까다로운 계획 문제,
이제 바로 풀립니다.

$ pip install optimaru

from optimaru import vehicle_routing as vrp
from optimaru.vehicle_routing.constraints import (
    get_constraints, VehicleCapacityCost, UnassignedVisitsCost,
    TravelTimeCost, MaxRouteDurationCost,
)

# One depot in Seoul and four customers: (latitude, longitude).
locations = [vrp.Location(*p) for p in [
    (37.5665, 126.9780),  # 0: depot
    (37.5796, 126.9770), (37.5512, 126.9882),
    (37.5443, 127.0557), (37.5133, 127.1001),
]]
vehicles = [vrp.Vehicle(f"V{i}", capacity=10, home_location_index=0,
                        departure_time=9 * 3600, max_route_duration=4 * 3600)
            for i in (1, 2)]
visits = [vrp.Visit(f"C{i}", f"Customer {i}", location_index=i, demand=4,
                    min_start_time=9 * 3600, max_end_time=18 * 3600,
                    service_duration=600)
          for i in range(1, 5)]
plan = vrp.VehicleRoutePlan("demo", locations, vehicles, visits)

config = vrp.SolverConfig()
config.spent_limit_seconds = 5
solver = vrp.Solver(config)

# Plug in only the rules you need, weighted your way.
solver.solve_with_constraints(plan, get_constraints([
    VehicleCapacityCost(),
    UnassignedVisitsCost(),
    TravelTimeCost(weight=10),
    MaxRouteDurationCost(level="hard"),
]))

print(plan.score)     # 0hard/0medium/-12430soft
print(plan.routes())  # {'V1': ['C3', 'C4'], 'V2': ['C1', 'C2']}