Update VRP notebook with iterations

N-Wouda · Oct 21, 2024 · 4f1d300 · 4f1d300
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Showing 2 changed files with 14 additions and 8 deletions.
diff --git a/examples/capacitated_vehicle_routing_problem.ipynb b/examples/capacitated_vehicle_routing_problem.ipynb
@@ -21,7 +21,7 @@
     "from alns import ALNS\n",
     "from alns.accept import RecordToRecordTravel\n",
     "from alns.select import RouletteWheel\n",
-    "from alns.stop import MaxRuntime"
+    "from alns.stop import MaxIterations"
    ]
   },
   {
@@ -472,10 +472,13 @@
    },
    "outputs": [],
    "source": [
+    "num_iterations = 3000\n",
     "init = nearest_neighbor()\n",
     "select = RouletteWheel([25, 5, 1, 0], 0.8, 1, 1)\n",
-    "accept = RecordToRecordTravel.autofit(init.objective(), 0.02, 0, 9000)\n",
-    "stop = MaxRuntime(60)\n",
+    "accept = RecordToRecordTravel.autofit(\n",
+    "    init.objective(), 0.02, 0, num_iterations\n",
+    ")\n",
+    "stop = MaxIterations(num_iterations)\n",
     "\n",
     "result = alns.iterate(init, select, accept, stop)"
    ]
@@ -632,10 +635,13 @@
    },
    "outputs": [],
    "source": [
+    "num_iterations = 3000\n",
     "init = nearest_neighbor()\n",
     "select = RouletteWheel([25, 5, 1, 0], 0.8, 1, 1)\n",
-    "accept = RecordToRecordTravel.autofit(init.objective(), 0.02, 0, 6000)\n",
-    "stop = MaxRuntime(60)\n",
+    "accept = RecordToRecordTravel.autofit(\n",
+    "    init.objective(), 0.02, 0, num_iterations\n",
+    ")\n",
+    "stop = MaxIterations(num_iterations)\n",
     "\n",
     "result = alns.iterate(init, select, accept, stop)"
    ]
@@ -696,7 +702,7 @@
    "id": "7424ca3f-b4e7-459d-a307-be2d26426cdc",
    "metadata": {},
    "source": [
-    "In this notebook we implemented two heuristics for the CVRP, using the ALNS meta-heuristic framework. The first heuristic used a random customer destroy operator and we obtained a solution which is 6% worse than the best known solution. The second heuristic used a destroy operator which removes strings arround a randomly selected customer and we obtained a solution that is only 3% worse than the best known solution. \n",
+    "In this notebook we implemented two heuristics for the CVRP, using the ALNS meta-heuristic framework. The first heuristic used a random customer destroy operator and we obtained a solution which is 10% worse than the best known solution. The second heuristic used a destroy operator which removes strings arround a randomly selected customer and we obtained a solution that is only 3% worse than the best known solution. \n",
     "\n",
     "This example shows that by constructing problem-specific operators, one can create even more powerful ALNS heuristics."
    ]
@@ -710,7 +716,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },

diff --git a/examples/permutation_flow_shop_problem.ipynb b/examples/permutation_flow_shop_problem.ipynb
@@ -747,7 +747,7 @@
    "metadata": {},
    "source": [
     "## Conclusions\n",
-    "In this notebook, we implemented several variants of adaptive large neighborhood search heuristic to solve the permutation flow shop problem. We obtained a solution that is only 1.5% worse than the best known solution. Furthermore, we showed several advanced features in ALNS, including:\n",
+    "In this notebook, we implemented several variants of adaptive large neighborhood search heuristic to solve the permutation flow shop problem. We obtained a solution that is only 1-2% worse than the best known solution. Furthermore, we showed several advanced features in ALNS, including:\n",
     "\n",
     "* Autofitting acceptance criteria\n",
     "* Adding local search to repair operators\n",