FA-67481 / Elevator dispatch scheduling / Open access

Idle car parking by zone: up-peak lobby pair · case 01

During up-peak only one car waits at the lobby.

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

ROOT CAUSE

The extra lobby target is not added.

VERIFIED REPAIR

Put the second lobby target first so it is served first.

Unsuccessful approach: Appending the lobby target last loses it when cars run out.

Case contract

The building is split into zones of ceil(floors/zones) floors; each zone base floor is a parking target. Up-peak adds an extra lobby target first (lobby gets two cars); down-peak orders the bases from the top down; otherwise bases ascend. Each target in order takes the nearest free car (ties to the lowest car id); leftover targets stay empty.

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']
    k = x['zones']
    size = -(-n // k)
    bases = [z * size for z in range(k) if z * size < n]
    if x['period'] == 'up-peak':
        targets = bases
    elif x['period'] == 'down-peak':
        targets = sorted(bases, reverse=True)
    else:
        targets = bases
    free = dict(x['cars'])
    plan = {}
    for t in targets:
        if not free:
            break
        cid = min(free, key=lambda c: (abs(free[c] - t), c))
        plan[cid] = t
        del free[cid]
    return plan
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 19', {'floors': 19, 'zones': 1, 'period': 'up-peak', 'cars': [['C4', 1], ['C3', 13], ['C1', 7], ['C5', 12]]}, {'C4': 0, 'C1': 0}), ('sampled regression 6', {'floors': 30, 'zones': 4, 'period': 'up-peak', 'cars': [['C5', 9], ['C2', 3], ['C4', 5], ['C3', 18]]}, {'C2': 0, 'C4': 0, 'C5': 8, 'C3': 16}), ('boundary: ten floors in three zones', {'floors': 10, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 9], ['C2', 5], ['C3', 1]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('boundary: twelve floors in three zones', {'floors': 12, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 11], ['C2', 6], ['C3', 2]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('control 1', {'floors': 8, 'zones': 3, 'period': 'off-peak', 'cars': [['C2', 3], ['C1', 7], ['C5', 4]]}, {'C2': 0, 'C5': 3, 'C1': 6}), ('control 4', {'floors': 27, 'zones': 2, 'period': 'down-peak', 'cars': [['C3', 14], ['C5', 10]]}, {'C3': 14, 'C5': 0}), ('control 7', {'floors': 14, 'zones': 3, 'period': 'down-peak', 'cars': [['C5', 13], ['C4', 0], ['C2', 11], ['C3', 5], ['C1', 11]]}, {'C1': 10, 'C3': 5, 'C4': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 56', {'floors': 22, 'zones': 4, 'period': 'up-peak', 'cars': [['C5', 4], ['C4', 1]]}, {'C4': 0, 'C5': 0}), ('sampled regression 65', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C3', 10], ['C1', 0]]}, {'C1': 0, 'C3': 0}), ('boundary: down-peak top first', {'floors': 20, 'zones': 4, 'period': 'down-peak', 'cars': [['C1', 16], ['C2', 13], ['C3', 2]]}, {'C1': 15, 'C2': 10, 'C3': 5}), ('boundary: equidistant cars listed out of order', {'floors': 10, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 2], ['C1', 2]]}, {'C1': 0}), ('control 12', {'floors': 14, 'zones': 2, 'period': 'off-peak', 'cars': [['C3', 9], ['C5', 5]]}, {'C5': 0, 'C3': 7}), ('control 15', {'floors': 22, 'zones': 1, 'period': 'off-peak', 'cars': [['C5', 11]]}, {'C5': 0}), ('control 18', {'floors': 10, 'zones': 2, 'period': 'down-peak', 'cars': [['C4', 7], ['C2', 2], ['C1', 0], ['C5', 0], ['C3', 5]]}, {'C3': 5, 'C1': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 24', {'floors': 8, 'zones': 3, 'period': 'up-peak', 'cars': [['C5', 4], ['C4', 6], ['C1', 0], ['C2', 1], ['C3', 2]]}, {'C1': 0, 'C2': 0, 'C3': 3, 'C4': 6}), ('sampled regression 48', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 9], ['C3', 7], ['C2', 14], ['C1', 7]]}, {'C1': 0, 'C3': 0, 'C4': 6, 'C2': 12}), ('boundary: equidistant cars listed out of order', {'floors': 10, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 2], ['C1', 2]]}, {'C1': 0}), ('boundary: fewer cars than targets', {'floors': 20, 'zones': 4, 'period': 'up-peak', 'cars': [['C2', 7]]}, {'C2': 0}), ('control 23', {'floors': 8, 'zones': 4, 'period': 'down-peak', 'cars': [['C3', 5], ['C4', 7]]}, {'C3': 6, 'C4': 4}), ('control 26', {'floors': 9, 'zones': 2, 'period': 'down-peak', 'cars': [['C2', 1], ['C4', 8], ['C1', 0], ['C5', 8], ['C3', 6]]}, {'C3': 5, 'C1': 0}), ('control 29', {'floors': 30, 'zones': 3, 'period': 'down-peak', 'cars': [['C3', 28], ['C5', 0], ['C2', 17], ['C4', 12], ['C1', 29]]}, {'C2': 20, 'C4': 10, 'C5': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 65', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C3', 10], ['C1', 0]]}, {'C1': 0, 'C3': 0}), ('sampled regression 25', {'floors': 17, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 8], ['C2', 15], ['C3', 12], ['C5', 10]]}, {'C4': 0, 'C5': 0, 'C3': 6, 'C2': 12}), ('boundary: ten floors in three zones', {'floors': 10, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 9], ['C2', 5], ['C3', 1]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('boundary: twelve floors in three zones', {'floors': 12, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 11], ['C2', 6], ['C3', 2]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('control 34', {'floors': 17, 'zones': 2, 'period': 'off-peak', 'cars': [['C5', 2], ['C2', 7], ['C1', 0]]}, {'C1': 0, 'C2': 9}), ('control 37', {'floors': 9, 'zones': 2, 'period': 'down-peak', 'cars': [['C1', 3], ['C5', 8], ['C4', 7]]}, {'C1': 5, 'C4': 0}), ('control 40', {'floors': 24, 'zones': 1, 'period': 'off-peak', 'cars': [['C4', 6]]}, {'C4': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 25', {'floors': 17, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 8], ['C2', 15], ['C3', 12], ['C5', 10]]}, {'C4': 0, 'C5': 0, 'C3': 6, 'C2': 12}), ('boundary: down-peak top first', {'floors': 20, 'zones': 4, 'period': 'down-peak', 'cars': [['C1', 16], ['C2', 13], ['C3', 2]]}, {'C1': 15, 'C2': 10, 'C3': 5}), ('boundary: equidistant cars listed out of order', {'floors': 10, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 2], ['C1', 2]]}, {'C1': 0}), ('boundary: fewer cars than targets', {'floors': 20, 'zones': 4, 'period': 'up-peak', 'cars': [['C2', 7]]}, {'C2': 0}), ('control 45', {'floors': 9, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 6], ['C4', 1], ['C5', 0], ['C2', 1], ['C1', 7]]}, {'C5': 0}), ('sampled regression 48', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 9], ['C3', 7], ['C2', 14], ['C1', 7]]}, {'C1': 0, 'C3': 0, 'C4': 6, 'C2': 12}), ('control 51', {'floors': 18, 'zones': 4, 'period': 'off-peak', 'cars': [['C5', 11]]}, {'C5': 0})]]
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: up-peak lobby pair	`{'C1': 0, 'C2': 6}`	`{'C1': 0, 'C2': 0, 'C3': 6}`	Failed
sampled regression 19	`{'C4': 0}`	`{'C1': 0, 'C4': 0}`	Failed
sampled regression 6	`{'C2': 0, 'C3': 16, 'C4': 24, 'C5': 8}`	`{'C2': 0, 'C3': 16, 'C4': 0, 'C5': 8}`	Failed
boundary: ten floors in three zones	`{'C1': 8, 'C2': 4, 'C3': 0}`	`{'C1': 8, 'C2': 4, 'C3': 0}`	Passed
boundary: twelve floors in three zones	`{'C1': 8, 'C2': 4, 'C3': 0}`	`{'C1': 8, 'C2': 4, 'C3': 0}`	Passed
control 1	`{'C1': 6, 'C2': 0, 'C5': 3}`	`{'C1': 6, 'C2': 0, 'C5': 3}`	Passed
control 4	`{'C3': 14, 'C5': 0}`	`{'C3': 14, 'C5': 0}`	Passed
control 7	`{'C1': 10, 'C3': 5, 'C4': 0}`	`{'C1': 10, 'C3': 5, 'C4': 0}`	Passed

