{"abstract":"The highest reversal floor is one floor low or high.","category":"Elevator dispatch scheduling","checks":8,"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.","contract_signature":"x","evaluation_group":"w2-elevator_dispatch_scheduling-uppeak-round-trip","failed_approach":"Shifting the range to 0..N-2 drops the largest term.","family":"w2-elevator_dispatch_scheduling-uppeak-round-trip-highest-reversal-floor","id":"FA-67611","implementations":{"attempt":{"sha256":"f50a0e1fabe635e61a720001ceb0f14da67e1b84cecba02540ffc855d9d43aa5","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    N = x['floors_above']\n    P = 0.8 * x['capacity']\n    S = N * (1 - (1 - 1 / N) ** P)\n    H = N - sum((i / N) ** P for i in range(0, N - 1))\n    rtt = 2 * H * x['tv_s'] + (S + 1) * x['ts_s'] + 2 * P * x['tp_s']\n    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)}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('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})]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"4943339ebbaa4c3dc9d61e3b4b95c4fd3cebb2003ec4ceecc88e3674f0b26d96","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    N = x['floors_above']\n    P = 0.8 * x['capacity']\n    S = N * (1 - (1 - 1 / N) ** P)\n    H = N - sum((i / N) ** P for i in range(1, N + 1))\n    rtt = 2 * H * x['tv_s'] + (S + 1) * x['ts_s'] + 2 * P * x['tp_s']\n    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)}\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('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})]]\nfor label, args, expected in fixtures[N-1]:\n    check(label, solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"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.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-elevator_dispatch_scheduling-uppeak-round-trip-highest-reversal-floor","generated_at":"2026-09-29T14:47:54.574959+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Lift group controllers make these decisions many times per minute; a wrong answer strands passengers, wastes trips or overrides a safety rule.","root_cause":"The sum includes the certain top-floor term (N/N)^P.","sha256":"090ba23d6c59770bc92bb95970c69f38f740dcc071c48011477124688e4014a7","title":"Up-peak round trip time: highest reversal floor · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":41.231,"exit_code":1,"observations":[{"actual":{"H":4.96,"S":3.8,"hc5":46.15,"interval":41.6,"rtt":83.21},"check":"regression: small office","expected":{"H":4.72,"S":3.8,"hc5":46.69,"interval":41.12,"rtt":82.25},"passed":false},{"actual":{"H":15.85,"S":5.47,"hc5":26.77,"interval":71.71,"rtt":143.43},"check":"sampled regression 1","expected":{"H":15.17,"S":5.47,"hc5":27.29,"interval":70.36,"rtt":140.72},"passed":false},{"actual":{"H":19.52,"S":9.63,"hc5":120.99,"interval":31.74,"rtt":190.42},"check":"regression: tall office","expected":{"H":19.0,"S":9.63,"hc5":121.99,"interval":31.48,"rtt":188.87},"passed":false},{"actual":{"H":11.78,"S":7.15,"hc5":81.29,"interval":38.38,"rtt":153.53},"check":"regression: fractional passenger load","expected":{"H":11.38,"S":7.15,"hc5":82.15,"interval":37.98,"rtt":151.91},"passed":false},{"actual":{"H":20.22,"S":8.36,"hc5":39.92,"interval":78.16,"rtt":156.31},"check":"sampled