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FA-92871 / EV charging session scheduling / Open access

Time-of-use cheapest slot plan: cost uses slot tariff · case 01

The quoted cost does not match the tariff of the slots listed in the plan.

Verified by executionVariant 1 · 7 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

After the plan is re-sorted by time it is zipped with the price-ranked slot list, pairing energy with the wrong tariff.

VERIFIED REPAIR

Price each plan entry by its own slot index.

Unsuccessful approach: Indexing prices relative to the window start shifts every tariff lookup for late arrivals.

Case contract

prices[i] is the tariff in cents/kWh of 15-minute slot i. The car is present for slots arrive..depart-1 (depart exclusive) clipped to the tariff horizon; arrive may be negative. Each slot delivers at most max_w//4 Wh. Fill the cheapest slots first (equal price: earlier slot first), the last slot partially. Return [[slot, wh] sorted by slot, cost in millicents (sum wh*price), unmet wh]. need_wh<=0 or max_w<=0 returns [[], 0, max(need_wh,0)].

Why this case matters

Depot, workplace and public EV chargers schedule sessions against prices, circuit limits and departure deadlines; a wrong decision silently strands a driver or overloads a feeder.

1 / The failure

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

N = 1
observations = []
def solve(prices, arrive, depart, need_wh, max_w):
    if need_wh <= 0 or max_w <= 0:
        return [[], 0, max(need_wh, 0)]
    slot_wh = max_w // 4
    lo = max(arrive, 0)
    hi = min(depart, len(prices))
    window = list(range(lo, hi))
    ranked = sorted(window, key=lambda s: (prices[s], s))
    plan = []
    left = need_wh
    for s in ranked:
        if left <= 0:
            break
        take = min(slot_wh, left)
        plan.append([s, take])
        left -= take
    plan.sort()
    cost = sum(wh * prices[r] for (s, wh), r in zip(plan, ranked))
    return [plan, cost, left]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff',
   [[12, 10, 8, 25, 30, 10, 20, 10, 8, 10, 10, 25, 25], 3, 16, 1500, 3700],
   [[[5, 575], [8, 925]], 13150, 0]],
  ['regression: cost uses slot tariff (partial repair)', [[20, 8, 15, 15], 3, 6, 1500, 11000],
   [[[3, 1500]], 22500, 0]],
  ['control 1', [[25, 30, 20, 12, 12], 2, 2, 20000, 11000], [[], 0, 20000]],
  ['control 2', [[8, 15, 12, 10, 8, 25, 12, 8, 8, 20, 20, 25, 12, 8], -3, 15, 7000, 3700],
   [[[0, 925], [2, 925], [3, 925], [4, 925], [6, 525], [7, 925], [8, 925], [13, 925]], 63650, 0]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff',
   [[30, 12, 20, 30, 25, 30, 12, 30, 20, 10, 20, 8, 15], 7, 12, 4000, 11000],
   [[[9, 1250], [11, 2750]], 34500, 0]],
  ['regression: cost uses slot tariff (partial repair)',
   [[30, 12, 30, 8, 15, 25, 8, 15, 15, 12, 8], 8, 9, 7000, 3700], [[[8, 925]], 13875, 6075]],
