FA-67616 / Elevator dispatch scheduling / Open access

Up-peak round trip time: lobby stop · case 01

The round trip omits the stop at the main lobby.

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

ROOT CAUSE

Only the upper-floor stops are charged stop time.

VERIFIED REPAIR

Charge S+1 stops including the lobby.

Unsuccessful approach: Charging two extra stops double-counts the lobby.

Case contract

Up-peak analysis with N floors above the lobby, P = 0.8*capacity passengers per trip (fractional), probable stops S = N(1-(1-1/N)^P), highest reversal floor H = N - sum_{i=1}^{N-1}(i/N)^P, RTT = 2*H*tv + (S+1)*ts + 2*P*tp, interval = RTT/cars and 5-minute handling capacity HC5 = 300*P*cars/RTT. Values rounded to 2 decimals.

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):
    N = x['floors_above']
    P = 0.8 * x['capacity']
    S = N * (1 - (1 - 1 / N) ** P)
    H = N - sum((i / N) ** P for i in range(1, N))
    rtt = 2 * H * x['tv_s'] + S * x['ts_s'] + 2 * P * x['tp_s']
    return {'S': round(S, 2), 'H': round(H, 2), 'rtt': round(rtt, 2), 'interval': round(rtt / x['cars'], 2), 'hc5': round(300 * P * x['cars'] / rtt, 2)}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 1', {'floors_above': 17, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 5.47, 'H': 15.17, 'rtt': 140.72, 'interval': 70.36, 'hc5': 27.29}), ('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('sampled regression 4', {'floors_above': 21, 'capacity': 13, 'cars': 2, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 8.36, 'H': 19.62, 'rtt': 154.51, 'interval': 77.25, 'hc5': 40.39}), ('sampled regression 7', {'floors_above': 24, 'capacity': 13, 'cars': 5, 'tv_s': 1.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 8.58, 'H': 22.36, 'rtt': 202.88, 'interval': 40.58, 'hc5': 76.89}), ('sampled regression 10', {'floors_above': 5, 'capacity': 8, 'cars': 5, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 3.8, 'H': 4.72, 'rtt': 74.97, 'interval': 14.99, 'hc5': 128.05}), ('sampled regression 13', {'floors_above': 8, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.2}, {'S': 6.55, 'H': 7.79, 'rtt': 114.51, 'interval': 19.08, 'hc5': 201.21})], [('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('sampled regression 8', {'floors_above': 25, 'capacity': 13, 'cars': 3, 'tv_s': 2.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 8.65, 'H': 23.27, 'rtt': 233.65, 'interval': 77.88, 'hc5': 40.06}), ('sampled regression 4', {'floors_above': 21, 'capacity': 13, 'cars': 2, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 8.36, 'H': 19.62, 'rtt': 154.51, 'interval': 77.25, 'hc5': 40.39}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 12', {'floors_above': 24, 'capacity': 21, 'cars': 4, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.2}, {'S': 12.26, 'H': 23.09, 'rtt': 215.68, 'interval': 53.92, 'hc5': 93.47}), ('sampled regression 15', {'floors_above': 19, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 9.49, 'H': 18.07, 'rtt': 205.68, 'interval': 34.28, 'hc5': 112.02}), ('sampled regression 18', {'floors_above': 24, 'capacity': 13, 'cars': 3, 'tv_s': 2.0, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 8.58, 'H': 22.36, 'rtt': 186.9, 'interval': 62.3, 'hc5': 50.08})], [('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('sampled regression 15', {'floors_above': 19, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 9.49, 'H': 18.07, 'rtt': 205.68, 'interval': 34.28, 'hc5': 112.02}), ('sampled regression 9', {'floors_above': 23, 'capacity': 21, 'cars': 3, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 12.1, 'H': 22.15, 'rtt': 253.2, 'interval': 84.4, 'hc5': 59.72}), ('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 23', {'floors_above': 7, 'capacity': 16, 'cars': 4, 'tv_s': 2.0, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 6.03, 'H': 6.85, 'rtt': 137.31, 'interval': 34.33, 'hc5': 111.86}), ('sampled regression 26', {'floors_above': 18, 'capacity': 