Or-Tools Solution Analysis

Co-authored-by: Daniel Monteiro <dfamonteiro@users.noreply.github.com>
Co-authored-by: Gonçalo Pascoal <goncalojpascoal@gmail.com>

scripts/analysis2.py

@@ -0,0 +1,49 @@
+import time
+from typing import Tuple, List
+
+from or_tools_solution import cvrp
+
+class Solver:
+    def solve(distance_matrix_path: str, cvrp_problem_path: str, **args) -> Tuple[float, List[List[int]], float]:
+        """Returns the time it took to solve (in seconds), list of paths, total travelled distance"""
+        raise Exception("Not implemented")
+
+
+class OrToolsSolver(Solver):
+    def solve(distance_matrix_path: str, cvrp_problem_path: str) -> Tuple[float, List[List[int]], float]:
+        start_time = time.time()
+        (data, manager, routing, solution) = cvrp(distance_matrix_path, cvrp_problem_path, 1)
+        end_time = time.time()
+        total_distance = 0
+        routes = []
+        for vehicle_id in range(len(data['distance_matrix'])):
+            route = []
+            index = routing.Start(vehicle_id)
+            while not routing.IsEnd(index):
+                node_index = manager.IndexToNode(index)
+                route.append(node_index)
+                previous_index = index
+                index = solution.Value(routing.NextVar(index))
+                total_distance += routing.GetArcCostForVehicle(
+                    previous_index, index, vehicle_id) / 100
+            routes.append(route)
+        return (end_time - start_time, routes, total_distance)
+
+class SimulatedAnnealing(Solver):
+    def solve(distance_matrix_path: str, cvrp_problem_path: str) -> Tuple[float, List[List[int]], float]:
+        pass
+
+class Greedy(Solver):
+    def solve(distance_matrix_path: str, cvrp_problem_path: str) -> Tuple[float, List[List[int]], float]:
+        pass
+
+class TabuSearch(Solver):
+    def solve(distance_matrix_path: str, cvrp_problem_path: str) -> Tuple[float, List[List[int]], float]:
+        pass
+
+class AntColony(Solver):
+    def solve(distance_matrix_path: str, cvrp_problem_path: str) -> Tuple[float, List[List[int]], float]:
+        pass
+
+if __name__=="__main__":
+    pass

scripts/or_tools_solution.py

@@ -6,16 +6,16 @@
 import argparse
 import json
 
-def create_data_model(args):
+def create_data_model(distance_matrix_path, cvrp_problem_path):
     """Stores the data for the problem."""
     data = {}
 
     lines = []
-    with open(args.dist_filename) as file:
+    with open(distance_matrix_path) as file:
         lines = file.readlines()
     data['distance_matrix'] = [[int(round(float(element) * 100, 0)) for element in line.split(' ')[:-1]] for line in lines]
 
-    with open(args.cvrp_filename) as file:
+    with open(cvrp_problem_path) as file:
         cvrp_info = json.load(file)
     data['demands'] = [0] + [delivery['size'] for delivery in cvrp_info['deliveries']]
     data['vehicle_capacity'] = cvrp_info['vehicle_capacity']
@@ -62,9 +62,16 @@ def main():
     parser.add_argument('dist_filename', type=str, help='path for the file with the distance matrix')
 
     args = parser.parse_args()
+    (data, manager, routing, solution) = cvrp(args.dist_filename, args.cvrp_filename)
 
+    # Print solution on console.
+    if solution:
+        print_solution(data, manager, routing, solution)
+
+
+def cvrp(distance_matrix_path, cvrp_problem_path, time_limit = 1):
     # Instantiate the data problem.
-    data = create_data_model(args)
+    data = create_data_model(distance_matrix_path, cvrp_problem_path)
 
     # Create the routing index manager.
     manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']), len(data['distance_matrix']), 0)
@@ -106,14 +113,11 @@ def demand_callback(from_index):
     search_parameters = pywrapcp.DefaultRoutingSearchParameters()
     search_parameters.first_solution_strategy = (routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
     search_parameters.local_search_metaheuristic = (routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
-    search_parameters.time_limit.FromSeconds(1)
+    search_parameters.time_limit.FromSeconds(time_limit)
 
     # Solve the problem.
     solution = routing.SolveWithParameters(search_parameters)
-
-    # Print solution on console.
-    if solution:
-        print_solution(data, manager, routing, solution)
+    return (data, manager, routing, solution)
 
 
 if __name__ == '__main__':