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FA-67806 / Elevator dispatch scheduling / Open access

Energy saving car shutdown: timer restart after shutdown · case 01

Cars are shut down on every quiet sample after the first shutdown.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The low-demand timer is not restarted after a shutdown.

THE FAILURE

The low-demand timer is not restarted after a shutdown.

Unsuccessful approach: Clearing the timer delays the next shutdown by an extra sample.

Case contract

Samples [t, demand, max wait]. If the max wait exceeds 45 s and some car is shut down, one car is reactivated and the low-demand timer cleared. Otherwise, while demand < low_demand, a timer runs from the first low sample; once it has run 600 s or more and more than min_active cars are active, one car shuts down and the timer restarts at that time. Any sample with normal demand clears the timer. Output active cars per sample.

Why this case matters

Lift group controllers make these decisions many times per minute; a wrong answer strands passengers, wastes trips or overrides a safety rule.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    active = x['cars']
    low_since = None
    out = []
    for t, demand, wait in x['samples']:
        if wait > 45 and active < x['cars']:
            active += 1
            low_since = None
        elif demand < x['low_demand']:
            if low_since is None:
                low_since = t
            elif t - low_since >= 600 and active > x['min_active']:
                active -= 1
        else:
            low_since = None
        out.append(active)
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: successive shutdowns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [900, 1, 10], [1200, 1, 10]]}, [4, 3, 3, 2]), ('sampled regression 28', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[600, 6, 10], [1200, 0, 10], [1800, 0, 30], [2100, 2, 45], [2400, 15, 80], [2460, 15, 10], [2520, 4, 46]]}, [3, 3, 2, 2, 3, 3, 3]), ('control 42', {'cars': 6, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 15, 10], [120, 4, 30], [720, 2, 46], [1320, 4, 45]]}, [6, 6, 5, 4]), ('boundary: ten quiet minutes', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10]]}, [4, 3]), ('boundary: minimum active cars', {'cars': 2, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [1200, 1, 10], [1800, 1, 10]]}, [2, 1, 1, 1]), ('control 1', {'cars': 6, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 6, 30], [120, 0, 46], [720, 5, 30], [1320, 4, 30], [1620, 0, 10], [2220, 2, 10], [2520, 5, 46], [2580, 6, 10], [2640, 15, 10], [2700, 15, 10]]}, [6, 6, 6, 6, 6, 5, 6, 6, 6, 6]), ('sampled regression 4', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[300, 5, 45], [900, 6, 10], [1500, 5, 30], [1560, 2, 80], [1860, 2, 45], [2460, 2, 80], [2520, 4, 10], [2820, 6, 80], [3420, 4, 46], [3720, 4, 46]]}, [4, 4, 4, 4, 4, 3, 3, 4, 4, 4]), ('control 7', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[300, 5, 46], [600, 15, 30], [900, 2, 46]]}, [3, 3, 3])], [('regression: successive shutdowns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [900, 1, 10], [1200, 1, 10]]}, [4, 3, 3, 2]), ('sampled regression 36', {'cars': 6, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 6, 46], [660, 0, 10], [1260, 2, 10], [1320, 2, 45], [1920, 4, 46]]}, [6, 6, 5, 5, 6]), ('control 76', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[600, 0, 30], [900, 0, 46], [1500, 0, 80], [2100, 2, 30], [2700, 2, 45], [2760, 0, 30], [3060, 4, 30], [3660, 4, 45], [4260, 4, 46]]}, [4, 4, 3, 2, 1, 1, 1, 1, 2]), ('boundary: minimum active cars', {'cars': 2, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [1200, 1, 10], [1800, 1, 10]]}, [2, 1, 1, 1]), ('boundary: demand returns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [300, 6, 10], [600, 1, 10], [900, 1, 10]]}, [4, 4, 4, 4]), ('control 12', {'cars': 6, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 4, 30], [120, 6, 45], [180, 4, 80], [780, 5, 10], [1080, 5, 80], [1680, 15, 10]]}, [6, 6, 6, 6, 6, 6]), ('control 15', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 2, 46], [660, 6, 10], [960, 15, 10]]}, [4, 4, 4]), ('control 18', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[300, 0, 80], [900, 15, 46], [1200, 15, 30], [1500, 6, 46], [2100, 6, 80], [2700, 6, 46], [3300, 2, 10], [3900, 2, 46], [4200, 15, 30], [4500, 4, 30]]}, [4, 4, 4, 4, 4, 4, 4, 3, 3, 3])], [('regression: successive shutdowns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [900, 1, 10], [1200, 1, 10]]}, [4, 3, 3, 2]), ('sampled regression 40', {'cars': 6, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 4, 45], [1200, 15, 46], [1260, 6, 30], [1320, 4, 10], [1920, 2, 10], [1980, 2, 10], [2040, 15, 46], [2340, 15, 30]]}, [6, 6, 6, 6, 5, 5, 6, 6]), ('control 80', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 4, 45], [660, 5, 80], [960, 4, 46], [1560, 2, 10], [1620, 15, 45], [1920, 0, 46], [1980, 2, 46], [2580, 4, 46], [3180, 0, 30], [3480, 4, 10]]}, [4, 4, 4, 3, 3, 4, 4, 3, 2, 2]), ('boundary: long wait reactivates', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [660, 1, 46], [720, 1, 45]]}, [3, 2, 3, 3]), ('boundary: long wait with all cars active', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [300, 1, 80], [600, 1, 10]]}, [3, 3, 2]), ('control 23', {'cars': 3, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 2, 30], [660, 6, 30], [720, 2, 46], [1320, 5, 10], [1620, 5, 10], [1920, 5, 46], [2520, 5, 45], [2580, 4, 46], [2640, 15, 80], [2940, 6, 30]]}, [3, 3, 3, 3, 3, 3, 3, 3, 3, 3]), ('control 26', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 2, 80], [360, 0, 30], [420, 15, 30], [720, 15, 30], [1320, 15, 10], [1920, 5, 80], [2520, 6, 45], [2820, 0, 10]]}, [4, 4, 4, 4, 4, 4, 4, 4]), ('control 29', {'cars': 6, 'min_active': 1, 'low_demand': 5, 'samples': [[300, 0, 45], [600, 5, 10], [900, 2, 10]]}, [6, 6, 6])], [('regression: successive shutdowns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [900, 1, 10], [1200, 1, 10]]}, [4, 3, 3, 2]), ('boundary: ten quiet minutes', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10]]}, [4, 3]), ('boundary: minimum active cars', {'cars': 2, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [1200, 1, 10], [1800, 1, 10]]}, [2, 1, 1, 1]), ('boundary: demand returns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [300, 6, 10], [600, 1, 10], [900, 1, 10]]}, [4, 4, 4, 4]), ('boundary: long wait reactivates', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [660, 1, 46], [720, 1, 45]]}, [3, 2, 3, 3]), ('control 34', {'cars': 3, 'min_active': 2, 'low_demand': 5, 'samples': [[300, 2, 10], [360, 4, 10], [660, 15, 46], [1260, 15, 10], [1860, 15, 30], [2160, 4, 30], [2760, 4, 46], [3360, 15, 46], [3960, 6, 30]]}, [3, 3, 3, 3, 3, 3, 2, 3, 3]), ('control 37', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 0, 10], [660, 15, 10], [720, 2, 80], [780, 4, 30], [840, 4, 10], [1140, 4, 46], [1200, 4, 46], [1800, 4, 10]]}, [4, 4, 4, 4, 4, 4, 4, 3]), ('sampled regression 40', {'cars': 6, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 4, 45], [1200, 15, 46], [1260, 6, 30], [1320, 4, 10], [1920, 2, 10], [1980, 2, 10], [2040, 15, 46], [2340, 15, 30]]}, [6, 6, 6, 6, 5, 5, 6, 6])], [('regression: successive shutdowns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [900, 1, 10], [1200, 1, 10]]}, [4, 3, 3, 2]), ('sampled regression 4', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[300, 5, 45], [900, 6, 10], [1500, 5, 30], [1560, 2, 80], [1860, 2, 45], [2460, 2, 80], [2520, 4, 10], [2820, 6, 80], [3420, 4, 46], [3720, 4, 46]]}, [4, 4, 4, 4, 4, 3, 3, 4, 4, 4]), ('control 42', {'cars': 6, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 15, 10], [120, 4, 30], [720, 2, 46], [1320, 4, 45]]}, [6, 6, 5, 4]), ('boundary: minimum active cars', {'cars': 2, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [1200, 1, 10], [1800, 1, 10]]}, [2, 1, 1, 1]), ('boundary: demand returns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [300, 6, 10], [600, 1, 10], [900, 1, 10]]}, [4, 4, 4, 4]), ('control 45', {'cars': 6, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 5, 45], [900, 0, 10], [960, 5, 46], [1560, 4, 46], [1620, 0, 10], [1920, 4, 45], [2520, 0, 46], [2820, 4, 46], [3120, 2, 45], [3720, 5, 45]]}, [6, 6, 6, 6, 6, 6, 5, 6, 6, 6]), ('control 48', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[60, 4, 10], [360, 0, 30], [960, 4, 46], [1020, 15, 45], [1620, 5, 45], [1680, 2, 80], [1740, 6, 45], [2340, 15, 46], [2940, 4, 30], [3000, 4, 30]]}, [4, 4, 3, 3, 3, 4, 4, 4, 4, 4]), ('control 51', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[600, 2, 30], [660, 4, 46], [960, 5, 80], [1020, 15, 46]]}, [3, 3, 3, 3])]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
regression: successive shutdowns[4, 3, 2, 1][4, 3, 3, 2]Failed
sampled regression 28[3, 3, 2, 1, 2, 2, 3][3, 3, 2, 2, 3, 3, 3]Failed
control 42[6, 6, 5, 4][6, 6, 5, 4]Passed
boundary: ten quiet minutes[4, 3][4, 3]Passed
boundary: minimum active cars[2, 1, 1, 1][2, 1, 1, 1]Passed
control 1[6, 6, 6, 6, 6, 5, 6, 6, 6, 6][6, 6, 6, 6, 6, 5, 6, 6, 6, 6]Passed
sampled regression 4[4, 4, 4, 4, 4, 3, 2, 3, 4, 4][4, 4, 4, 4, 4, 3, 3, 4, 4, 4]Failed
control 7[3, 3, 3][3, 3, 3]Passed

