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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()
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()
import jline.lang.ClosedClass;
import jline.lang.RoutingMatrix;
import jline.lang.processes.Erlang;
import jline.solvers.nc.NC;

Network model = new Network("RL");
Queue queue = new Queue(model, "Queue", SchedStrategy.FCFS);
ClosedClass jobClass1 = new ClosedClass(model, "Class1", 1, queue);
ClosedClass jobClass2 = new ClosedClass(model, "Class2", 0, queue);
ClosedClass jobClass3 = new ClosedClass(model, "Class3", 0, queue);
queue.setService(jobClass1, Erlang.fitMeanAndOrder(1, 2));
queue.setService(jobClass2, Erlang.fitMeanAndOrder(2, 2));
queue.setService(jobClass3, Erlang.fitMeanAndOrder(3, 2));

RoutingMatrix P = model.initRoutingMatrix();
P.set(jobClass1, jobClass2, queue, queue, 1.0);
P.set(jobClass2, jobClass3, queue, queue, 1.0);
P.set(jobClass3, jobClass1, queue, queue, 1.0);
model.link(P);

new NC(model).avgTable().print();
new NC(model).avgSysTable().print();
jobClass1.setCompletes(false);
jobClass2.setCompletes(false);
new NC(model).avgSysTable().print();
#include "line/solvers/nc/solver_nc_runner.h"

Network model("RL");
Queue queue(model, "Queue", SchedStrategy::FCFS);
const std::size_t K = 3;
const double N[3] = {1, 0, 0};
std::vector<std::size_t> jobclass(K);
for (std::size_t k = 0; k < K; ++k) {
    jobclass[k] = model.add_closed_class(
        "Class" + std::to_string(k + 1), N[k], queue);
    queue.set_service(jobclass[k], Erlang(2.0 / double(k + 1), 2));
}

Routing P;
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::NcSolverOptions opt;
const mva::AvgResult<double> res =
    nc::solver_nc_run_analyzer(model.get_struct(), opt);
model.raw_struct().classes[jobclass[0] - 1].completes = false;
model.raw_struct().classes[jobclass[1] - 1].completes = false;

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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