SHA-256 / 4a988aa1244f8b6261d16b4760cfcf3bfd332fce7e91055741aff91edc540420

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']
    k = x['zones']
    size = -(-n // k)
    bases = [z * size for z in range(k) if z * size < n]
    if x['period'] == 'up-peak':
        targets = bases + [0]
    elif x['period'] == 'down-peak':
        targets = sorted(bases, reverse=True)
    else:
        targets = bases
    free = dict(x['cars'])
    plan = {}
    for t in targets:
        if not free:
            break
        cid = min(free, key=lambda c: (abs(free[c] - t), c))
        plan[cid] = t
        del free[cid]
    return plan
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 19', {'floors': 19, 'zones': 1, 'period': 'up-peak', 'cars': [['C4', 1], ['C3', 13], ['C1', 7], ['C5', 12]]}, {'C4': 0, 'C1': 0}), ('sampled regression 6', {'floors': 30, 'zones': 4, 'period': 'up-peak', 'cars': [['C5', 9], ['C2', 3], ['C4', 5], ['C3', 18]]}, {'C2': 0, 'C4': 0, 'C5': 8, 'C3': 16}), ('boundary: ten floors in three zones', {'floors': 10, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 9], ['C2', 5], ['C3', 1]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('boundary: twelve floors in three zones', {'floors': 12, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 11], ['C2', 6], ['C3', 2]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('control 1', {'floors': 8, 'zones': 3, 'period': 'off-peak', 'cars': [['C2', 3], ['C1', 7], ['C5', 4]]}, {'C2': 0, 'C5': 3, 'C1': 6}), ('control 4', {'floors': 27, 'zones': 2, 'period': 'down-peak', 'cars': [['C3', 14], ['C5', 10]]}, {'C3': 14, 'C5': 0}), ('control 7', {'floors': 14, 'zones': 3, 'period': 'down-peak', 'cars': [['C5', 13], ['C4', 0], ['C2', 11], ['C3', 5], ['C1', 11]]}, {'C1': 10, 'C3': 5, 'C4': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 56', {'floors': 22, 'zones': 4, 'period': 'up-peak', 'cars': [['C5', 4], ['C4', 1]]}, {'C4': 0, 'C5': 0}), ('sampled regression 65', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C3', 10], ['C1', 0]]}, {'C1': 0, 'C3': 0}), ('boundary: down-peak top first', {'floors': 20, 'zones': 4, 'period': 'down-peak', 'cars': [['C1', 16], ['C2', 13], ['C3', 2]]}, {'C1': 15, 'C2': 10, 'C3': 5}), ('boundary: equidistant cars listed out of order', {'floors': 10, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 2], ['C1', 2]]}, {'C1': 0}), ('control 12', {'floors': 14, 'zones': 2, 'period': 'off-peak', 'cars': [['C3', 9], ['C5', 5]]}, {'C5': 0, 'C3': 7}), ('control 15', {'floors': 22, 'zones': 1, 'period': 'off-peak', 'cars': [['C5', 11]]}, {'C5': 0}), ('control 18', {'floors': 10, 'zones': 2, 'period': 'down-peak', 'cars': [['C4', 7], ['C2', 2], ['C1', 0], ['C5', 0], ['C3', 5]]}, {'C3': 5, 'C1': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 24', {'floors': 8, 'zones': 3, 'period': 'up-peak', 'cars': [['C5', 4], ['C4', 6], ['C1', 0], ['C2', 1], ['C3', 2]]}, {'C1': 0, 'C2': 0, 'C3': 3, 'C4': 6}), ('sampled regression 48', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 9], ['C3', 7], ['C2', 14], ['C1', 7]]}, {'C1': 0, 'C3': 0, 'C4': 6, 'C2': 12}), ('boundary: equidistant cars listed out of order', {'floors': 10, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 2], ['C1', 2]]}, {'C1': 0}), ('boundary: fewer cars than targets', {'floors': 20, 'zones': 4, 'period': 