SHA-256 / 4b3d8dd80c3a924f223f8798e88c9f851bfe3f6c343a065f078212ef5d0ff2be

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(x):
    active = x['cars']
    low_since = None
    out = []
    for t, demand, wait in x['samples']:
        if wait > 45 and active < x['cars']:
            active += 1
            low_since = None
        elif demand < x['low_demand']:
            if low_since is None:
                low_since = t
            elif t - low_since >= 600 and active > x['min_active']:
                active -= 1
                low_since = None
        else:
            low_since = None
        out.append(active)
    return out
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: successive shutdowns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [900, 1, 10], [1200, 1, 10]]}, [4, 3, 3, 2]), ('sampled regression 28', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[600, 6, 10], [1200, 0, 10], [1800, 0, 30], [2100, 2, 45], [2400, 15, 80], [2460, 15, 10], [2520, 4, 46]]}, [3, 3, 2, 2, 3, 3, 3]), ('control 42', {'cars': 6, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 15, 10], [120, 4, 30], [720, 2, 46], [1320, 4, 45]]}, [6, 6, 5, 4]), ('boundary: ten quiet minutes', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10]]}, [4, 3]), ('boundary: minimum active cars', {'cars': 2, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [1200, 1, 10], [1800, 1, 10]]}, [2, 1, 1, 1]), ('control 1', {'cars': 6, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 6, 30], [120, 0, 46], [720, 5, 30], [1320, 4, 30], [1620, 0, 10], [2220, 2, 10], [2520, 5, 46], [2580, 6, 10], [2640, 15, 10], [2700, 15, 10]]}, [6, 6, 6, 6, 6, 5, 6, 6, 6, 6]), ('sampled regression 4', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[300, 5, 45], [900, 6, 10], [1500, 5, 30], [1560, 2, 80], [1860, 2, 45], [2460, 2, 80], [2520, 4, 10], [2820, 6, 80], [3420, 4, 46], [3720, 4, 46]]}, [4, 4, 4, 4, 4, 3, 3, 4, 4, 4]), ('control 7', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[300, 5, 46], [600, 15, 30], [900, 2, 46]]}, [3, 3, 3])], [('regression: successive shutdowns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [900, 1, 10], [1200, 1, 10]]}, [4, 3, 3, 2]), ('sampled regression 36', {'cars': 6, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 6, 46], [660, 0, 10], [1260, 2, 10], [1320, 2, 45], [1920, 4, 46]]}, [6, 6, 5, 5, 6]), ('control 76', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[600, 0, 30], [900, 0, 46], [1500, 0, 80], [2100, 2, 30], [2700, 2, 45], [2760, 0, 30], [3060, 4, 30], [3660, 4, 45], [4260, 4, 46]]}, [4, 4, 3, 2, 1, 1, 1, 1, 2]), ('boundary: minimum active cars', {'cars': 2, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [1200, 1, 10], [1800, 1, 10]]}, [2, 1, 1, 1]), ('boundary: demand returns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [300, 6, 10], [600, 1, 10], [900, 1, 10]]}, [4, 4, 4, 4]), ('control 12', {'cars': 6, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 4, 30], [120, 6, 