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Unit Tests for AETERNA AI Operational Logistics Engine
Validates transparent fleet sizing, crew allocation, throughput-based collection duration,
operational efficiency, and zero/large volume edge cases.
"""
import pytest
import math
from services.logistics_engine import (
LOGISTICS_CONFIG,
calculate_fleet_requirements,
calculate_manpower_requirements,
calculate_collection_time,
calculate_operational_efficiency_score,
calculate_forecast_reliability_score,
calculate_full_logistics_plan
)
def test_1_fleet_calculation():
"""
Test 1: Fleet Calculation for forecast = 1135.63 ton.
Verifies dimensional correctness and transparent formula:
effective_capacity = 15.0 * 0.95 = 14.25 ton
base_trucks = ceil(1135.63 / 14.25) = 80
recommended_trucks = ceil(80 * 1.05) = 84
required_truck_loads = 1135.63 / 15.0 = 75.71
"""
forecast = 1135.63
fleet = calculate_fleet_requirements(forecast)
assert fleet["truck_capacity_ton"] == 15.0
assert fleet["load_factor"] == 0.95
assert fleet["effective_capacity_ton"] == 14.25
assert fleet["base_trucks"] == 80
assert fleet["operational_buffer_percent"] == 5.0
assert fleet["recommended_trucks"] == 84
assert fleet["required_truck_loads"] == 75.71
def test_2_manpower_calculation():
"""
Test 2: Manpower staffing derived directly from recommended fleet.
Standard crew: 1 driver + 2 collectors = 3 personnel per truck.
"""
# Test with 80 base trucks
manpower_80 = calculate_manpower_requirements(80)
assert manpower_80["drivers"] == 80
assert manpower_80["collectors"] == 160
assert manpower_80["total_personnel"] == 240
assert manpower_80["crew_per_truck"] == 3
# Test with 84 recommended trucks
manpower_84 = calculate_manpower_requirements(84)
assert manpower_84["drivers"] == 84
assert manpower_84["collectors"] == 168
assert manpower_84["total_personnel"] == 252
assert manpower_84["total_personnel"] == 84 * 3
def test_3_collection_time_uses_throughput_not_volume_div_capacity():
"""
Test 3: Collection time MUST use fleet throughput, NOT volume / truck_capacity.
Volume / 15 gives 75.7 (which is truck loads, NOT hours).
Throughput = recommended_trucks * collection_rate_ton_per_hour (2.0 T/h).
"""
forecast = 1135.63
truck_capacity = 15.0
faulty_time = forecast / truck_capacity # 75.70866...
rec_trucks = 84
collection = calculate_collection_time(
forecast_volume_ton=forecast,
recommended_trucks=rec_trucks,
collection_rate_per_truck=2.0,
rainfall_mm=0.0,
has_event=False,
operational_efficiency=0.98
)
# Collection time must NOT equal faulty volume / capacity
assert collection["adjusted_hours"] != round(faulty_time, 1)
assert collection["raw_hours"] != round(faulty_time, 1)
# Dimensional verification:
# fleet_throughput = 84 * 2.0 = 168.0 ton/hour
# raw_hours = 1135.63 / 168.0 = 6.7597... ~ 6.8 hours
assert collection["raw_hours"] == 6.8
# adjusted_hours = (6.7597 * 1.1) / 0.98 = 7.587... ~ 7.6 hours
assert collection["adjusted_hours"] == 7.6
def test_4_zero_forecast_handling():
"""
Test 4: Zero forecast volume edge case.
If forecast_volume = 0, trucks = 0, crew = 0, collection time = 0.
"""
plan = calculate_full_logistics_plan(0.0)
assert plan["forecast_volume_ton"] == 0.0
assert plan["trucks_needed"] == 0
assert plan["manpower"] == 0
assert plan["estimated_duration_hours"] == 0.0
assert plan["required_truck_loads"] == 0.0
fleet = plan["recommended_fleet"]
assert fleet["base_trucks"] == 0
assert fleet["recommended_trucks"] == 0
assert fleet["required_truck_loads"] == 0.0
crew = plan["manpower_breakdown"]
assert crew["drivers"] == 0
assert crew["collectors"] == 0
assert crew["total_personnel"] == 0
def test_5_large_volume_scaling():
"""
Test 5: Large volume handling without hardcoded limits.
For citywide 10,000 tons surge, all formulas scale consistently.
"""
large_volume = 10000.0
plan = calculate_full_logistics_plan(large_volume)
# Effective capacity = 14.25
expected_base_trucks = math.ceil(10000.0 / 14.25) # 702
expected_rec_trucks = math.ceil(expected_base_trucks * 1.05) # 738
expected_personnel = expected_rec_trucks * 3 # 2214
assert plan["recommended_fleet"]["base_trucks"] == expected_base_trucks
assert plan["recommended_fleet"]["recommended_trucks"] == expected_rec_trucks
assert plan["manpower"] == expected_personnel
assert plan["required_truck_loads"] == round(10000.0 / 15.0, 2)
assert plan["estimated_duration_hours"] > 0.0
assert plan["operational_efficiency"]["score_percent"] > 0.0
def test_6_operational_factors_adjustment():
"""
Test 6: Real-world adjustments (rain, traffic, events) affect collection duration.
Heavy rain (>25mm) and event crowd increase estimated duration.
"""
base_plan = calculate_collection_time(
forecast_volume_ton=1000.0,
recommended_trucks=50,
rainfall_mm=0.0,
has_event=False,
operational_efficiency=0.85
)
rain_event_plan = calculate_collection_time(
forecast_volume_ton=1000.0,
recommended_trucks=50,
rainfall_mm=30.0, # Heavy rain (factor 1.20)
has_event=True, # Event crowd (factor 1.10)
operational_efficiency=0.85
)
assert rain_event_plan["adjusted_hours"] > base_plan["adjusted_hours"]
assert rain_event_plan["factors"]["weather_factor"] == 1.20
assert rain_event_plan["factors"]["event_factor"] == 1.10
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