feat(full_model): + warmup event

Author: TomMonks

content/08_full_model.ipynb

@@ -642,6 +642,33 @@
     "        env.process(service(caller_count, env, args))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ed7d9306-2f77-4f4f-bb14-ea3e4aebae0a",
+   "metadata": {},
+   "source": [
+    "## 🥵 Warm-up period\n",
+    "\n",
+    "The call centre model starts from empty.  If the call centre runs 24/7 then it is a non-terminating system and our estimates of waiting time and server utilisation are biased due to the empty period at the start of the simulation.  We can remove this initialisation bias using a warm-up period.  \n",
+    "\n",
+    "We will implement a warm-up through an **event** that happens once in a single run of the model.  The model will be run for the warm-up period + results collection period.  At the end of the warm-up period an event will happen where all variables in the current experiment are reset (e.g. empty lists and set quantitative values to 0.0).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e5148415-3dff-41ad-ad17-ba755a86074f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def end_of_warmup(warm_up_period, env, args):\n",
+    "    yield env.timeout(warm_up_period)\n",
+    "    trace(f'{env.now:.2f}: Warm-up complete')\n",
+    "\n",
+    "    # reset variables to remove initalisation bias.\n",
+    "    args.init_results_variables()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "161ce6c8-b50b-457c-8f9e-4837edb07933",