45], [180, 4, 80], [780, 5, 10], [1080, 5, 80], [1680, 15, 10]]}, [6, 6, 6, 6, 6, 6]), ('control 15', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 2, 46], [660, 6, 10], [960, 15, 10]]}, [4, 4, 4]), ('control 18', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[300, 0, 80], [900, 15, 46], [1200, 15, 30], [1500, 6, 46], [2100, 6, 80], [2700, 6, 46], [3300, 2, 10], [3900, 2, 46], [4200, 15, 30], [4500, 4, 30]]}, [4, 4, 4, 4, 4, 4, 4, 3, 3, 3])], [('regression: successive shutdowns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [900, 1, 10], [1200, 1, 10]]}, [4, 3, 3, 2]), ('sampled regression 40', {'cars': 6, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 4, 45], [1200, 15, 46], [1260, 6, 30], [1320, 4, 10], [1920, 2, 10], [1980, 2, 10], [2040, 15, 46], [2340, 15, 30]]}, [6, 6, 6, 6, 5, 5, 6, 6]), ('control 80', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 4, 45], [660, 5, 80], [960, 4, 46], [1560, 2, 10], [1620, 15, 45], [1920, 0, 46], [1980, 2, 46], [2580, 4, 46], [3180, 0, 30], [3480, 4, 10]]}, [4, 4, 4, 3, 3, 4, 4, 3, 2, 2]), ('boundary: long wait reactivates', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [660, 1, 46], [720, 1, 45]]}, [3, 2, 3, 3]), ('boundary: long wait with all cars active', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [300, 1, 80], [600, 1, 10]]}, [3, 3, 2]), ('control 23', {'cars': 3, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 2, 30], [660, 6, 30], [720, 2, 46], [1320, 5, 10], [1620, 5, 10], [1920, 5, 46], [2520, 5, 45], [2580, 4, 46], [2640, 15, 80], [2940, 6, 30]]}, [3, 3, 3, 3, 3, 3, 3, 3, 3, 3]), ('control 26', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 2, 80], [360, 0, 30], [420, 15, 30], [720, 15, 30], [1320, 15, 10], [1920, 5, 80], [2520, 6, 45], [2820, 0, 10]]}, [4, 4, 4, 4, 4, 4, 4, 4]), ('control 29', {'cars': 6, 'min_active': 1, 'low_demand': 5, 'samples': [[300, 0, 45], [600, 5, 10], [900, 2, 10]]}, [6, 6, 6])], [('regression: successive shutdowns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [900, 1, 10], [1200, 1, 10]]}, [4, 3, 3, 2]), ('boundary: ten quiet minutes', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10]]}, [4, 3]), ('boundary: minimum active cars', {'cars': 2, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [1200, 1, 10], [1800, 1, 10]]}, [2, 1, 1, 1]), ('boundary: demand returns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [300, 6, 10], [600, 1, 10], [900, 1, 10]]}, [4, 4, 4, 4]), ('boundary: long wait reactivates', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [660, 1, 46], [720, 1, 45]]}, [3, 2, 3, 3]), ('control 34', {'cars': 3, 'min_active': 2, 'low_demand': 5, 'samples': [[300, 2, 10], [360, 4, 10], [660, 15, 46], [1260, 15, 10], [1860, 15, 30], [2160, 4, 30], [2760, 4, 46], [3360, 15, 46], [3960, 6, 30]]}, [3, 3, 3, 3, 3, 3, 2, 3, 3]), ('control 37', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 0, 10], [660, 15, 10], [720, 2, 80], [780, 4, 30], [840, 4, 10], [1140, 4, 46], [1200, 4, 