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 TestSampleMonitor(IEnumerable <double> data)
{
var stat = new SampleMonitor(collect: false)
{
Active = false
};
var stat_collect = new SampleMonitor(collect: true)
{
Active = false
};
var data_list = data.ToList();
if (data_list.All(x => !double.IsNaN(x) && !double.IsInfinity(x)))
{
var count = 0;
foreach (var d in data_list)
{
if (count == 2)
{
stat.Active = stat_collect.Active = true;
}
stat.Add(d); stat_collect.Add(d);
count++;
}
var avg = data_list.Skip(2).Average();
var sum = data_list.Skip(2).Sum();
var cnt = data_list.Skip(2).Count();
var min = data_list.Skip(2).Min();
var max = data_list.Skip(2).Max();
var pvar = 0.0;
foreach (var d in data_list.Skip(2))
{
pvar += (d - avg) * (d - avg);
}
pvar /= cnt;
var med = data_list.Skip(2).Count() % 2 == 1 ? data_list.Skip(2).OrderBy(x => x).Skip(data_list.Skip(2).Count() / 2 - 1).First()
: data_list.Skip(2).OrderBy(x => x).Skip(data_list.Skip(2).Count() / 2 - 1).Take(2).Average();
Assert.Equal(avg, stat.Mean);
Assert.Equal(sum, stat.Total);
Assert.Equal(cnt, stat.Count);
Assert.Equal(min, stat.Min);
Assert.Equal(max, stat.Max);
Assert.Equal(pvar, stat.Variance, 14);
Assert.True(double.IsNaN(stat.GetMedian()));
Assert.Empty(stat.Samples);
Assert.Equal(avg, stat_collect.Mean);
Assert.Equal(sum, stat_collect.Total);
Assert.Equal(cnt, stat_collect.Count);
Assert.Equal(min, stat_collect.Min);
Assert.Equal(max, stat_collect.Max);
Assert.Equal(pvar, stat_collect.Variance, 14);
Assert.Equal(med, stat_collect.GetMedian());
Assert.Equal(data_list.Skip(2).Count(), stat_collect.Samples.Count());
stat.Active = false;
stat.Reset();
Assert.False(stat.Active);
stat_collect.Active = false;
stat_collect.Reset();
Assert.False(stat_collect.Active);
count = 0;
foreach (var d in data_list)
{
if (count == 2)
{
stat.Active = stat_collect.Active = true;
}
stat.Add(d); stat_collect.Add(d);
count++;
}
Assert.Equal(avg, stat.Mean);
Assert.Equal(sum, stat.Total);
Assert.Equal(cnt, stat.Count);
Assert.Equal(min, stat.Min);
Assert.Equal(max, stat.Max);
Assert.Equal(pvar, stat.Variance, 14);
Assert.True(double.IsNaN(stat.GetMedian()));
Assert.Empty(stat.Samples);
Assert.Equal(avg, stat_collect.Mean);
Assert.Equal(sum, stat_collect.Total);
Assert.Equal(cnt, stat_collect.Count);
Assert.Equal(min, stat_collect.Min);
Assert.Equal(max, stat_collect.Max);
Assert.Equal(pvar, stat_collect.Variance, 14);
Assert.Equal(med, stat_collect.GetMedian());
Assert.Equal(data_list.Skip(2).Count(), stat_collect.Samples.Count());
}
else
{
stat.Active = true;
Assert.Throws <ArgumentException>(() => {
foreach (var d in data_list)
{
stat.Add(d);
}
});
}
}