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AI4Finance-Foundation · Nov 20, 2024 · 08fb6cd · 08fb6cd
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diff --git a/rlsolver/methods/VRPTW_algs/ESPPRC_demo.py b/rlsolver/methods/VRPTW_algs/ESPPRC_demo.py
@@ -4,13 +4,11 @@
 import copy
 from typing import List, Tuple, Set, Dict
 
-
 class Label:
     path = []
     travel_time = 0
     dist = 0
 
-
 # dominance rule
 def dominate(labels: List[Label], path_dict: Dict[int, Label]):
     labels_copy = copy.deepcopy(labels)
@@ -37,6 +35,8 @@ def dominate(labels: List[Label], path_dict: Dict[int, Label]):
     return labels, path_dict
 
 
+
+
 # labeling algorithm
 def labeling_SPPRC(graph, orig, dest):
     # initial Q
@@ -135,6 +135,6 @@ def main():
     print('optimal path : ', opt_path[1].path)
     print('optimal path (dist): ', opt_path[1].dist)
 
-
 if __name__ == '__main__':
     main()
+
diff --git a/rlsolver/methods/VRPTW_algs/ESPPRC_demo2.py b/rlsolver/methods/VRPTW_algs/ESPPRC_demo2.py
@@ -4,7 +4,6 @@
 import copy
 from typing import List, Tuple, Set, Dict
 
-
 class Label:
     path = []
     duration = 0
@@ -43,7 +42,6 @@ def make_unique(labels) -> List:
                 res.append(labels[i])
         return res
 
-
 # dominance rule
 def EFF(labels2: List[Label]):
     labels = Label.make_unique(labels2)
@@ -77,6 +75,8 @@ def EFF(labels2: List[Label]):
     return filtered_labels
 
 
+
+
 # labeling algorithm
 def labeling_SPPRC(graph, orig, dest):
     # initial Q
@@ -175,6 +175,6 @@ def main():
         print(f'The {i + 1}-th optimal path (time): {opt_label.duration}')
     print()
 
-
 if __name__ == '__main__':
     main()
+
diff --git a/rlsolver/methods/VRPTW_algs/ESPPRC_demo3.py b/rlsolver/methods/VRPTW_algs/ESPPRC_demo3.py
@@ -4,6 +4,7 @@
 rlsolver_path = os.path.join(cur_path, '../../../rlsolver')
 sys.path.append(os.path.dirname(rlsolver_path))
 
+import numpy as np
 import networkx as nx
 import copy
 from typing import List, Tuple, Union, Dict

diff --git a/rlsolver/methods/VRPTW_algs/Label.py b/rlsolver/methods/VRPTW_algs/Label.py
@@ -126,7 +126,6 @@ def dominate(self, another, forward: bool):
                 return False
 
     @staticmethod
-    # keep non-dominated labels
     def EFF(labels2: List, forward: bool) -> List:
         labels = Label.make_unique(labels2)
         if len(labels) <= 1:

diff --git a/rlsolver/methods/VRPTW_algs/Vehicle.py b/rlsolver/methods/VRPTW_algs/Vehicle.py
@@ -1,6 +1,5 @@
 import sys
 import os
-
 cur_path = os.path.dirname(os.path.abspath(__file__))
 rlsolver_path = os.path.join(cur_path, '../../../rlsolver')
 sys.path.append(os.path.dirname(rlsolver_path))

diff --git a/rlsolver/methods/VRPTW_algs/impact_heuristic.py b/rlsolver/methods/VRPTW_algs/impact_heuristic.py
@@ -1,7 +1,6 @@
 # The impact heuristic is implemented based on the paper "A greedy look-ahead heuristic for the vehicle routing problem with time windows"
 import sys
 import os
-
 cur_path = os.path.dirname(os.path.abspath(__file__))
 rlsolver_path = os.path.join(cur_path, '../../../rlsolver')
 sys.path.append(os.path.dirname(rlsolver_path))
@@ -13,19 +12,19 @@
 import networkx as nx
 
 from rlsolver.methods.VRPTW_algs.Customer import (Customer,
-                                                  )
+                      )
 from rlsolver.methods.VRPTW_algs.Vehicle import Vehicle
 from rlsolver.methods.VRPTW_algs.util import (read_data,
-                                              read_data_as_nxdigraph,
-                                              generate_vehicles,
-                                              generate_vehicles_and_assign_paths,
-                                              obtain_paths_based_on_vehicles,
-                                              calc_demands_of_paths,
-                                              calc_durations_of_paths,
-                                              calc_dists_of_paths,
-                                              write_result,
-                                              write_result_based_on_vehicles,
-                                              )
+                  read_data_as_nxdigraph,
+                  generate_vehicles,
+                  generate_vehicles_and_assign_paths,
+                  obtain_paths_based_on_vehicles,
+                  calc_demands_of_paths,
+                  calc_durations_of_paths,
+                  calc_dists_of_paths,
+                  write_result,
+                  write_result_based_on_vehicles,
+                  )
 
 from rlsolver.methods.VRPTW_algs.config import Config
 

diff --git a/rlsolver/methods/VRPTW_algs/util.py b/rlsolver/methods/VRPTW_algs/util.py
@@ -398,6 +398,30 @@ def filter_vehicles_based_on_paths(vehicles2: List[Vehicle], paths: List[str], c
             vehicle.path_denoted_by_customers.append(this)
     return vehicles
 
+    # vehicles = generate_vehicles(num_vehicles, customers)
+    # res = []
+    # dest = Customer.obtain_by_name(Config.DEST_NAME, customers)
+    # if dest is None:
+    #     return []
+    # dest.labels.sort(key=operator.attrgetter('cumulative_travel_cost'))
+    # num = min(len(dest.labels), len(vehicles))
+    # for i in range(num):
+    #     label = dest.labels[i]
+    #     vehicle = vehicles[i]
+    #     vehicle.path_denoted_by_names = label.path_denoted_by_names
+    #     vehicle.departure_time_list = label.departure_time_list
+    #     vehicle.arrival_time_list = label.arrival_time_list
+    #     vehicle.departure_time_dict = {}
+    #     vehicle.arrival_time_dict = {}
+    #     vehicle.path_denoted_by_customers = []
+    #     for k in range(len(vehicle.path_denoted_by_names)):
+    #         this_name = vehicle.path_denoted_by_names[k]
+    #         customer = Customer.obtain_by_name(this_name, customers)
+    #         vehicle.path_denoted_by_customers.append(customer)
+    #         vehicle.departure_time_dict[this_name] = vehicle.departure_time_list[k]
+    #         vehicle.arrival_time_dict[this_name] = vehicle.arrival_time_list[k]
+    #     res.append(vehicle)
+    # return res
 
 def obtain_var_vals(model, var_name: str):
     theta_vals = []