16, 'cars': 7, 'tv_s': 2.0, 'ts_s': 8.0, 'tp_s': 1.2}, {'S': 9.34, 'H': 17.14, 'rtt': 181.99, 'interval': 26.0, 'hc5': 147.7}), ('sampled regression 29', {'floors_above': 23, 'capacity': 16, 'cars': 3, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 9.98, 'H': 21.79, 'rtt': 271.41, 'interval': 90.47, 'hc5': 42.44})], [('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 22', {'floors_above': 18, 'capacity': 21, 'cars': 7, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 11.11, 'H': 17.41, 'rtt': 272.7, 'interval': 38.96, 'hc5': 129.37}), ('sampled regression 14', {'floors_above': 7, 'capacity': 16, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 6.03, 'H': 6.85, 'rtt': 123.26, 'interval': 61.63, 'hc5': 62.31}), ('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('sampled regression 34', {'floors_above': 16, 'capacity': 8, 'cars': 8, 'tv_s': 2.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 5.41, 'H': 14.3, 'rtt': 135.63, 'interval': 16.95, 'hc5': 113.25}), ('sampled regression 37', {'floors_above': 14, 'capacity': 13, 'cars': 2, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 7.52, 'H': 13.21, 'rtt': 193.28, 'interval': 96.64, 'hc5': 32.28}), ('sampled regression 40', {'floors_above': 9, 'capacity': 21, 'cars': 2, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 7.76, 'H': 8.85, 'rtt': 182.9, 'interval': 91.45, 'hc5': 55.11})], [('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('sampled regression 29', {'floors_above': 23, 'capacity': 16, 'cars': 3, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 9.98, 'H': 21.79, 'rtt': 271.41, 'interval': 90.47, 'hc5': 42.44}), ('sampled regression 19', {'floors_above': 18, 'capacity': 16, 'cars': 7, 'tv_s': 2.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.34, 'H': 17.14, 'rtt': 214.68, 'interval': 30.67, 'hc5': 125.21}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 45', {'floors_above': 14, 'capacity': 13, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.52, 'H': 13.21, 'rtt': 163.03, 'interval': 81.51, 'hc5': 38.28}), ('sampled regression 48', {'floors_above': 19, 'capacity': 8, 'cars': 7, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 5.56, 'H': 16.9, 'rtt': 145.99, 'interval': 20.86, 'hc5': 92.06}), ('sampled regression 51', {'floors_above': 16, 'capacity': 8, 'cars': 8, 'tv_s': 2.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 5.41, 'H': 14.3, 'rtt': 135.63, 'interval': 16.95, 'hc5': 113.25})]]
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 fixture	Actual	Expected	Outcome
regression: small office	`{'H': 4.72, 'S': 3.8, 'hc5': 53.15, 'interval': 36.12, 'rtt': 72.25}`	`{'H': 4.72, 'S': 3.8, 'hc5': 46.69, 'interval': 41.12, 'rtt': 82.25}`	Failed
sampled regression 1	`{'H': 15.17, 'S': 5.47, 'hc5': 29.38, 'interval': 65.36, 'rtt': 130.72}`	`{'H': 15.17, 'S': 5.47, 'hc5': 27.29, 'interval': 70.36, 'rtt': 140.72}`	Failed
regression: tall office	`{'H': 19.0, 'S': 9.63, 'hc5': 128.81, 'interval': 29.81, 'rtt': 178.87}`	`{'H': 19.0, 'S': 9.63, 'hc5': 121.99, 'interval': 31.48, 'rtt': 188.87}`	Failed
regression: fractional passenger load	`{'H': 11.38, 'S': 7.15, 'hc5': 87.94, 'interval': 35.48, 'rtt': 141.91}`	`{'H': 11.38, 'S': 7.15, 'hc5': 82.15, 'interval': 37.98, 'rtt': 151.91}`	Failed
sampled regression 4	`{'H': 19.62, 'S': 8.36, 'hc5': 42.59, 'interval': 73.25, 'rtt': 146.51}`	`{'H': 19.62, 'S': 8.36, 'hc5': 40.39, 'interval': 77.25, 'rtt': 154.51}`	Failed
sampled regression 7	`{'H': 22.36, 'S': 8.58, 'hc5': 81.73, 'interval': 38.18, 'rtt': 190.88}`	`{'H': 22.36, 'S': 8.58, 'hc5': 76.89, 'interval': 40.58, 'rtt': 202.88}`	Failed
sampled regression 10	`{'H': 4.72, 'S': 3.8, 'hc5': 147.76, 'interval': 12.99, 'rtt': 64.97}`	`{'H': 4.72, 'S': 3.8, 'hc5': 128.05, 'interval': 14.99, 'rtt': 74.97}`	Failed
sampled regression 13	`{'H': 7.79, 'S': 6.55, 'hc5': 216.32, 'interval': 17.75, 'rtt': 106.51}`	`{'H': 7.79, 'S': 6.55, 'hc5': 201.21, 'interval': 19.08, 'rtt': 114.51}`	Failed