  ['control 1', [[12, 12, 25, 30, 8, 20, 12, 12], 3, 11, 9000, 3700],
   [[[3, 925], [4, 925], [5, 925], [6, 925], [7, 925]], 75850, 4375]],
  ['control 2', [[12, 15, 8, 12, 25, 15, 8, 30], -1, 6, 20000, 3700],
   [[[0, 925], [1, 925], [2, 925], [3, 925], [4, 925], [5, 925]], 80475, 14450]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff', [[15, 15, 25, 20], -1, 7, 7000, 7400],
   [[[0, 1850], [1, 1850], [2, 1450], [3, 1850]], 128750, 0]],
  ['regression: cost uses slot tariff (partial repair)',
   [[10, 25, 25, 30, 8, 12, 25, 8, 25, 25], 3, 13, 9000, 3700],
   [[[3, 925], [4, 925], [5, 925], [6, 925], [7, 925], [8, 925], [9, 925]], 123025, 2525]],
  ['control 1', [[12, 10, 15, 25, 30], 4, 4, 12500, 7400], [[], 0, 12500]],
  ['control 2', [[30, 12, 15, 12, 30, 15, 12, 20, 20], -2, 1, 9000, 0], [[], 0, 9000]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff', [[10, 12, 10, 8, 10, 12, 12], 2, 5, 4000, 11000],
   [[[2, 1250], [3, 2750]], 34500, 0]],
  ['regression: cost uses slot tariff (partial repair)', [[10, 10, 12, 25, 30], 2, 7, 1500, 7400],
   [[[2, 1500]], 18000, 0]],
  ['control 1', [[12, 20, 8, 15, 10, 8, 25, 12, 30, 12], 2, 12, 20000, 11000],
   [[[2, 2750], [3, 2750], [4, 2750], [5, 2750], [6, 2750], [7, 2750], [8, 750], [9, 2750]], 270000,
    0]],
  ['control 2', [[30, 30, 20, 12, 12, 15, 12, 10], -1, 8, 7000, 7400],
   [[[3, 1850], [4, 1850], [6, 1450], [7, 1850]], 80300, 0]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff',
   [[12, 15, 15, 8, 12, 15, 10, 25, 25, 20, 20], 0, 14, 9000, 3700],
   [[[0, 925], [1, 925], [2, 925], [3, 925], [4, 925], [5, 925], [6, 925], [7, 675], [9, 925],
     [10, 925]],
    134350, 0]],
  ['regression: cost uses slot tariff (partial repair)',
   [[8, 25, 15, 12, 12, 12, 20, 25, 10, 12, 20, 20], 10, 14, 9000, 11000],
   [[[10, 2750], [11, 2750]], 110000, 3500]],
  ['control 1', [[12, 12, 8, 15, 10, 12, 20, 15, 12, 10, 12, 12, 15, 15], 12, 13, 1500, 3700],
   [[[12, 925]], 13875, 575]],
  ['control 2', [[10, 12, 10, 8, 10, 12, 12], 2, 5, 4000, 11000],
   [[[2, 1250], [3, 2750]], 34500, 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 fixtureActualExpectedOutcome
boundary: nothing needed[[], 0, 0][[], 0, 0]Passed
boundary: empty window[[], 0, 5000][[], 0, 5000]Passed
boundary: departure beyond tariff horizon[[[1, 1850], [2, 1850], [3, 1300]], 63000, 0][[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]Passed
regression: cost uses slot tariff[[[5, 575], [8, 925]], 13850, 0][[[5, 575], [8, 925]], 13150, 0]Failed
regression: cost uses slot tariff (partial repair)[[[3, 1500]], 22500, 0][[[3, 1500]], 22500, 0]Passed
control 1[[], 0, 20000][[], 0, 20000]Passed
control 2[[[0, 925], [2, 925], [3, 925], [4, 925], [6, 525], [7, 925], [8, 925], [13, 925]], 65250, 0][[[0, 925], [2, 925], [3, 925], [4, 925], [6, 525], [7, 925], [8, 925], [13, 925]], 63650, 0]Failed