'up-peak', 'cars': [['C2', 7]]}, {'C2': 0}), ('control 23', {'floors': 8, 'zones': 4, 'period': 'down-peak', 'cars': [['C3', 5], ['C4', 7]]}, {'C3': 6, 'C4': 4}), ('control 26', {'floors': 9, 'zones': 2, 'period': 'down-peak', 'cars': [['C2', 1], ['C4', 8], ['C1', 0], ['C5', 8], ['C3', 6]]}, {'C3': 5, 'C1': 0}), ('control 29', {'floors': 30, 'zones': 3, 'period': 'down-peak', 'cars': [['C3', 28], ['C5', 0], ['C2', 17], ['C4', 12], ['C1', 29]]}, {'C2': 20, 'C4': 10, 'C5': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 65', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C3', 10], ['C1', 0]]}, {'C1': 0, 'C3': 0}), ('sampled regression 25', {'floors': 17, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 8], ['C2', 15], ['C3', 12], ['C5', 10]]}, {'C4': 0, 'C5': 0, 'C3': 6, 'C2': 12}), ('boundary: ten floors in three zones', {'floors': 10, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 9], ['C2', 5], ['C3', 1]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('boundary: twelve floors in three zones', {'floors': 12, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 11], ['C2', 6], ['C3', 2]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('control 34', {'floors': 17, 'zones': 2, 'period': 'off-peak', 'cars': [['C5', 2], ['C2', 7], ['C1', 0]]}, {'C1': 0, 'C2': 9}), ('control 37', {'floors': 9, 'zones': 2, 'period': 'down-peak', 'cars': [['C1', 3], ['C5', 8], ['C4', 7]]}, {'C1': 5, 'C4': 0}), ('control 40', {'floors': 24, 'zones': 1, 'period': 'off-peak', 'cars': [['C4', 6]]}, {'C4': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 25', {'floors': 17, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 8], ['C2', 15], ['C3', 12], ['C5', 10]]}, {'C4': 0, 'C5': 0, 'C3': 6, 'C2': 12}), ('boundary: down-peak top first', {'floors': 20, 'zones': 4, 'period': 'down-peak', 'cars': [['C1', 16], ['C2', 13], ['C3', 2]]}, {'C1': 15, 'C2': 10, 'C3': 5}), ('boundary: equidistant cars listed out of order', {'floors': 10, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 2], ['C1', 2]]}, {'C1': 0}), ('boundary: fewer cars than targets', {'floors': 20, 'zones': 4, 'period': 'up-peak', 'cars': [['C2', 7]]}, {'C2': 0}), ('control 45', {'floors': 9, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 6], ['C4', 1], ['C5', 0], ['C2', 1], ['C1', 7]]}, {'C5': 0}), ('sampled regression 48', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 9], ['C3', 7], ['C2', 14], ['C1', 7]]}, {'C1': 0, 'C3': 0, 'C4': 6, 'C2': 12}), ('control 51', {'floors': 18, 'zones': 4, 'period': 'off-peak', 'cars': [['C5', 11]]}, {'C5': 0})]]
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: up-peak lobby pair	`{'C1': 0, 'C2': 6, 'C3': 0}`	`{'C1': 0, 'C2': 0, 'C3': 6}`	Failed
sampled regression 19	`{'C1': 0, 'C4': 0}`	`{'C1': 0, 'C4': 0}`	Passed
sampled regression 6	`{'C2': 0, 'C3': 16, 'C4': 24, 'C5': 8}`	`{'C2': 0, 'C3': 16, 'C4': 0, 'C5': 8}`	Failed
boundary: ten floors in three zones	`{'C1': 8, 'C2': 4, 'C3': 0}`	`{'C1': 8, 'C2': 4, 'C3': 0}`	Passed
boundary: twelve floors in three zones	`{'C1': 8, 'C2': 4, 'C3': 0}`	`{'C1': 8, 'C2': 4, 'C3': 0}`	Passed
control 1	`{'C1': 6, 'C2': 0, 'C5': 3}`	`{'C1': 6, 'C2': 0, 'C5': 3}`	Passed
control 4	`{'C3': 14, 'C5': 0}`	`{'C3': 14, 'C5': 0}`	Passed
control 7	`{'C1': 10, 'C3': 5, 'C4': 0}`	`{'C1': 10, 'C3': 5, 'C4': 0}`	Passed

