V2.0.0

README.md

@@ -257,17 +257,17 @@ disjunctive_graph = build_disjunctive_graph(instance)
 
  >>> disjunctive_graph.nodes_by_type
  defaultdict(list,
-            {<NodeType.OPERATION: 1>: [Node(node_type=OPERATION, value=O(m=0, d=1, j=0, p=0), id=0),
-              Node(node_type=OPERATION, value=O(m=1, d=1, j=0, p=1), id=1),
-              Node(node_type=OPERATION, value=O(m=2, d=7, j=0, p=2), id=2),
-              Node(node_type=OPERATION, value=O(m=1, d=5, j=1, p=0), id=3),
-              Node(node_type=OPERATION, value=O(m=2, d=1, j=1, p=1), id=4),
-              Node(node_type=OPERATION, value=O(m=0, d=1, j=1, p=2), id=5),
-              Node(node_type=OPERATION, value=O(m=2, d=1, j=2, p=0), id=6),
-              Node(node_type=OPERATION, value=O(m=0, d=3, j=2, p=1), id=7),
-              Node(node_type=OPERATION, value=O(m=1, d=2, j=2, p=2), id=8)],
-             <NodeType.SOURCE: 5>: [Node(node_type=SOURCE, value=None, id=9)],
-             <NodeType.SINK: 6>: [Node(node_type=SINK, value=None, id=10)]})
+            {<NodeType.OPERATION: 1>: [Node(node_type=OPERATION, value=O(m=0, d=1, j=0, p=0), id=("OPERATION", 0)),
+              Node(node_type=OPERATION, value=O(m=1, d=1, j=0, p=1), id=("OPERATION", 1)),
+              Node(node_type=OPERATION, value=O(m=2, d=7, j=0, p=2), id=("OPERATION", 2)),
+              Node(node_type=OPERATION, value=O(m=1, d=5, j=1, p=0), id=("OPERATION", 3)),
+              Node(node_type=OPERATION, value=O(m=2, d=1, j=1, p=1), id=("OPERATION", 4)),
+              Node(node_type=OPERATION, value=O(m=0, d=1, j=1, p=2), id=("OPERATION", 5)),
+              Node(node_type=OPERATION, value=O(m=2, d=1, j=2, p=0), id=("OPERATION", 6)),
+              Node(node_type=OPERATION, value=O(m=0, d=3, j=2, p=1), id=("OPERATION", 7)),
+              Node(node_type=OPERATION, value=O(m=1, d=2, j=2, p=2), id=("OPERATION", 8))],
+             <NodeType.SOURCE: 5>: [Node(node_type=SOURCE, value=None, id=('SOURCE', 0))],
+             <NodeType.SINK: 6>: [Node(node_type=SINK, value=None, id=('SINK', 0))]})
 ```
 
 Other attributes include:

docs/source/examples/05-Load-Benchmark-Instances.ipynb

@@ -303,6 +303,7 @@
    ],
    "source": [
     "import numpy as np\n",
+    "\n",
     "np.array(ft06.durations_matrix)"
    ]
   },

docs/source/examples/09-SingleJobShopGraphEnv.ipynb

@@ -49,9 +49,7 @@
     "    feature_observer_configs=feature_observer_configs,\n",
     "    reward_function_config=DispatcherObserverConfig(IdleTimeReward),\n",
     "    render_mode=\"human\",  # Try \"save_video\"\n",
-    "    render_config={\n",
-    "        \"video_config\": {\"fps\": 4}\n",
-    "    }\n",
+    "    render_config={\"video_config\": {\"fps\": 4}},\n",
     ")"
    ]
   },
@@ -252,7 +250,9 @@
     "import numpy as np\n",
     "\n",
     "rewards = np.array(env.reward_function.rewards)\n",
-    "print(f\"{len(list(filter(lambda x: x != 0, rewards)))} zeros out of {len(rewards)}\")"
+    "print(\n",
+    "    f\"{len(list(filter(lambda x: x != 0, rewards)))} zeros out of {len(rewards)}\"\n",
+    ")"
    ]
   },
   {

docs/source/examples/12-ReadyOperationsFilter.ipynb

@@ -375,7 +375,13 @@
     "    df = print_makespans_for_optimizations(ta01, pdr)\n",
     "    print()\n",
     "    # Chamge the number of xticks to 10\n",
-    "    df.sort_values(by=\"Makespan\", ascending=False).plot.barh(x=\"Filter Combination\", y=\"Makespan\", title=pdr.name, xlim=(1500, 2000), xticks=range(1500, 2001, 50))\n",
+    "    df.sort_values(by=\"Makespan\", ascending=False).plot.barh(\n",
+    "        x=\"Filter Combination\",\n",
+    "        y=\"Makespan\",\n",
+    "        title=pdr.name,\n",
+    "        xlim=(1500, 2000),\n",
+    "        xticks=range(1500, 2001, 50),\n",
+    "    )\n",
     "    dfs.append(df)"
    ]
   },
@@ -408,7 +414,9 @@
    "source": [
     "df_combined = pd.concat(dfs, keys=[pdr.name for pdr in DispatchingRuleType])\n",
     "\n",
-    "df_combined.groupby(\"Filter Combination\").agg(\"mean\").sort_values(by=\"Makespan\", ascending=False).plot.barh(title=\"Average Makespan\")"
+    "df_combined.groupby(\"Filter Combination\").agg(\"mean\").sort_values(\n",
+    "    by=\"Makespan\", ascending=False\n",
+    ").plot.barh(title=\"Average Makespan\")"
    ]
   },
   {

job_shop_lib/__init__.py

@@ -19,7 +19,7 @@
 from job_shop_lib._base_solver import BaseSolver, Solver
 
 
-__version__ = "1.6.1"
+__version__ = "1.7.0"
 
 __all__ = [
     "Operation",

job_shop_lib/dispatching/feature_observers/_dates_observer.py

@@ -135,8 +135,7 @@ def update(self, scheduled_operation: ScheduledOperation):
         elapsed_time = current_time - self._previous_current_time
         self._previous_current_time = current_time
         cols = [
-            self._attribute_map[attr]
-            for attr in self.attributes_to_observe
+            self._attribute_map[attr] for attr in self.attributes_to_observe
         ]
         self.features[FeatureType.OPERATIONS][:, cols] -= elapsed_time
 

job_shop_lib/graphs/_build_disjunctive_graph.py

@@ -81,8 +81,12 @@ def build_solved_disjunctive_graph(schedule: Schedule) -> JobShopGraph:
                 break
             next_scheduled_operation = machine_schedule[i + 1]
             graph.add_edge(
-                scheduled_operation.operation.operation_id,
-                next_scheduled_operation.operation.operation_id,
+                graph.get_operation_node(
+                    scheduled_operation.operation.operation_id
+                ),
+                graph.get_operation_node(
+                    next_scheduled_operation.operation.operation_id
+                ),
                 type=EdgeType.DISJUNCTIVE,
             )