SHA-256 / aed35cc8ed7b9653b96832f901138a3661124c9b907db87894d5aa7be9712890

2 / The unsuccessful fix

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

N = 1
observations = []
def solve(prices, arrive, depart, need_wh, max_w):
    if need_wh <= 0 or max_w <= 0:
        return [[], 0, max(need_wh, 0)]
    slot_wh = max_w // 4
    lo = max(arrive, 0)
    hi = min(depart, len(prices))
    window = list(range(lo, hi))
    ranked = sorted(window, key=lambda s: (prices[s], s))
    plan = []
    left = need_wh
    for s in ranked:
        if left <= 0:
            break
        take = min(slot_wh, left)
        plan.append([s, take])
        left -= take
    plan.sort()
    cost = sum(wh * prices[s - lo] for s, wh in plan)
    return [plan, cost, left]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff',
   [[12, 10, 8, 25, 30, 10, 20, 10, 8, 10, 10, 25, 25], 3, 16, 1500, 3700],
   [[[5, 575], [8, 925]], 13150, 0]],
  ['regression: cost uses slot tariff (partial repair)', [[20, 8, 15, 15], 3, 6, 1500, 11000],
   [[[3, 1500]], 22500, 0]],
  ['control 1', [[25, 30, 20, 12, 12], 2, 2, 20000, 11000], [[], 0, 20000]],
  ['control 2', [[8, 15, 12, 10, 8, 25, 12, 8, 8, 20, 20, 25, 12, 8], -3, 15, 7000, 3700],
   [[[0, 925], [2, 925], [3, 925], [4, 925], [6, 525], [7, 925], [8, 925], [13, 925]], 63650, 0]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff',
   [[30, 12, 20, 30, 25, 30, 12, 30, 20, 10, 20, 8, 15], 7, 12, 4000, 11000],
   [[[9, 1250], [11, 2750]], 34500, 0]],
  ['regression: cost uses slot tariff (partial repair)',
   [[30, 12, 30, 8, 15, 25, 8, 15, 15, 12, 8], 8, 9, 7000, 3700], [[[8, 925]], 13875, 6075]],
  ['control 1', [[12, 12, 25, 30, 8, 20, 12, 12], 3, 11, 9000, 3700],
   [[[3, 925], [4, 925], [5, 925], [6, 925], [7, 925]], 75850, 4375]],
  ['control 2', [[12, 15, 8, 12, 25, 15, 8, 30], -1, 6, 20000, 3700],
   [[[0, 925], [1, 925], [2, 925], [3, 925], [4, 925], [5, 925]], 80475, 14450]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff', [[15, 15, 25, 20], -1, 7, 7000, 7400],
   [[[0, 1850], [1, 1850], [2, 1450], [3, 1850]], 128750, 0]],
  ['regression: cost uses slot tariff (partial repair)',
   [[10, 25, 25, 30, 8, 12, 25, 8, 25, 25], 3, 13, 9000, 3700],
   [[[3, 925], [4, 925], [5, 925], [6, 925], [7, 925], [8, 925], [9, 925]], 123025, 2525]],
  ['control 1', [[12, 10, 15, 25, 30], 4, 4, 12500, 7400], [[], 0, 12500]],
  ['control 2', [[30, 12, 15, 12, 30, 15, 12, 20, 20], -2, 1, 9000, 0], [[], 0, 9000]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff', [[10, 12, 10, 8, 10, 12, 12], 2, 5, 4000, 11000],
   [[[2, 1250], [3, 2750]], 34500, 0]],
  ['regression: cost uses slot tariff (partial repair)', [[10, 10, 12, 25, 30], 2, 7, 1500, 7400],
   [[[2, 1500]], 18000, 0]],
  ['control 1', [[12, 20, 8, 15, 10, 8, 25, 12, 30, 12], 2, 12, 20000, 11000],
   [[[2, 2750], [3, 2750], [4, 2750], [5, 2750], [6, 2750], [7, 2750], [8, 750], [9, 2750]], 270000,
    0]],
  ['control 2', [[30, 30, 20, 12, 12, 15, 12, 10], -1, 8, 7000, 7400],
   [[[3, 1850], [4, 1850], [6, 1450], [7, 1850]], 80300, 0]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff',
   [[12, 15, 15, 8, 12, 15, 10, 25, 25, 20, 20], 0, 14, 9000, 3700],
   [[[0, 925], [1, 925], [2, 925], [3, 925], [4, 925], [5, 925], [6, 925], [7, 675], [9, 925],
     [10, 925]],
    134350, 0]],
  ['regression: cost uses slot tariff (partial repair)',
   [[8, 25, 15, 12, 12, 12, 20, 25, 10, 12, 20, 20], 10, 14, 9000, 11000],
   [[[10, 2750], [11, 2750]], 110000, 3500]],
  ['control 1', [[12, 12, 8, 15, 10, 12, 20, 15, 12, 10, 12, 12, 15, 15], 12, 13, 1500, 3700],
   [[[12, 925]], 13875, 575]],
  ['control 2', [[10, 12, 10, 8, 10, 12, 12], 2, 5, 4000, 11000],
   [[[2, 1250], [3, 2750]], 34500, 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 fixtureActualExpectedOutcome
boundary: nothing needed[[], 0, 0][[], 0, 0]Passed
boundary: empty window[[], 0, 5000][[], 0, 5000]Passed
boundary: departure beyond tariff horizon[[[1, 1850], [2, 1850], [3, 1300]], 87000, 0][[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]Failed
regression: cost uses slot tariff[[[5, 575], [8, 925]], 13850, 0][[[5, 575], [8, 925]], 13150, 0]Failed
regression: cost uses slot tariff (partial repair)[[[3, 1500]], 30000, 0][[[3, 1500]], 22500, 0]Failed
control 1[[], 0, 20000][[], 0, 20000]Passed
control 2[[[0, 925], [2, 925], [3, 925], [4, 925], [6, 525], [7, 925], [8, 925], [13, 925]], 63650, 0][[[0, 925], [2, 925], [3, 925], [4, 925], [6, 525], [7, 925], [8, 925], [13, 925]], 63650, 0]Passed