SHA-256 / 8d5a0c6a26863aa89a0d78c755b16c05cf8e4850e7d0f7dedbe3c8a367c12124

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(x):
    N = x['floors_above']
    P = 0.8 * x['capacity']
    S = N * (1 - (1 - 1 / N) ** P)
    H = N - sum((i / N) ** P for i in range(1, N))
    rtt = 2 * H * x['tv_s'] + (S + 2) * x['ts_s'] + 2 * P * x['tp_s']
    return {'S': round(S, 2), 'H': round(H, 2), 'rtt': round(rtt, 2), 'interval': round(rtt / x['cars'], 2), 'hc5': round(300 * P * x['cars'] / rtt, 2)}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 1', {'floors_above': 17, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 5.47, 'H': 15.17, 'rtt': 140.72, 'interval': 70.36, 'hc5': 27.29}), ('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('sampled regression 4', {'floors_above': 21, 'capacity': 13, 'cars': 2, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 8.36, 'H': 19.62, 'rtt': 154.51, 'interval': 77.25, 'hc5': 40.39}), ('sampled regression 7', {'floors_above': 24, 'capacity': 13, 'cars': 5, 'tv_s': 1.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 8.58, 'H': 22.36, 'rtt': 202.88, 'interval': 40.58, 'hc5': 76.89}), ('sampled regression 10', {'floors_above': 5, 'capacity': 8, 'cars': 5, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 3.8, 'H': 4.72, 'rtt': 74.97, 'interval': 14.99, 'hc5': 128.05}), ('sampled regression 13', {'floors_above': 8, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.2}, {'S': 6.55, 'H': 7.79, 'rtt': 114.51, 'interval': 19.08, 'hc5': 201.21})], [('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('sampled regression 8', {'floors_above': 25, 'capacity': 13, 'cars': 3, 'tv_s': 2.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 8.65, 'H': 23.27, 'rtt': 233.65, 'interval': 77.88, 'hc5': 40.06}), ('sampled regression 4', {'floors_above': 21, 'capacity': 13, 'cars': 2, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 8.36, 'H': 19.62, 'rtt': 154.51, 'interval': 77.25, 'hc5': 40.39}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 12', {'floors_above': 24, 'capacity': 21, 'cars': 4, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.2}, {'S': 12.26, 'H': 23.09, 'rtt': 215.68, 'interval': 53.92, 'hc5': 93.47}), ('sampled regression 15', {'floors_above': 19, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 9.49, 'H': 18.07, 'rtt': 205.68, 'interval': 34.28, 'hc5': 112.02}), ('sampled regression 18', {'floors_above': 24, 'capacity': 13, 'cars': 3, 'tv_s': 2.0, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 8.58, 'H': 22.36, 'rtt': 186.9, 'interval': 62.3, 'hc5': 50.08})], [('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('sampled regression 15', {'floors_above': 19, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 9.49, 'H': 18.07, 'rtt': 205.68, 'interval': 34.28, 'hc5': 112.02}), ('sampled regression 9', {'floors_above': 23, 'capacity': 21, 'cars': 3, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 12.1, 'H': 22.15, 'rtt': 253.2, 'interval': 84.4, 'hc5': 59.72}), ('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 23', {'floors_above': 7, 'capacity': 16, 'cars': 4, 'tv_s': 2.0, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 6.03, 'H': 6.85, 'rtt': 137.31, 'interval': 34.33, 'hc5': 111.86}), ('sampled regression 26', {'floors_above': 18, 'capacity': 16, 'cars': 7, 'tv_s': 2.0, 'ts_s': 8.0, 'tp_s': 1.2}, {'S': 9.34, 'H': 17.14, 'rtt': 181.99, 'interval': 26.0, 'hc5': 147.7}), ('sampled regression 29', {'floors_above': 23, 'capacity': 16, 'cars': 3, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 9.98, 'H': 21.79, 'rtt': 271.41, 'interval': 90.47, 'hc5': 