diff --git a/notebooks/Optimization.ipynb b/notebooks/Optimization.ipynb
index 47c5eda538d156f5794e00c9485bab23277fbb10..bb1dc12d2028235b77c42d06977797fb0c4466b3 100644
--- a/notebooks/Optimization.ipynb
+++ b/notebooks/Optimization.ipynb
@@ -169,23 +169,35 @@
     "                paper = Paper(paper_id, [reviewer], score)\n",
     "                result.append(paper)\n",
     "\n",
-    "    return result\n",
-    "\n",
-    "papers = load_data('../data/optimization_data.tsv')"
+    "    return result"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "36d5ab0d",
+   "id": "b96637c3",
    "metadata": {},
    "source": [
-    "## Functions"
+    "## The Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "537d7040",
+   "metadata": {},
+   "source": [
+    "### Slow approach\n",
+    "\n",
+    "<div class=\"alert alert-warning\">\n",
+    "    \n",
+    "**Warning**: Depending on the slot size and the number of papers, this approach gets very slow very fast. It is recommended to limit the number of papers and `max_slot_size`.\n",
+    "\n",
+    "</div>"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d737af19",
+   "id": "ea63212b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,22 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "067ea4ea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def score_paper(paper) -> int:\n",
-    "    if len(paper.reviewers) > 0:\n",
-    "        review_score = 2 - abs(paper.score)\n",
-    "        return review_score\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "23670286",
+   "id": "05dd3bc9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,28 +271,6 @@
     "        return True"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "b96637c3",
-   "metadata": {},
-   "source": [
-    "## The Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd6a6099",
-   "metadata": {},
-   "source": [
-    "### Slow approach\n",
-    "\n",
-    "<div class=\"alert alert-warning\">\n",
-    "    \n",
-    "**Warning**: Depending on the slot size and the number of papers, this approach gets very slow very fast. It is recommended to limit the number of papers and `max_slot_size`.\n",
-    "\n",
-    "</div>"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -303,12 +278,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "max_time_slots = 3\n",
-    "max_slot_size = 5\n",
-    "\n",
+    "papers = load_data('../data/optimization_data.tsv')\n",
     "# use the first 15 papers\n",
     "papers = papers[:15]\n",
     "\n",
+    "max_time_slots = 3\n",
+    "max_slot_size = 5\n",
+    "\n",
     "all_time_slots = [c for c in pulp.allcombinations(papers, max_slot_size)]\n",
     "possible_time_slots = []\n",
     "\n",
@@ -363,7 +339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0918db10",
+   "id": "7d5e9686",
    "metadata": {},
    "source": [
     "### Fast approach\n",
@@ -378,10 +354,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "501af564",
+   "id": "9a065c01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score_paper(paper) -> int:\n",
+    "    if len(paper.reviewers) > 0:\n",
+    "        review_score = 2 - abs(paper.score)\n",
+    "        return review_score\n",
+    "    else:\n",
+    "        return 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fea427b4",
    "metadata": {},
    "outputs": [],
    "source": [
+    "papers = load_data('../data/optimization_data.tsv')\n",
+    "\n",
     "max_time_slots = 3\n",
     "max_slot_size = 50\n",
     "\n",