Intralogistics examples

Three runnable scripts in examples/ form a progressive learning path --- from a minimal setup to a full-shift warehouse operation.

Simple

File: examples/intralogistics_simple.py

The minimal starting point. Uses build_simple_system() to create a 5-node linear graph with two warehouses and two AGVs. Submits four transfer orders and prints results.

graph LR
    WH_A --- N1 --- N2 --- N3 --- WH_B

What it demonstrates:

• Using build_simple_system() for quick setup
• Creating orders with coordinator.create_order() and coordinator.submit()
• Reading results: order status, inventory levels, fleet utilization

Run it:

uv run python examples/intralogistics_simple.py

Intermediate

File: examples/intralogistics_intermediate.py

A manufacturing plant with Raw Materials and Finished Goods warehouses connected by a corridor with production floor branches. Builds everything manually --- no builder functions.

graph LR
    RM_IN --- RM_OUT --- C1 --- C2 --- C3 --- FG_IN --- FG_OUT
    C1 --- P[P<br>Parking]
    C1 --- PROD_A
    C3 --- PROD_B
    PROD_A -->|one-way| PROD_B

All corridor arcs are bidirectional. PROD_A → PROD_B is one-way, providing a forward bypass when the main corridor is busy.

What it demonstrates:

• Custom graph construction --- manual Node, Arc, and LayoutGraph instead of a builder
• Multiple SKUs --- Steel Sheets (80 kg), Plastic Pellets (15 kg), Electronics (2 kg) with weight/volume capacity constraints
• Dispatch strategy --- NearestIdleStrategy selects the closest idle AGV for each order
• Parking and repositioning --- ParkingArea at node P with NearestParkingPolicy sending idle AGVs back to parking
• Staggered order batches --- three batches at t=0, t=30 min, and t=60 min to show queuing dynamics
• Time-series plots --- DefaultIntralogisticsCollector with plot_fleet_utilization() and plot_pending_orders()

Key configuration:

# 3 AGVs with trapezoidal speed profile
speed_profile = TrapezoidalProfile(max_speed=1.5, acceleration=0.8, deceleration=1.0)
agv_type = AGVType(
    name="standard",
    speed_profile=speed_profile,
    battery_capacity=200.0,
    weight_capacity=100.0,
    volume_capacity=3.0,
    ...
)

# 8 orders across 3 batches, all Raw Materials -> Finished Goods
batches = [
    (0,    [(steel, 1), (plastic, 2), (electronics, 3)]),
    (1800, [(steel, 1), (plastic, 3), (plastic, 2)]),
    (1800, [(electronics, 3), (steel, 1)]),
]

Sample output:

Manufacturing Plant Floor — Intermediate Example
Layout: 10 nodes, 10 arcs
Fleet: 3 AGVs
Simulation time: 7200s (120 min)
Orders: 8 submitted, 8 completed, 0 failed
Avg fulfillment time: 151.2s

Run it:

uv run python examples/intralogistics_intermediate.py

Advanced

File: examples/intralogistics_advanced.py

A distribution hub with three warehouses --- Receiving (inbound), Bulk Storage (central), and Dispatch (outbound) --- running a full 8-hour shift. Goods flow inbound via automatic replenishment and outbound via continuous customer orders.

graph LR
    RCV_IN --- RCV_OUT --- R1 --- R2 --- BULK_IN
    R2 --- CHRG[CHRG<br>Charger] --- PARK[PARK<br>Parking]
    R2 --- B2
    BULK_OUT --- B1 --- B2 --- B3 --- DSP_IN --- DSP_OUT
    B2 -->|one-way| B4 --> B5 --> B6 -->|one-way| B3

Main corridors are bidirectional. The B2 → B4 → B5 → B6 → B3 alternate route is one-way, giving the pathfinder a forward bypass option.

What it demonstrates:

• 3-warehouse topology --- Receiving, Bulk Storage, and Dispatch with separate input/output bays
• Battery lifecycle --- TrapezoidalProfile with battery_degradation_fn (speed drops below 30% charge) and load_speed_factor_fn (heavier loads = slower)
• Charging station --- ChargingStation at CHRG with 2 slots; AGVs charge automatically when battery is low
• Automatic replenishment --- ReorderPointPolicy monitors Bulk Storage inventory and triggers transfers from Receiving when stock drops below thresholds
• Round-robin dispatch --- RoundRobinStrategy cycles through idle AGVs for balanced fleet utilization
• Load recovery --- ReturnToOrigin returns cargo to the origin warehouse if an AGV is interrupted
• EMA metrics --- EMAOrderMetrics tracks fulfillment time, dispatch delay, travel times (empty/loaded), and late order rate
• 4 time-series plots --- fleet utilization, throughput, pending orders, and inventory levels over time

Key configuration:

# 5 AGVs with battery degradation and load-dependent speed
speed_profile = TrapezoidalProfile(
    max_speed=2.0, acceleration=0.8, deceleration=1.0,
    battery_degradation_fn=lambda level: 1.0 if level >= 0.3 else 0.7 + level,
    load_speed_factor_fn=lambda weight: max(0.5, 1.0 - weight / 300),
)

# Automatic replenishment when Bulk Storage drops below thresholds
replenishment = ReorderPointPolicy(
    thresholds={pallet_a: 10, pallet_b: 10, ...},
    reorder_quantity={pallet_a: 1, pallet_b: 1, pallet_c: 5, ...},
)
coordinator.add_replenishment_policy(replenishment, bulk_storage)

# Continuous outbound orders at random intervals (5-10 min)
def outbound_order_stream(env, coordinator, ...):
    while True:
        yield env.timeout(rng.uniform(300, 600))
        ...
        coordinator.submit(order)

Sample output:

Multi-Warehouse Distribution Hub — Advanced Example
Layout: 16 nodes, 16 arcs
Fleet: 5 AGVs
Simulation time: 28800s (480 min)

Shift summary:
  Total orders: 62 (61 outbound, 1 replenishment)
  Completed: 62, Failed: 0
  Outbound:      61 completed, 0 failed
  Replenishment: 1 completed, 0 failed
  Avg outbound fulfillment time: 220.7s (3.7 min)

Run it:

uv run python examples/intralogistics_advanced.py

Feature progression