42.44})], [('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 22', {'floors_above': 18, 'capacity': 21, 'cars': 7, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 11.11, 'H': 17.41, 'rtt': 272.7, 'interval': 38.96, 'hc5': 129.37}), ('sampled regression 14', {'floors_above': 7, 'capacity': 16, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 6.03, 'H': 6.85, 'rtt': 123.26, 'interval': 61.63, 'hc5': 62.31}), ('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('sampled regression 34', {'floors_above': 16, 'capacity': 8, 'cars': 8, 'tv_s': 2.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 5.41, 'H': 14.3, 'rtt': 135.63, 'interval': 16.95, 'hc5': 113.25}), ('sampled regression 37', {'floors_above': 14, 'capacity': 13, 'cars': 2, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 7.52, 'H': 13.21, 'rtt': 193.28, 'interval': 96.64, 'hc5': 32.28}), ('sampled regression 40', {'floors_above': 9, 'capacity': 21, 'cars': 2, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 7.76, 'H': 8.85, 'rtt': 182.9, 'interval': 91.45, 'hc5': 55.11})], [('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('sampled regression 29', {'floors_above': 23, 'capacity': 16, 'cars': 3, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 9.98, 'H': 21.79, 'rtt': 271.41, 'interval': 90.47, 'hc5': 42.44}), ('sampled regression 19', {'floors_above': 18, 'capacity': 16, 'cars': 7, 'tv_s': 2.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.34, 'H': 17.14, 'rtt': 214.68, 'interval': 30.67, 'hc5': 125.21}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 45', {'floors_above': 14, 'capacity': 13, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.52, 'H': 13.21, 'rtt': 163.03, 'interval': 81.51, 'hc5': 38.28}), ('sampled regression 48', {'floors_above': 19, 'capacity': 8, 'cars': 7, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 5.56, 'H': 16.9, 'rtt': 145.99, 'interval': 20.86, 'hc5': 92.06}), ('sampled regression 51', {'floors_above': 16, 'capacity': 8, 'cars': 8, 'tv_s': 2.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 5.41, 'H': 14.3, 'rtt': 135.63, 'interval': 16.95, 'hc5': 113.25})]]
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 fixture	Actual	Expected	Outcome
regression: small office	`{'H': 4.72, 'S': 3.8, 'hc5': 41.63, 'interval': 46.12, 'rtt': 92.25}`	`{'H': 4.72, 'S': 3.8, 'hc5': 46.69, 'interval': 41.12, 'rtt': 82.25}`	Failed
sampled regression 1	`{'H': 15.17, 'S': 5.47, 'hc5': 25.48, 'interval': 75.36, 'rtt': 150.72}`	`{'H': 15.17, 'S': 5.47, 'hc5': 27.29, 'interval': 70.36, 'rtt': 140.72}`	Failed
regression: tall office	`{'H': 19.0, 'S': 9.63, 'hc5': 115.86, 'interval': 33.14, 'rtt': 198.87}`	`{'H': 19.0, 'S': 9.63, 'hc5': 121.99, 'interval': 31.48, 'rtt': 188.87}`	Failed
regression: fractional passenger load	`{'H': 11.38, 'S': 7.15, 'hc5': 77.08, 'interval': 40.48, 'rtt': 161.91}`	`{'H': 11.38, 'S': 7.15, 'hc5': 82.15, 'interval': 37.98, 'rtt': 151.91}`	Failed
sampled regression 4	`{'H': 19.62, 'S': 8.36, 'hc5': 38.4, 'interval': 81.25, 'rtt': 162.51}`	`{'H': 19.62, 'S': 8.36, 'hc5': 40.39, 'interval': 77.25, 'rtt': 154.51}`	Failed
sampled regression 7	`{'H': 22.36, 'S': 8.58, 'hc5': 72.6, 'interval': 42.98, 'rtt': 214.88}`	`{'H': 22.36, 'S': 8.58, 'hc5': 76.89, 'interval': 40.58, 'rtt': 202.88}`	Failed
sampled regression 10	`{'H': 4.72, 'S': 3.8, 'hc5': 112.98, 'interval': 16.99, 'rtt': 84.97}`	`{'H': 4.72, 'S': 3.8, 'hc5': 128.05, 'interval': 14.99, 'rtt': 74.97}`	Failed
sampled regression 13	`{'H': 7.79, 'S': 6.55, 'hc5': 188.07, 'interval': 20.42, 'rtt': 122.51}`	`{'H': 7.79, 'S': 6.55, 'hc5': 201.21, 'interval': 19.08, 'rtt': 114.51}`	Failed