regression 4","expected":{"H":19.62,"S":8.36,"hc5":40.39,"interval":77.25,"rtt":154.51},"passed":false},{"actual":{"H":23.0,"S":8.58,"hc5":76.17,"interval":40.96,"rtt":204.81},"check":"sampled regression 7","expected":{"H":22.36,"S":8.58,"hc5":76.89,"interval":40.58,"rtt":202.88},"passed":false},{"actual":{"H":4.96,"S":3.8,"hc5":126.83,"interval":15.14,"rtt":75.69},"check":"sampled regression 10","expected":{"H":4.72,"S":3.8,"hc5":128.05,"interval":14.99,"rtt":74.97},"passed":false},{"actual":{"H":7.97,"S":6.55,"hc5":200.26,"interval":19.18,"rtt":115.05},"check":"sampled regression 13","expected":{"H":7.79,"S":6.55,"hc5":201.21,"interval":19.08,"rtt":114.51},"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: small office\", \"actual\": {\"S\": 3.8, \"H\": 4.96, \"rtt\": 83.21, \"interval\": 41.6, \"hc5\": 46.15}, \"expected\": {\"S\": 3.8, \"H\": 4.72, \"rtt\": 82.25, \"interval\": 41.12, \"hc5\": 46.69}, \"passed\": false}, {\"check\": \"sampled regression 1\", \"actual\": {\"S\": 5.47, \"H\": 15.85, \"rtt\": 143.43, \"interval\": 71.71, \"hc5\": 26.77}, \"expected\": {\"S\": 5.47, \"H\": 15.17, \"rtt\": 140.72, \"interval\": 70.36, \"hc5\": 27.29}, \"passed\": false}, {\"check\": \"regression: tall office\", \"actual\": {\"S\": 9.63, \"H\": 19.52, \"rtt\": 190.42, \"interval\": 31.74, \"hc5\": 120.99}, \"expected\": {\"S\": 9.63, \"H\": 19.0, \"rtt\": 188.87, \"interval\": 31.48, \"hc5\": 121.99}, \"passed\": false}, {\"check\": \"regression: fractional passenger load\", \"actual\": {\"S\": 7.15, \"H\": 11.78, \"rtt\": 153.53, \"interval\": 38.38, \"hc5\": 81.29}, \"expected\": {\"S\": 7.15, \"H\": 11.38, \"rtt\": 151.91, \"interval\": 37.98, \"hc5\": 82.15}, \"passed\": false}, {\"check\": \"sampled regression 4\", \"actual\": {\"S\": 8.36, \"H\": 20.22, \"rtt\": 156.31, \"interval\": 78.16, \"hc5\": 39.92}, \"expected\": {\"S\": 8.36, \"H\": 19.62, \"rtt\": 154.51, \"interval\": 77.25, \"hc5\": 40.39}, \"passed\": false}, {\"check\": \"sampled regression 7\", \"actual\": {\"S\": 8.58, \"H\": 23.0, \"rtt\": 204.81, \"interval\": 40.96, \"hc5\": 76.17}, \"expected\": {\"S\": 8.58, \"H\": 22.36, \"rtt\": 202.88, \"interval\": 40.58, \"hc5\": 76.89}, \"passed\": false}, {\"check\": \"sampled regression 10\", \"actual\": {\"S\": 3.8, \"H\": 4.96, \"rtt\": 75.69, \"interval\": 15.14, \"hc5\": 126.83}, \"expected\": {\"S\": 3.8, \"H\": 4.72, \"rtt\": 74.97, \"interval\": 14.99, \"hc5\": 128.05}, \"passed\": false}, {\"check\": \"sampled regression 13\", \"actual\": {\"S\": 6.55, \"H\": 7.97, \"rtt\": 115.05, \"interval\": 19.18, \"hc5\": 200.26}, \"expected\": {\"S\": 6.55, \"H\": 7.79, \"rtt\": 114.51, \"interval\": 19.08, \"hc5\": 201.21}, \"passed\": false}], \"passed\": false}\n"},"broken":{"elapsed_ms":43.295,"exit_code":1,"observations":[{"actual":{"H":3.72,"S":3.8,"hc5":49.07,"interval":39.12,"rtt":78.25},"check":"regression: small office","expected":{"H":4.72,"S":3.8,"hc5":46.69,"interval":41.12,"rtt":82.25},"passed":false},{"actual":{"H":14.17,"S":5.47,"hc5":28.09,"interval":68.36,"rtt":136.72},"check":"sampled regression 1","expected":{"H":15.17,"S":5.47,"hc5":27.29,"interval":70.36,"rtt":140.72},"passed":false},{"actual":{"H":18.0,"S":9.63,"hc5":123.96,"interval":30.98,"rtt":185.87},"check":"regression: tall office","expected":{"H":19.0,"S":9.63,"hc5":121.99,"interval":31.48,"rtt":188.87},"passed":false},{"actual":{"H":10.38,"S":7.15,"hc5":84.37,"interval":36.98,"rtt":147.91},"check":"regression: fractional passenger load","expected":{"H":11.38,"S":7.15,"hc5":82.15,"interval":37.98,"rtt":151.91},"passed":false},{"actual":{"H":18.62,"S":8.36,"hc5":41.19,"interval":75.75,"rtt":151.51},"check":"sampled regression 4","expected":{"H":19.62,"S":8.36,"hc5":40.39,"interval":77.25,"rtt":154.51},"passed":false},{"actual":{"H":21.36,"S":8.58,"hc5":78.05,"interval":39.98,"rtt":199.88},"check":"sampled regression 7","expected":{"H":22.36,"S":8.58,"hc5":76.89,"interval":40.58,"rtt":202.88},"passed":false},{"actual":{"H":3.72,"S":3.8,"hc5":133.39,"interval":14.39,"rtt":71.97},"check":"sampled regression 10","expected":{"H":4.72,"S":3.8,"hc5":128.05,"interval":14.99,"rtt":74.97},"passed":false},{"actual":{"H":6.79,"S":6.55,"hc5":206.62,"interval":18.58,"rtt":111.51},"check":"sampled regression 13","expected":{"H":7.79,"S":6.55,"hc5":201.21,"interval":19.08,"rtt":114.51},"passed":false}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression: small office\", \"actual\": {\"S\": 3.8, \"H\": 3.72, \"rtt\": 78.25, \"interval\": 39.12, \"hc5\": 49.07}, \"expected\": {\"S\": 3.8, \"H\": 4.72, \"rtt\": 82.25, \"interval\": 41.12, \"hc5\": 46.69}, \"passed\": false}, {\"check\": \"sampled regression 1\", \"actual\": {\"S\": 5.47, \"H\": 14.17, \"rtt\": 136.72, \"interval\": 68.36, \"hc5\": 28.09}, \"expected\": {\"S\": 5.47, \"H\": 15.17, \"rtt\": 140.72, \"interval\": 70.36, \"hc5\": 27.29}, \"passed\": false}, {\"check\": \"regression: tall office\", \"actual\": {\"S\": 9.63, \"H\": 18.0, \"rtt\": 185.87, \"interval\": 30.98, \"hc5\": 123.96}, \"expected\": {\"S\": 9.63, \"H\": 19.0, \"rtt\": 188.87, \"interval\": 31.48, \"hc5\": 121.99}, \"passed\": false}, {\"check\": \"regression: fractional passenger load\", \"actual\": {\"S\": 7.15, \"H\": 10.38, \"rtt\": 147.91, \"interval\": 36.98, \"hc5\": 84.37}, \"expected\": {\"S\": 7.15, \"H\": 11.38, \"rtt\": 151.91, \"interval\": 37.98, \"hc5\": 82.15}, \"passed\": false}, {\"check\": \"sampled regression 4\", \"actual\": {\"S\": 8.36, \"H\": 18.62, \"rtt\": 151.51, \"interval\": 75.75, \"hc5\": 41.19}, \"expected\": {\"S\": 8.36, \"H\": 19.62, \"rtt\": 154.51, \"interval\": 77.25, \"hc5\": 40.39}, \"passed\": false}, {\"check\": \"sampled regression 7\", \"actual\": {\"S\": 8.58, \"H\": 21.36, \"rtt\": 199.88, \"interval\": 39.98, \"hc5\": 78.05}, \"expected\": {\"S\": 8.58, \"H\": 22.36, \"rtt\": 202.88, \"interval\": 40.58, \"hc5\": 76.89}, \"passed\": false}, {\"check\": \"sampled regression 10\", \"actual\": {\"S\": 3.8, \"H\": 3.72, \"rtt\": 71.97, \"interval\": 14.39, \"hc5\": 133.39}, \"expected\": {\"S\": 3.8, \"H\": 4.72, \"rtt\": 74.97, \"interval\": 14.99, \"hc5\": 128.05}, \"passed\": false}, {\"check\": \"sampled regression 13\", \"actual\": {\"S\": 6.55, \"H\": 6.79, \"rtt\": 111.51, \"interval\": 18.58, \"hc5\": 206.62}, \"expected\": {\"S\": 6.55, \"H\": 7.79, \"rtt\": 114.51, \"interval\": 19.08, \"hc5\": 201.21}, \"passed\": false}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}