example_sim_call_center.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Complex call center model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulator\n",
    "from queuesim import Simulator\n",
    "\n",
    "# Station types\n",
    "from queuesim.stations import Source, Process, Decide, Delay, Dispose\n",
    "\n",
    "# Pseudo random number generators\n",
    "from queuesim.random_dist import exp as dist_exp\n",
    "\n",
    "# Print results\n",
    "from queuesim.models import call_center_results\n",
    "\n",
    "# Plot model\n",
    "from queuesim import build_graph\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Defining general plot style\n",
    "sns.set()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n",
    "<pre><code>\n",
    "             -------------\n",
    "             v           |\n",
    " Source -> Process -> Forward -> Dispose\n",
    "           ^    |                   ^\n",
    "           |   Retry ---------------|\n",
    "           |    v\n",
    "           |-- Delay\n",
    "</code></pre>\n",
    "\n",
    "This model can also be simulated via this online simulator:\n",
    "<https://a-herzog.github.io/MiniSimulator/>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrivals to be simulated\n",
    "count = 100_000\n",
    "\n",
    "# Arrival process\n",
    "mean_i = 100\n",
    "get_i = dist_exp(mean_i)\n",
    "get_ib = None  # Batch arrivals\n",
    "\n",
    "# Service process\n",
    "mean_s = 80\n",
    "get_s = dist_exp(mean_s)\n",
    "b = 1  # Service batch size\n",
    "\n",
    "# Post-processing times\n",
    "get_s2 = None\n",
    "\n",
    "# Impatience\n",
    "mean_nu = 900\n",
    "get_nu = None  # None = no impatience\n",
    "\n",
    "# Retry process\n",
    "retry_rate = 0\n",
    "mean_retry_delay = 900\n",
    "get_retry_delay = dist_exp(mean_retry_delay)\n",
    "\n",
    "# Number of operators\n",
    "c = 1\n",
    "\n",
    "# System size\n",
    "K = -1  # -1 stands for \"unlimited\"\n",
    "\n",
    "# FIFO or LIFO client selection\n",
    "LIFO = False\n",
    "\n",
    "# Forwarding\n",
    "forwarding_rate = 0"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the model above many of the possible properties are not activated (like retry, forwarding, batching etc.). Also only the exponential distribution is used. So this just a M/M/c model with mit E[I]=100, E[S]=80 and c=1. This can be solved analytically. The results are:\n",
    "\n",
    "* Mean waiting time: E[W]=320\n",
    "* Mean service time: E[S]=80 (just defined above)\n",
    "* Mean residence time: E[V]=E[W]+E[S]=400\n",
    "* Mean queue length: E[N<sub>Q</sub>]=3,2\n",
    "* Mean number of clients in the system: E[N]=4\n",
    "* Utilization: &rho;=80%\n",
    "\n",
    "Try changing some of the model parameters and see what happens in each case compared to the Erlang C base model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator = Simulator()\n",
    "source = Source(simulator, count, get_i, get_ib)\n",
    "process = Process(simulator, get_s, c, get_nu, get_s2, K, b, LIFO)\n",
    "forwarding = Decide(simulator)\n",
    "retry = Decide(simulator)\n",
    "delay = Delay(simulator, get_retry_delay)\n",
    "dispose = Dispose(simulator)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Link stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "source.set_next(process)\n",
    "process.set_next(forwarding)\n",
    "process.set_next_cancel(retry)\n",
    "forwarding.add_next(dispose, 1 - forwarding_rate)\n",
    "forwarding.add_next(process, forwarding_rate)\n",
    "retry.add_next(dispose, 1 - retry_rate)\n",
    "retry.add_next(delay, retry_rate)\n",
    "delay.set_next(process)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator.run()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System\n",
      "  Simulated arrivals: 100000\n",
      "  Inter-arrival times at the system (I): count = 100000, mean = 99.8, sd = 99.8, cv = 1.0, min = 0.0, max = 892.5\n",
      "  Inter-departure times from the system (ID): count = 100000, mean = 99.8, sd = 99.5, cv = 1.0, min = 0.0, max = 793.1\n",
      "\n",
      "Process station\n",
      "  Success rate: count = 100000, Success = 100.0%\n",
      "  Waiting times (W): count = 100000, mean = 313.8, sd = 373.6, cv = 1.2, min = 0.0, max = 1525.0\n",
      "  Service times (S): count = 100000, mean = 80.2, sd = 79.9, cv = 1.0, min = 0.0, max = 659.5\n",
      "  Queue length (NQ): timespan = 9980313.8, mean = 3.1, min = 0.0, max = 39.0\n",
      "  Clients at the station (N): timespan = 9980313.8, mean = 3.9, min = 0.0, max = 40.0\n",
      "  Work load (c*rho): timespan = 9980313.8, mean = 0.8, min = 0.0, max = 1.0\n",
      "\n",
      "Clients\n",
      "  Waiting times (W): count = 100000, mean = 313.8, sd = 373.6, cv = 1.2, min = 0.0, max = 1525.0\n",
      "  Service times (S): count = 100000, mean = 80.2, sd = 79.9, cv = 1.0, min = 0.0, max = 659.5\n",
      "  Residence times (V): count = 100000, mean = 393.9, sd = 381.8, cv = 1.0, min = 0.0, max = 1561.1\n",
      "\n",
      "Simulator\n",
      "  Total computing time: 2.5 seconds\n",
      "  Computing time per client: 25.0 µs\n",
      "  Computing time per event: 5.0 µs\n",
      "  Simulated events: 500000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(call_center_results(source, process, forwarding, retry, delay, dispose, simulator))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Queueing network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dg = build_graph([source])\n",
    "fig, ax = plt.subplots(figsize=(19, 9))\n",
    "nx.draw(dg, ax=ax, with_labels=True, node_color='#CCCCFF', node_size=2000, arrowsize=30, width=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}