def test_product_path_solver_integration(full_problem):
max_flight_capacity = mean(drone.max_payload for drone in full_problem.drones)
customers = full_problem.get_customers()[:20]
products = full_problem.products[:20]
hubs = full_problem.warehouses[:10]
solve_product_trips = SolveProductTrips(
customers=customers,
hubs=hubs,
products=products,
max_flight_capacity=max_flight_capacity,
environment=full_problem.get_environment(),
)
product_trips = solve_product_trips.solve(Mip_Solver=MipSolver, max_seconds=60)
assert product_trips
customer_demand = {
customer: {
your_key: customer.demand[your_key]
for your_key in products
if your_key in customer.demand
}
for customer in customers
}
# apply trips to customers to see if everything is delivered
for trip in product_trips["hub_to_customer"]:
customer_demand[trip.destination][trip.product_type] -= trip.product_quantity
for customer, demanded_products in customer_demand.items():
for product, still_need_to_be_delivered in demanded_products.items():
assert still_need_to_be_delivered == 0
def test_get_distance(full_problem):
environment = full_problem.get_environment()
place1 = Location(x=10, y=40)
place2 = Location(x=15, y=50)
distance = environment.get_distance(place1, place2)
assert distance == 12
def test_warehouses_in_environment(full_problem):
environment = full_problem.get_environment()
for warehouse in full_problem.warehouses:
assert warehouse in environment
def test_get_nearest_warehouse(full_problem):
environment = full_problem.get_environment()
place1 = Mock()
place1.location = Location(x=10, y=30)
nearest_warehouse = environment.get_nearest_warehouse(place1)
assert isinstance(nearest_warehouse, WareHouse)
def test_get_all_locations(full_problem):
environment = full_problem.get_environment()
assert (len(list(
environment.get_all_locations())) == full_problem.grid.n_x *
full_problem.grid.n_y)
def test_orders_in_environment(full_problem):
environment = full_problem.get_environment()
for order in full_problem.orders:
assert order in environment