SHA-256 / f8e0e1a34e60a5d0740a4d56ebce1fb441604f73e8287a4012e2fac60c11e116

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(x):
    N = x['floors_above']
    P = 0.8 * x['capacity']
    S = N * (1 - (1 - 1 / N) ** P)
    H = N - sum((i / N) ** P for i in range(1, N))
    rtt = 2 * H * x['tv_s'] + (S + 1) * x['ts_s'] + 2 * P * x['tp_s']
    return {'S': round(S, 2), 'H': round(H, 2), 'rtt': round(rtt, 2), 'interval': round(rtt / x['cars'], 2), 'hc5': round(300 * P * x['cars'] / rtt, 2)}
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 1', {'floors_above': 17, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 5.47, 'H': 15.17, 'rtt': 140.72, 'interval': 70.36, 'hc5': 27.29}), ('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('sampled regression 4', {'floors_above': 21, 'capacity': 13, 'cars': 2, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 8.36, 'H': 19.62, 'rtt': 154.51, 'interval': 77.25, 'hc5': 40.39}), ('sampled regression 7', {'floors_above': 24, 'capacity': 13, 'cars': 5, 'tv_s': 1.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 8.58, 'H': 22.36, 'rtt': 202.88, 'interval': 40.58, 'hc5': 76.89}), ('sampled regression 10', {'floors_above': 5, 'capacity': 8, 'cars': 5, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 3.8, 'H': 4.72, 'rtt': 74.97, 'interval': 14.99, 'hc5': 128.05}), ('sampled regression 13', {'floors_above': 8, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.2}, {'S': 6.55, 'H': 7.79, 'rtt': 114.51, 'interval': 19.08, 'hc5': 201.21})], [('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('sampled regression 8', {'floors_above': 25, 'capacity': 13, 'cars': 3, 'tv_s': 2.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 8.65, 'H': 23.27, 'rtt': 233.65, 'interval': 77.88, 'hc5': 40.06}), ('sampled regression 4', {'floors_above': 21, 'capacity': 13, 'cars': 2, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 8.36, 'H': 19.62, 'rtt': 154.51, 'interval': 77.25, 'hc5': 40.39}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 12', {'floors_above': 24, 'capacity': 21, 'cars': 4, 'tv_s': 1.5, 'ts_s': 8.0, 'tp_s': 1.2}, {'S': 12.26, 'H': 23.09, 'rtt': 215.68, 'interval': 53.92, 'hc5': 93.47}), ('sampled regression 15', {'floors_above': 19, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 9.49, 'H': 18.07, 'rtt': 205.68, 'interval': 34.28, 'hc5': 112.02}), ('sampled regression 18', {'floors_above': 24, 'capacity': 13, 'cars': 3, 'tv_s': 2.0, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 8.58, 'H': 22.36, 'rtt': 186.9, 'interval': 62.3, 'hc5': 50.08})], [('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('sampled regression 15', {'floors_above': 19, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 9.49, 'H': 18.07, 'rtt': 205.68, 'interval': 34.28, 'hc5': 112.02}), ('sampled regression 9', {'floors_above': 23, 'capacity': 21, 'cars': 3, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 12.1, 'H': 22.15, 'rtt': 253.2, 'interval': 84.4, 'hc5': 59.72}), ('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 23', {'floors_above': 7, 'capacity': 16, 'cars': 4, 'tv_s': 2.0, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 6.03, 'H': 6.85, 'rtt': 137.31, 'interval': 34.33, 'hc5': 111.86}), ('sampled regression 26', {'floors_above': 18, 'capacity': 16, 'cars': 7, 'tv_s': 2.0, 'ts_s': 8.0, 'tp_s': 1.2}, {'S': 9.34, 'H': 17.14, 'rtt': 181.99, 'interval': 26.0, 'hc5': 147.7}), ('sampled regression 29', {'floors_above': 23, 'capacity': 16, 'cars': 3, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 9.98, 'H': 21.79, 'rtt': 271.41, 'interval': 90.47, 'hc5': 42.44})], [('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 22', {'floors_above': 18, 'capacity': 21, 'cars': 7, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 11.11, 'H': 17.41, 'rtt': 272.7, 'interval': 38.96, 'hc5': 129.37}), ('sampled regression 14', {'floors_above': 7, 'capacity': 16, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 6.03, 'H': 6.85, 'rtt': 123.26, 'interval': 61.63, 'hc5': 62.31}), ('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('sampled regression 34', {'floors_above': 16, 'capacity': 8, 'cars': 8, 'tv_s': 2.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 5.41, 'H': 14.3, 'rtt': 135.63, 'interval': 16.95, 'hc5': 113.25}), ('sampled regression 37', {'floors_above': 14, 'capacity': 13, 'cars': 2, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 7.52, 'H': 13.21, 'rtt': 193.28, 'interval': 96.64, 'hc5': 32.28}), ('sampled regression 40', {'floors_above': 9, 'capacity': 21, 'cars': 2, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.0}, {'S': 7.76, 'H': 8.85, 'rtt': 182.9, 'interval': 91.45, 'hc5': 55.11})], [('regression: tall office', {'floors_above': 20, 'capacity': 16, 'cars': 6, 'tv_s': 1.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.63, 'H': 19.0, 'rtt': 188.87, 'interval': 31.48, 'hc5': 121.99}), ('sampled regression 29', {'floors_above': 23, 'capacity': 16, 'cars': 3, 'tv_s': 2.5, 'ts_s': 12.0, 'tp_s': 1.2}, {'S': 9.98, 'H': 21.79, 'rtt': 271.41, 'interval': 90.47, 'hc5': 42.44}), ('sampled regression 19', {'floors_above': 18, 'capacity': 16, 'cars': 7, 'tv_s': 2.5, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 9.34, 'H': 17.14, 'rtt': 214.68, 'interval': 30.67, 'hc5': 125.21}), ('regression: fractional passenger load', {'floors_above': 