Update README and add preprint

README.md

@@ -1,5 +1,33 @@
 # Routing and appointment scheduling
 
-This repository contains the code implementation of our paper "A queueing-based approach for integrated routing and appointment scheduling."
+This repository contains the code implementation of our paper [*A queueing-based approach for integrated routing and appointment scheduling*](preprint.pdf).
 
-TODO
+## Installation
+
+To use this repository, make sure to have [Poetry](https://python-poetry.org/) installed with version 1.2 or higher. The following command will then create a virtual environment with all necessary dependencies:
+
+```shell
+poetry install
+```
+
+If you don't want to use Poetry, you can use install all packages listed in the `pyproject.toml` file (requires Python 3.9 or higher).
+
+
+## Usage
+
+You can use the `benchmark.py` script to solve instances. Here's an example:
+
+``` shell
+poetry run python benchmark.py instances/*.json \
+--num_procs 4 \
+--weight_travel 1 \
+--weight_idle 2.5 \
+--weight_wait 10 \
+--algorithm lns \
+--seed 1 \
+--max_runtime 1
+```
+
+This command solves all instances, four in parallel at a time. The objective weights are 1, 2.5 and 10 for the travel, idle and waiting time contributions, respectively. The selected algorithm is LNS with seed 1 and runs for one second.
+
+All results presented in our paper can be found in this repository. In particular, the raw results data is stored in `data/` and the notebooks that generate tables and figures can be found in `notebooks/`.

notebooks/analyze-benchmark-results.ipynb

@@ -805,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 201,
    "id": "4bb0a920-d33a-44b6-8583-1a40dbda6467",
    "metadata": {
     "tags": []
@@ -1098,7 +1098,7 @@
        "    2.0         5.85  1.04  0.06  21.48  0.00  4.15  2.81  16.65"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 201,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1119,7 +1119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 202,
    "id": "04c4162d-bd77-41c0-af20-824976f7fa12",
    "metadata": {},
    "outputs": [
@@ -1458,7 +1458,7 @@
        "    2.0         5.85  1.04  0.06  21.48  0.00  4.15  2.81  16.65"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 202,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1474,6 +1474,85 @@
     "print(res.mean().to_frame().T.to_latex(float_format=\"%.2f\", escape=True))\n",
     "res"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "57882e46-c228-4e62-8915-09de3e224480",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>serv</th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>level_1</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DOTSP</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>2.584167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LNS</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.764167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MTSP</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>3.383889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NNSVF</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>12.784722</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         serv          0\n",
+       "level_1                 \n",
+       "DOTSP     0.5   2.584167\n",
+       "LNS       0.5   0.764167\n",
+       "MTSP      0.5   3.383889\n",
+       "NNSVF     0.5  12.784722"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res.mean().reset_index().groupby(\"level_1\").mean()"
+   ]
   }
  ],
  "metadata": {

notebooks/utils.py

@@ -0,0 +1,32 @@
+from ast import literal_eval as make_tuple
+from pathlib import Path
+from typing import Union
+
+import pandas as pd
+
+
+def read_single_instance_benchmark_output(path: Union[str, Path]) -> pd.DataFrame:
+    with open(path, "r") as fh:
+        line = fh.read()
+
+    print(line)
+    row = list(make_tuple(line.strip()))
+
+    # Parse the instance name to identify the instance characteristics
+    values = [s.strip("nidxdistributiontravelserv") for s in row[0].split("-")]
+    data = values + row[1:]
+
+    headers = [
+        "n",
+        "idx",
+        "distribution",
+        "travel",
+        "serv",
+        "obj",
+        "iters",
+        "time",
+        "alg",
+        "cost_profile",
+    ]
+    dtypes = [int, int, int, int, int, float, float, float, str, str]
+    return pd.DataFrame([data], columns=headers).astype(dict(zip(headers, dtypes)))