"""Multi-vehicle VRP example demonstrating Phase 1 functionality.
This example shows how to solve vehicle routing problems with multiple vehicles,
capacity constraints, and different optimization objectives.
"""
import asyncio
from chuk_mcp_solver.server import solve_routing_problem
async def example_1_basic_vrp():
"""Example 1: Basic multi-vehicle routing with capacity constraints."""
print("\n" + "=" * 70)
print("Example 1: Basic Multi-Vehicle VRP")
print("=" * 70)
print("\nProblem: Deliver to 5 customers using 2 trucks with capacity limits")
locations = [
{"id": "warehouse", "coordinates": (0, 0), "demand": 0},
{"id": "customer_1", "coordinates": (10, 5), "demand": 15, "service_time": 10},
{"id": "customer_2", "coordinates": (5, 10), "demand": 20, "service_time": 15},
{"id": "customer_3", "coordinates": (15, 15), "demand": 25, "service_time": 10},
{"id": "customer_4", "coordinates": (20, 10), "demand": 10, "service_time": 5},
{"id": "customer_5", "coordinates": (12, 3), "demand": 18, "service_time": 12},
]
vehicles = [
{
"id": "truck_1",
"capacity": 50,
"start_location": "warehouse",
"cost_per_distance": 1.5,
"fixed_cost": 50.0,
},
{
"id": "truck_2",
"capacity": 40,
"start_location": "warehouse",
"cost_per_distance": 1.2,
"fixed_cost": 40.0,
},
]
response = await solve_routing_problem(
locations=locations,
vehicles=vehicles,
objective="minimize_distance",
max_time_ms=20000,
)
_print_solution(response)
async def example_2_minimize_vehicles():
"""Example 2: Minimize number of vehicles used."""
print("\n" + "=" * 70)
print("Example 2: Minimize Fleet Size")
print("=" * 70)
print("\nProblem: Use as few vehicles as possible to serve all customers")
locations = [
{"id": "depot", "coordinates": (0, 0), "demand": 0},
{"id": "c1", "coordinates": (5, 0), "demand": 10},
{"id": "c2", "coordinates": (10, 0), "demand": 10},
{"id": "c3", "coordinates": (15, 0), "demand": 15},
{"id": "c4", "coordinates": (20, 0), "demand": 12},
]
vehicles = [
{"id": "truck_A", "capacity": 30, "start_location": "depot", "fixed_cost": 100.0},
{"id": "truck_B", "capacity": 30, "start_location": "depot", "fixed_cost": 100.0},
{"id": "truck_C", "capacity": 30, "start_location": "depot", "fixed_cost": 100.0},
]
response = await solve_routing_problem(
locations=locations,
vehicles=vehicles,
objective="minimize_vehicles",
max_time_ms=15000,
)
_print_solution(response)
async def example_3_minimize_cost():
"""Example 3: Minimize total cost (fixed + variable)."""
print("\n" + "=" * 70)
print("Example 3: Minimize Total Cost")
print("=" * 70)
print("\nProblem: Balance fixed vehicle costs with distance costs")
locations = [
{"id": "warehouse", "coordinates": (0, 0), "demand": 0},
{"id": "store_1", "coordinates": (10, 0), "demand": 20},
{"id": "store_2", "coordinates": (20, 0), "demand": 25},
{"id": "store_3", "coordinates": (30, 0), "demand": 15},
]
vehicles = [
{
"id": "small_van",
"capacity": 30,
"start_location": "warehouse",
"cost_per_distance": 0.8,
"fixed_cost": 30.0,
},
{
"id": "large_truck",
"capacity": 70,
"start_location": "warehouse",
"cost_per_distance": 1.5,
"fixed_cost": 100.0,
},
]
response = await solve_routing_problem(
locations=locations,
vehicles=vehicles,
objective="minimize_cost",
max_time_ms=15000,
)
_print_solution(response)
async def example_4_capacity_infeasible():
"""Example 4: Demonstrate infeasible problem (demand exceeds capacity)."""
print("\n" + "=" * 70)
print("Example 4: Infeasible Problem (Capacity Exceeded)")
print("=" * 70)
print("\nProblem: Customer demand exceeds all vehicle capacities")
locations = [
{"id": "depot", "coordinates": (0, 0), "demand": 0},
{"id": "bulk_order", "coordinates": (10, 0), "demand": 100}, # Too large!
]
vehicles = [
{"id": "small_truck", "capacity": 50, "start_location": "depot"},
{"id": "medium_truck", "capacity": 60, "start_location": "depot"},
]
response = await solve_routing_problem(
locations=locations,
vehicles=vehicles,
objective="minimize_distance",
max_time_ms=5000,
)
_print_solution(response)
def _print_solution(response):
"""Pretty-print routing solution."""
print(f"\n{'Status:':<20} {response.status}")
if response.status in ("optimal", "feasible", "timeout_best"):
print(f"{'Vehicles Used:':<20} {response.vehicles_used}")
print(f"{'Total Distance:':<20} {response.total_distance}")
print(f"{'Total Time:':<20} {response.total_time}")
print(f"{'Total Cost:':<20} ${response.total_cost:.2f}")
print(f"{'Solve Time:':<20} {response.solve_time_ms}ms")
if response.optimality_gap:
print(f"{'Optimality Gap:':<20} {response.optimality_gap:.2f}%")
print("\nRoutes:")
for i, route in enumerate(response.routes, 1):
print(f"\n Route {i} - {route.vehicle_id}:")
print(f" Sequence: {' → '.join(route.sequence)}")
print(f" Distance: {route.total_distance}")
print(f" Time: {route.total_time}")
print(f" Cost: ${route.total_cost:.2f}")
if route.load_timeline:
print(" Load Timeline:")
for loc, load in route.load_timeline:
print(f" {loc}: {load} units")
if response.explanation:
print(f"\nExplanation: {response.explanation.summary}")
else:
# Infeasible, error, or timeout
print(
f"\n{'Explanation:':<20} {response.explanation.summary if response.explanation else 'No solution found'}"
)
if response.explanation and response.explanation.recommendations:
print("\nRecommendations:")
for rec in response.explanation.recommendations:
print(f" • {rec}")
async def main():
"""Run all VRP examples."""
print("\n" + "=" * 70)
print("MULTI-VEHICLE VRP EXAMPLES")
print("=" * 70)
await example_1_basic_vrp()
await example_2_minimize_vehicles()
await example_3_minimize_cost()
await example_4_capacity_infeasible()
print("\n" + "=" * 70)
print("All examples completed!")
print("=" * 70)
if __name__ == "__main__":
asyncio.run(main())