SHA-256 / 3bc31eb6de26997db177592a0be5373f617834c1585216798a5a87fc0a00cf18

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']
    k = x['zones']
    size = -(-n // k)
    bases = [z * size for z in range(k) if z * size < n]
    if x['period'] == 'up-peak':
        targets = [0] + bases
    elif x['period'] == 'down-peak':
        targets = sorted(bases, reverse=True)
    else:
        targets = bases
    free = dict(x['cars'])
    plan = {}
    for t in targets:
        if not free:
            break
        cid = min(free, key=lambda c: (abs(free[c] - t), c))
        plan[cid] = t
        del free[cid]
    return plan
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 19', {'floors': 19, 'zones': 1, 'period': 'up-peak', 'cars': [['C4', 1], ['C3', 13], ['C1', 7], ['C5', 12]]}, {'C4': 0, 'C1': 0}), ('sampled regression 6', {'floors': 30, 'zones': 4, 'period': 'up-peak', 'cars': [['C5', 9], ['C2', 3], ['C4', 5], ['C3', 18]]}, {'C2': 0, 'C4': 0, 'C5': 8, 'C3': 16}), ('boundary: ten floors in three zones', {'floors': 10, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 9], ['C2', 5], ['C3', 1]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('boundary: twelve floors in three zones', {'floors': 12, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 11], ['C2', 6], ['C3', 2]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('control 1', {'floors': 8, 'zones': 3, 'period': 'off-peak', 'cars': [['C2', 3], ['C1', 7], ['C5', 4]]}, {'C2': 0, 'C5': 3, 'C1': 6}), ('control 4', {'floors': 27, 'zones': 2, 'period': 'down-peak', 'cars': [['C3', 14], ['C5', 10]]}, {'C3': 14, 'C5': 0}), ('control 7', {'floors': 14, 'zones': 3, 'period': 'down-peak', 'cars': [['C5', 13], ['C4', 0], ['C2', 11], ['C3', 5], ['C1', 11]]}, {'C1': 10, 'C3': 5, 'C4': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 56', {'floors': 22, 'zones': 4, 'period': 'up-peak', 'cars': [['C5', 4], ['C4', 1]]}, {'C4': 0, 'C5': 0}), ('sampled regression 65', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C3', 10], ['C1', 0]]}, {'C1': 0, 'C3': 0}), ('boundary: down-peak top first', {'floors': 20, 'zones': 4, 'period': 'down-peak', 'cars': [['C1', 16], ['C2', 13], ['C3', 2]]}, {'C1': 15, 'C2': 10, 'C3': 5}), ('boundary: equidistant cars listed out of order', {'floors': 10, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 2], ['C1', 2]]}, {'C1': 0}), ('control 12', {'floors': 14, 'zones': 2, 'period': 'off-peak', 'cars': [['C3', 9], ['C5', 5]]}, {'C5': 0, 'C3': 7}), ('control 15', {'floors': 22, 'zones': 1, 'period': 'off-peak', 'cars': [['C5', 11]]}, {'C5': 0}), ('control 18', {'floors': 10, 'zones': 2, 'period': 'down-peak', 'cars': [['C4', 7], ['C2', 2], ['C1', 0], ['C5', 0], ['C3', 5]]}, {'C3': 5, 'C1': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 24', {'floors': 8, 'zones': 3, 'period': 'up-peak', 'cars': [['C5', 4], ['C4', 6], ['C1', 0], ['C2', 1], ['C3', 2]]}, {'C1': 0, 'C2': 0, 'C3': 3, 'C4': 6}), ('sampled regression 48', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 9], ['C3', 7], ['C2', 14], ['C1', 7]]}, {'C1': 0, 'C3': 0, 'C4': 6, 'C2': 12}), ('boundary: equidistant cars listed out of order', {'floors': 10, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 2], ['C1', 2]]}, {'C1': 0}), ('boundary: fewer cars than targets', {'floors': 20, 'zones': 4, 'period': 'up-peak', 'cars': [['C2', 7]]}, {'C2': 0}), ('control 23', {'floors': 8, 'zones': 4, 'period': 'down-peak', 'cars': [['C3', 5], ['C4', 7]]}, {'C3': 6, 'C4': 4}), ('control 