46], [1800, 4, 10]]}, [4, 4, 4, 4, 4, 4, 4, 3]), ('sampled regression 40', {'cars': 6, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 4, 45], [1200, 15, 46], [1260, 6, 30], [1320, 4, 10], [1920, 2, 10], [1980, 2, 10], [2040, 15, 46], [2340, 15, 30]]}, [6, 6, 6, 6, 5, 5, 6, 6])], [('regression: successive shutdowns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [900, 1, 10], [1200, 1, 10]]}, [4, 3, 3, 2]), ('sampled regression 4', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[300, 5, 45], [900, 6, 10], [1500, 5, 30], [1560, 2, 80], [1860, 2, 45], [2460, 2, 80], [2520, 4, 10], [2820, 6, 80], [3420, 4, 46], [3720, 4, 46]]}, [4, 4, 4, 4, 4, 3, 3, 4, 4, 4]), ('control 42', {'cars': 6, 'min_active': 1, 'low_demand': 5, 'samples': [[60, 15, 10], [120, 4, 30], [720, 2, 46], [1320, 4, 45]]}, [6, 6, 5, 4]), ('boundary: minimum active cars', {'cars': 2, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [600, 1, 10], [1200, 1, 10], [1800, 1, 10]]}, [2, 1, 1, 1]), ('boundary: demand returns', {'cars': 4, 'min_active': 1, 'low_demand': 5, 'samples': [[0, 1, 10], [300, 6, 10], [600, 1, 10], [900, 1, 10]]}, [4, 4, 4, 4]), ('control 45', {'cars': 6, 'min_active': 2, 'low_demand': 5, 'samples': [[600, 5, 45], [900, 0, 10], [960, 5, 46], [1560, 4, 46], [1620, 0, 10], [1920, 4, 45], [2520, 0, 46], [2820, 4, 46], [3120, 2, 45], [3720, 5, 45]]}, [6, 6, 6, 6, 6, 6, 5, 6, 6, 6]), ('control 48', {'cars': 4, 'min_active': 2, 'low_demand': 5, 'samples': [[60, 4, 10], [360, 0, 30], [960, 4, 46], [1020, 15, 45], [1620, 5, 45], [1680, 2, 80], [1740, 6, 45], [2340, 15, 46], [2940, 4, 30], [3000, 4, 30]]}, [4, 4, 3, 3, 3, 4, 4, 4, 4, 4]), ('control 51', {'cars': 3, 'min_active': 1, 'low_demand': 5, 'samples': [[600, 2, 30], [660, 4, 46], [960, 5, 80], [1020, 15, 46]]}, [3, 3, 3, 3])]]
for label, args, expected in fixtures[N-1]:
    check(label, solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
regression: successive shutdowns[4, 3, 3, 3][4, 3, 3, 2]Failed
sampled regression 28[3, 3, 2, 2, 3, 3, 3][3, 3, 2, 2, 3, 3, 3]Passed
control 42[6, 6, 5, 5][6, 6, 5, 4]Failed
boundary: ten quiet minutes[4, 3][4, 3]Passed
boundary: minimum active cars[2, 1, 1, 1][2, 1, 1, 1]Passed
control 1[6, 6, 6, 6, 6, 5, 6, 6, 6, 6][6, 6, 6, 6, 6, 5, 6, 6, 6, 6]Passed
sampled regression 4[4, 4, 4, 4, 4, 3, 3, 4, 4, 4][4, 4, 4, 4, 4, 3, 3, 4, 4, 4]Passed
control 7[3, 3, 3][3, 3, 3]Passed

SHA-256 / cbcb635a7953547d56e5c218d86971c2e941c5b2b75fc13e186eae39a84e2202

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This mechanism has 8 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

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Verification & scope

Stipulated toy lift-control contract for a bounded teaching model; it makes no claim of conformance to any lift code or vendor dispatcher and omits real safety cases. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:47:56.329023+00:00.

Case digest / 045914f87fc3fab7118548400abe4c8d8e5d7fed198b92aea6f5ba0631ea7e6b