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Tutorial 5: Re-entrant Line Modeling

This example models re-entrant lines where jobs re-enter a station multiple times, asking for different classes of service at each visit. The completes flag controls whether a class transition counts as a job completion.

Re-entrant Line Network Diagram 
model = Network('RL');
queue = Queue(model, 'Queue', SchedStrategy.FCFS);

K = 3; N = [1,0,0];
for k=1:K
    jobclass{k} = ClosedClass(model, ['Class',int2str(k)], N(k), queue);
    queue.setService(jobclass{k}, Erlang.fitMeanAndOrder(k,2));
end

P = model.initRoutingMatrix();
P{jobclass{1},jobclass{2}}(queue,queue) = 1.0;
P{jobclass{2},jobclass{3}}(queue,queue) = 1.0;
P{jobclass{3},jobclass{1}}(queue,queue) = 1.0;
model.link(P);

ncAvgTable = NC(model).avgTable()
ncAvgSysTable = NC(model).avgSysTable()

jobclass{1}.completes = false;
jobclass{2}.completes = false;
ncAvgSysTable2 = NC(model).avgSysTable()
// Example 5: Re-entrant line modeling
val model = Network("RL")
val queue = Queue(model, "Queue", SchedStrategy.FCFS)

val K = 3
val N = intArrayOf(1, 0, 0)
val jobclass = mutableListOf<ClosedClass>()

for (k in 0 until K) {
    val jc = ClosedClass(model, "Class${k+1}", N[k], queue)
    queue.setService(jc, Erlang.fitMeanAndOrder((k+1).toDouble(), 2))
    jobclass.add(jc)
}

val P = model.initRoutingMatrix()
P.set(jobclass[0], jobclass[1], queue, queue, 1.0)
P.set(jobclass[1], jobclass[2], queue, queue, 1.0)
P.set(jobclass[2], jobclass[0], queue, queue, 1.0)
model.link(P)

NC(model).avgTable.print()
NC(model).avgSysTable.print()

jobclass[0].setCompletes(false)
jobclass[1].setCompletes(false)
NC(model).avgSysTable.print()
from line_solver import *

model = Network('RL')
queue = Queue(model, 'Queue', SchedStrategy.FCFS)

K = 3
N = (1, 0, 0)
jobclass = []
for k in range(K):
    jobclass.append(ClosedClass(model, 'Class' + str(k+1), N[k], queue))
    queue.set_service(jobclass[k], Erlang.fit_mean_and_order(1+k, 2))

P = model.init_routing_matrix()
P.set(jobclass[0], jobclass[1], queue, queue, 1.0)
P.set(jobclass[1], jobclass[2], queue, queue, 1.0)
P.set(jobclass[2], jobclass[0], queue, queue, 1.0)
model.link(P)

ncAvgTable = NC(model).avg_table()
ncAvgSysTable = NC(model).avg_sys_table()

jobclass[0].completes = False
jobclass[1].completes = False
ncAvgSysTable2 = NC(model).avg_sys_table()

Expected Output (Per-class metrics)

ncAvgTable =
    Station    JobClass     QLen       Util      RespT    ResidT      ArvR       Tput
    _______    ________    _______    _______    _____    _______    _______    _______
     Queue      Class1     0.16667    0.16667      1      0.33333    0.16667    0.16667
     Queue      Class2     0.33333    0.33333      2      0.66667    0.16667    0.16667
     Queue      Class3         0.5        0.5      3            1    0.16667    0.16667

Expected Output (System metrics, completes=true)

ncAvgSysTable =
    Chain               JobClasses              SysRespT    SysTput
    ______    ______________________________    ________    _______
    Chain1    {[Class1    Class2    Class3]}       2          0.5

Expected Output (System metrics, completes=false for Class1,2)

ncAvgSysTable2 =
    Chain               JobClasses              SysRespT    SysTput
    ______    ______________________________    ________    _______
    Chain1    {[Class1    Class2    Class3]}       6        0.16667
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