example_sim_mmc_priorities2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Different priorities for different client types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulation\n",
    "from queuesim.random_dist import exp as dist_exp\n",
    "from queuesim.descore import Simulator\n",
    "from queuesim.stations import Source, Process, DecideClientType, Dispose\n",
    "\n",
    "# Plot model\n",
    "from queuesim import build_graph\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean inter-arrival time\n",
    "mean_i = 100\n",
    "\n",
    "# Mean service time\n",
    "mean_s = 80\n",
    "\n",
    "# Number of operators\n",
    "c = 1\n",
    "\n",
    "# Number of arrivals to be simulated\n",
    "count = 100_000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Priority formula\n",
    "\n",
    "For type A clients, each waiting second has 5 times more value in terms of service priority than for type B clients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def priority(client, waiting_time):\n",
    "    if client.type_name==\"ClientsA\":\n",
    "        return 5*waiting_time\n",
    "    else:\n",
    "        return waiting_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define parameters\n",
    "get_i = dist_exp(mean_i * 2)  # average inter-arrival time mean_i is for both client types together. mean_i*2 is for a single client type.\n",
    "get_s = dist_exp(mean_s)\n",
    "\n",
    "# Create and configure stations\n",
    "simulator = Simulator()\n",
    "source1 = Source(simulator, round(count / 2), get_i, client_type_name=\"ClientsA\")\n",
    "source2 = Source(simulator, round(count / 2), get_i, client_type_name=\"ClientsB\")\n",
    "process = Process(simulator, get_s, c, getPriority=priority)\n",
    "decide = DecideClientType(simulator)\n",
    "dispose1 = Dispose(simulator)\n",
    "dispose2 = Dispose(simulator)\n",
    "\n",
    "# Link stations\n",
    "source1.set_next(process)\n",
    "source2.set_next(process)\n",
    "process.set_next(decide)\n",
    "decide.set_next(\"ClientsA\", dispose1)\n",
    "decide.set_next(\"ClientsB\", dispose2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Queueing network model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dg = build_graph([source1, source2])\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 2.84 s\n",
      "Wall time: 2.85 s\n",
      "ClientsA\n",
      "  Service times (S): mean = 79.5, sd = 79.3, cv = 1.0\n",
      "  Residence times (V): mean = 243.7, sd = 189.3, cv = 0.8\n",
      "  Waiting times (W): mean = 164.3, sd = 172.2, cv = 1.0\n",
      "ClientsB\n",
      "  Service times (S): mean = 80.2, sd = 80.6, cv = 1.0\n",
      "  Residence times (V): mean = 526.2, sd = 571.3, cv = 1.1\n",
      "  Waiting times (W): mean = 445.9, sd = 565.3, cv = 1.3\n"
     ]
    }
   ],
   "source": [
    "# Run simulation\n",
    "%time simulator.run()\n",
    "\n",
    "# Show results\n",
    "print(\"ClientsA\")\n",
    "print(\"  Service times (S): \" + dispose1.statistic_client_service.info)\n",
    "print(\"  Residence times (V): \" + dispose1.statistic_client_residence.info)\n",
    "print(\"  Waiting times (W): \" + dispose1.statistic_client_waiting.info)\n",
    "print(\"ClientsB\")\n",
    "print(\"  Service times (S): \" + dispose2.statistic_client_service.info)\n",
    "print(\"  Residence times (V): \" + dispose2.statistic_client_residence.info)\n",
    "print(\"  Waiting times (W): \" + dispose2.statistic_client_waiting.info)"
   ]
  }
 ],
 "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
}