26', {'floors': 9, 'zones': 2, 'period': 'down-peak', 'cars': [['C2', 1], ['C4', 8], ['C1', 0], ['C5', 8], ['C3', 6]]}, {'C3': 5, 'C1': 0}), ('control 29', {'floors': 30, 'zones': 3, 'period': 'down-peak', 'cars': [['C3', 28], ['C5', 0], ['C2', 17], ['C4', 12], ['C1', 29]]}, {'C2': 20, 'C4': 10, 'C5': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 65', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C3', 10], ['C1', 0]]}, {'C1': 0, 'C3': 0}), ('sampled regression 25', {'floors': 17, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 8], ['C2', 15], ['C3', 12], ['C5', 10]]}, {'C4': 0, 'C5': 0, 'C3': 6, 'C2': 12}), ('boundary: ten floors in three zones', {'floors': 10, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 9], ['C2', 5], ['C3', 1]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('boundary: twelve floors in three zones', {'floors': 12, 'zones': 3, 'period': 'off-peak', 'cars': [['C1', 11], ['C2', 6], ['C3', 2]]}, {'C3': 0, 'C2': 4, 'C1': 8}), ('control 34', {'floors': 17, 'zones': 2, 'period': 'off-peak', 'cars': [['C5', 2], ['C2', 7], ['C1', 0]]}, {'C1': 0, 'C2': 9}), ('control 37', {'floors': 9, 'zones': 2, 'period': 'down-peak', 'cars': [['C1', 3], ['C5', 8], ['C4', 7]]}, {'C1': 5, 'C4': 0}), ('control 40', {'floors': 24, 'zones': 1, 'period': 'off-peak', 'cars': [['C4', 6]]}, {'C4': 0})], [('regression: up-peak lobby pair', {'floors': 12, 'zones': 2, 'period': 'up-peak', 'cars': [['C1', 3], ['C2', 8], ['C3', 11]]}, {'C1': 0, 'C2': 0, 'C3': 6}), ('sampled regression 25', {'floors': 17, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 8], ['C2', 15], ['C3', 12], ['C5', 10]]}, {'C4': 0, 'C5': 0, 'C3': 6, 'C2': 12}), ('boundary: down-peak top first', {'floors': 20, 'zones': 4, 'period': 'down-peak', 'cars': [['C1', 16], ['C2', 13], ['C3', 2]]}, {'C1': 15, 'C2': 10, 'C3': 5}), ('boundary: equidistant cars listed out of order', {'floors': 10, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 2], ['C1', 2]]}, {'C1': 0}), ('boundary: fewer cars than targets', {'floors': 20, 'zones': 4, 'period': 'up-peak', 'cars': [['C2', 7]]}, {'C2': 0}), ('control 45', {'floors': 9, 'zones': 1, 'period': 'off-peak', 'cars': [['C3', 6], ['C4', 1], ['C5', 0], ['C2', 1], ['C1', 7]]}, {'C5': 0}), ('sampled regression 48', {'floors': 16, 'zones': 3, 'period': 'up-peak', 'cars': [['C4', 9], ['C3', 7], ['C2', 14], ['C1', 7]]}, {'C1': 0, 'C3': 0, 'C4': 6, 'C2': 12}), ('control 51', {'floors': 18, 'zones': 4, 'period': 'off-peak', 'cars': [['C5', 11]]}, {'C5': 0})]]
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: up-peak lobby pair	`{'C1': 0, 'C2': 0, 'C3': 6}`	`{'C1': 0, 'C2': 0, 'C3': 6}`	Passed
sampled regression 19	`{'C1': 0, 'C4': 0}`	`{'C1': 0, 'C4': 0}`	Passed
sampled regression 6	`{'C2': 0, 'C3': 16, 'C4': 0, 'C5': 8}`	`{'C2': 0, 'C3': 16, 'C4': 0, 'C5': 8}`	Passed
boundary: ten floors in three zones	`{'C1': 8, 'C2': 4, 'C3': 0}`	`{'C1': 8, 'C2': 4, 'C3': 0}`	Passed
boundary: twelve floors in three zones	`{'C1': 8, 'C2': 4, 'C3': 0}`	`{'C1': 8, 'C2': 4, 'C3': 0}`	Passed
control 1	`{'C1': 6, 'C2': 0, 'C5': 3}`	`{'C1': 6, 'C2': 0, 'C5': 3}`	Passed
control 4	`{'C3': 14, 'C5': 0}`	`{'C3': 14, 'C5': 0}`	Passed
control 7	`{'C1': 10, 'C3': 5, 'C4': 0}`	`{'C1': 10, 'C3': 5, 'C4': 0}`	Passed

SHA-256 / d9e327eb0b037f67e932b07fbbc35de3308c4c2ab292d374699a93e1831e5565

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 2026-09-29T14:47:53.281212+00:00.

Case digest / 51c616108967d2837f0eec72352117fbe5d113a9b0c5d81bdbd4a0be9fa54e99