12, 'capacity': 13, 'cars': 4, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.15, 'H': 11.38, 'rtt': 151.91, 'interval': 37.98, 'hc5': 82.15}), ('regression: small office', {'floors_above': 5, 'capacity': 8, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 3.8, 'H': 4.72, 'rtt': 82.25, 'interval': 41.12, 'hc5': 46.69}), ('sampled regression 45', {'floors_above': 14, 'capacity': 13, 'cars': 2, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.2}, {'S': 7.52, 'H': 13.21, 'rtt': 163.03, 'interval': 81.51, 'hc5': 38.28}), ('sampled regression 48', {'floors_above': 19, 'capacity': 8, 'cars': 7, 'tv_s': 2.0, 'ts_s': 10.0, 'tp_s': 1.0}, {'S': 5.56, 'H': 16.9, 'rtt': 145.99, 'interval': 20.86, 'hc5': 92.06}), ('sampled regression 51', {'floors_above': 16, 'capacity': 8, 'cars': 8, 'tv_s': 2.5, 'ts_s': 8.0, 'tp_s': 1.0}, {'S': 5.41, 'H': 14.3, 'rtt': 135.63, 'interval': 16.95, 'hc5': 113.25})]]
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 fixture	Actual	Expected	Outcome
regression: small office	`{'H': 4.72, 'S': 3.8, 'hc5': 46.69, 'interval': 41.12, 'rtt': 82.25}`	`{'H': 4.72, 'S': 3.8, 'hc5': 46.69, 'interval': 41.12, 'rtt': 82.25}`	Passed
sampled regression 1	`{'H': 15.17, 'S': 5.47, 'hc5': 27.29, 'interval': 70.36, 'rtt': 140.72}`	`{'H': 15.17, 'S': 5.47, 'hc5': 27.29, 'interval': 70.36, 'rtt': 140.72}`	Passed
regression: tall office	`{'H': 19.0, 'S': 9.63, 'hc5': 121.99, 'interval': 31.48, 'rtt': 188.87}`	`{'H': 19.0, 'S': 9.63, 'hc5': 121.99, 'interval': 31.48, 'rtt': 188.87}`	Passed
regression: fractional passenger load	`{'H': 11.38, 'S': 7.15, 'hc5': 82.15, 'interval': 37.98, 'rtt': 151.91}`	`{'H': 11.38, 'S': 7.15, 'hc5': 82.15, 'interval': 37.98, 'rtt': 151.91}`	Passed
sampled regression 4	`{'H': 19.62, 'S': 8.36, 'hc5': 40.39, 'interval': 77.25, 'rtt': 154.51}`	`{'H': 19.62, 'S': 8.36, 'hc5': 40.39, 'interval': 77.25, 'rtt': 154.51}`	Passed
sampled regression 7	`{'H': 22.36, 'S': 8.58, 'hc5': 76.89, 'interval': 40.58, 'rtt': 202.88}`	`{'H': 22.36, 'S': 8.58, 'hc5': 76.89, 'interval': 40.58, 'rtt': 202.88}`	Passed
sampled regression 10	`{'H': 4.72, 'S': 3.8, 'hc5': 128.05, 'interval': 14.99, 'rtt': 74.97}`	`{'H': 4.72, 'S': 3.8, 'hc5': 128.05, 'interval': 14.99, 'rtt': 74.97}`	Passed
sampled regression 13	`{'H': 7.79, 'S': 6.55, 'hc5': 201.21, 'interval': 19.08, 'rtt': 114.51}`	`{'H': 7.79, 'S': 6.55, 'hc5': 201.21, 'interval': 19.08, 'rtt': 114.51}`	Passed

SHA-256 / 5d2890cbec13be8afca410469c2815c230bbd7edf7ac419bb1e037d982675fdb

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 2026-09-29T14:47:54.574959+00:00.

Case digest / d6da959642dea991473169c4fcf53cb821c359094008d0a57c3875e5d7bde814