.TotalDays;
var mu = 1 / ProcessingTime.TotalDays;
var rho = lambda / mu;
var analyticWIP = rho / (1 - rho);
var analyticLeadtime = 1 / (mu - lambda);
var analyticWaitingtime = rho / (mu - lambda);
var env = new Simulation(randomSeed: 1, defaultStep: TimeSpan.FromDays(1));
var utilization = new TimeSeriesMonitor(env, name: "Utilization");
var wip = new TimeSeriesMonitor(env, name: "WIP", collect: true);
var leadtime = new SampleMonitor(name: "Lead time", collect: true);
var waitingtime = new SampleMonitor(name: "Waiting time", collect: true);
env.Log("Analytical results of this system:");
env.Log("Time\tUtilization.Mean\tWIP.Mean\tLeadtime.Mean\tWaitingTime.Mean");
env.Log("{4}\t{0}\t{1}\t{2}\t{3}", rho, analyticWIP, analyticLeadtime, analyticWaitingtime, double.PositiveInfinity);
env.Log("");
// example to create a running report of these measures every simulated week
//var report = Report.CreateBuilder(env)
// .Add("Utilization", utilization, Report.Measures.Mean | Report.Measures.StdDev)
// .Add("WIP", wip, Report.Measures.Min | Report.Measures.Mean | Report.Measures.Max)
// .Add("Leadtime", leadtime, Report.Measures.Min | Report.Measures.Mean | Report.Measures.Max)
// .Add("WaitingTime", waitingtime, Report.Measures.Min | Report.Measures.Mean | Report.Measures.Max)
// .SetOutput(env.Logger) // use a "new StreamWriter("report.csv")" to direct to a file
// .SetSeparator("\t")
// .SetPeriodicUpdate(TimeSpan.FromDays(7), withHeaders: true)
// .Build();
var summary = Report.CreateBuilder(env)
.Add("Utilization", utilization, Report.Measures.Mean)
.Add("WIP", wip, Report.Measures.Mean)
.Add("Leadtime", leadtime, Report.Measures.Mean)
.Add("WaitingTime", waitingtime, Report.Measures.Mean)
.SetOutput(env.Logger)
.SetSeparator("\t")
.SetFinalUpdate(withHeaders: false) // creates a summary of the means at the end
.SetTimeAPI(useDApi: true)
.Build();
env.Log("Simulated results of this system (" + repetitions + " repetitions):");
env.Log("");
summary.WriteHeader(); // write the header just once
for (var i = 0; i < repetitions; i++)
{
env.Reset(i + 1); // reset environment
utilization.Reset(); // reset monitors
wip.Reset();
leadtime.Reset();
waitingtime.Reset();
var server = new Resource(env, capacity: 1)
{
Utilization = utilization,
WIP = wip,
LeadTime = leadtime,
WaitingTime = waitingtime,
};
env.Process(Source(env, server));
env.Process(HandleWarmup(env, TimeSpan.FromDays(32), utilization, wip, leadtime, waitingtime));
env.Run(TimeSpan.FromDays(365));
}
env.Log("");
env.Log("Detailed results from the last run:");
env.Log("");
env.Log(utilization.Summarize());
env.Log(wip.Summarize(maxBins: 10, binWidth: 2));
env.Log(leadtime.Summarize(maxBins: 10, binWidth: 5 / 1440.0));
env.Log(waitingtime.Summarize(maxBins: 10, binWidth: 4 / 1440.0));;
}
public void TestTimeSeriesMonitor(double[] times, double[] values, double min, double max,
double mean, double variance, double area, double median)
{
var env = new Simulation();
var stat = new TimeSeriesMonitor(env, collect: false)
{
Active = false
};
var stat_collect = new TimeSeriesMonitor(env, collect: true)
{
Active = false
};
var count = 0;
foreach (var v in times.Zip(values, Tuple.Create))
{
if (v.Item1 > 0)
{
env.RunD(v.Item1);
}
if (count == 3)
{
stat.Active = stat_collect.Active = true;
}
stat.UpdateTo(v.Item2);
stat_collect.UpdateTo(v.Item2);
if (count == times.Length - 3)
{
stat.Active = stat_collect.Active = false;
}
count++;
}
Assert.Equal(min, stat.Min);
Assert.Equal(max, stat.Max);
Assert.Equal(mean, stat.Mean, 14);
Assert.Equal(variance, stat.Variance, 14);
Assert.Equal(area, stat.Area);
Assert.True(double.IsNaN(stat.GetMedian()));
Assert.True(double.IsNaN(stat.GetPercentile(0.25)));
Assert.True(double.IsNaN(stat.GetPercentile(0.75)));
Assert.Empty(stat.Series);
Assert.Equal(min, stat_collect.Min);
Assert.Equal(max, stat_collect.Max);
Assert.Equal(mean, stat_collect.Mean, 14);
Assert.Equal(variance, stat_collect.Variance, 14);
Assert.Equal(area, stat_collect.Area);
Assert.Equal(median, stat_collect.GetMedian());
Assert.True(stat_collect.GetPercentile(0.25) <= median);
Assert.True(stat_collect.GetPercentile(0.75) >= median);
Assert.Equal(values.Length - 5, stat_collect.Series.Count());
stat.Reset();
Assert.False(stat.Active);
stat_collect.Reset();
Assert.False(stat_collect.Active);
count = 0;
foreach (var v in times.Zip(values, Tuple.Create))
{
if (v.Item1 > 0)
{
env.RunD(v.Item1);
}
if (count == 3)
{
stat.Active = stat_collect.Active = true;
}
stat.UpdateTo(v.Item2);
stat_collect.UpdateTo(v.Item2);
if (count == times.Length - 3)
{
stat.Active = stat_collect.Active = false;
}
count++;
}
Assert.Equal(min, stat.Min);
Assert.Equal(max, stat.Max);
Assert.Equal(mean, stat.Mean, 14);
Assert.Equal(variance, stat.Variance, 14);
Assert.Equal(area, stat.Area);
Assert.True(double.IsNaN(stat.GetMedian()));
Assert.True(double.IsNaN(stat.GetPercentile(0.25)));
Assert.True(double.IsNaN(stat.GetPercentile(0.75)));
Assert.Empty(stat.Series);
Assert.Equal(min, stat_collect.Min);
Assert.Equal(max, stat_collect.Max);
Assert.Equal(mean, stat_collect.Mean, 14);
Assert.Equal(variance, stat_collect.Variance, 14);
Assert.Equal(area, stat_collect.Area);
Assert.Equal(median, stat_collect.GetMedian());
Assert.True(stat_collect.GetPercentile(0.25) <= median);
Assert.True(stat_collect.GetPercentile(0.75) >= median);
Assert.Equal(values.Length - 5, stat_collect.Series.Count());
}