SHA-256 / 1f7898e7afb1c97bbe32a4f967ee910a0019bc7d414faebce61d13a4fc6e74b9

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(prices, arrive, depart, need_wh, max_w):
    if need_wh <= 0 or max_w <= 0:
        return [[], 0, max(need_wh, 0)]
    slot_wh = max_w // 4
    lo = max(arrive, 0)
    hi = min(depart, len(prices))
    window = list(range(lo, hi))
    ranked = sorted(window, key=lambda s: (prices[s], s))
    plan = []
    left = need_wh
    for s in ranked:
        if left <= 0:
            break
        take = min(slot_wh, left)
        plan.append([s, take])
        left -= take
    plan.sort()
    cost = sum(wh * prices[s] for s, wh in plan)
    return [plan, cost, left]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff',
   [[12, 10, 8, 25, 30, 10, 20, 10, 8, 10, 10, 25, 25], 3, 16, 1500, 3700],
   [[[5, 575], [8, 925]], 13150, 0]],
  ['regression: cost uses slot tariff (partial repair)', [[20, 8, 15, 15], 3, 6, 1500, 11000],
   [[[3, 1500]], 22500, 0]],
  ['control 1', [[25, 30, 20, 12, 12], 2, 2, 20000, 11000], [[], 0, 20000]],
  ['control 2', [[8, 15, 12, 10, 8, 25, 12, 8, 8, 20, 20, 25, 12, 8], -3, 15, 7000, 3700],
   [[[0, 925], [2, 925], [3, 925], [4, 925], [6, 525], [7, 925], [8, 925], [13, 925]], 63650, 0]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff',
   [[30, 12, 20, 30, 25, 30, 12, 30, 20, 10, 20, 8, 15], 7, 12, 4000, 11000],
   [[[9, 1250], [11, 2750]], 34500, 0]],
  ['regression: cost uses slot tariff (partial repair)',
   [[30, 12, 30, 8, 15, 25, 8, 15, 15, 12, 8], 8, 9, 7000, 3700], [[[8, 925]], 13875, 6075]],
  ['control 1', [[12, 12, 25, 30, 8, 20, 12, 12], 3, 11, 9000, 3700],
   [[[3, 925], [4, 925], [5, 925], [6, 925], [7, 925]], 75850, 4375]],
  ['control 2', [[12, 15, 8, 12, 25, 15, 8, 30], -1, 6, 20000, 3700],
   [[[0, 925], [1, 925], [2, 925], [3, 925], [4, 925], [5, 925]], 80475, 14450]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff', [[15, 15, 25, 20], -1, 7, 7000, 7400],
   [[[0, 1850], [1, 1850], [2, 1450], [3, 1850]], 128750, 0]],
  ['regression: cost uses slot tariff (partial repair)',
   [[10, 25, 25, 30, 8, 12, 25, 8, 25, 25], 3, 13, 9000, 3700],
   [[[3, 925], [4, 925], [5, 925], [6, 925], [7, 925], [8, 925], [9, 925]], 123025, 2525]],
  ['control 1', [[12, 10, 15, 25, 30], 4, 4, 12500, 7400], [[], 0, 12500]],
  ['control 2', [[30, 12, 15, 12, 30, 15, 12, 20, 20], -2, 1, 9000, 0], [[], 0, 9000]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff', [[10, 12, 10, 8, 10, 12, 12], 2, 5, 4000, 11000],
   [[[2, 1250], [3, 2750]], 34500, 0]],
  ['regression: cost uses slot tariff (partial repair)', [[10, 10, 12, 25, 30], 2, 7, 1500, 7400],
   [[[2, 1500]], 18000, 0]],
  ['control 1', [[12, 20, 8, 15, 10, 8, 25, 12, 30, 12], 2, 12, 20000, 11000],
   [[[2, 2750], [3, 2750], [4, 2750], [5, 2750], [6, 2750], [7, 2750], [8, 750], [9, 2750]], 270000,
    0]],
  ['control 2', [[30, 30, 20, 12, 12, 15, 12, 10], -1, 8, 7000, 7400],
   [[[3, 1850], [4, 1850], [6, 1450], [7, 1850]], 80300, 0]]],
 [['boundary: nothing needed', [[10, 20, 30], 0, 3, 0, 7400], [[], 0, 0]],
  ['boundary: empty window', [[10, 20, 30], 2, 2, 5000, 7400], [[], 0, 5000]],
  ['boundary: departure beyond tariff horizon', [[30, 10, 10, 20], 1, 9, 5000, 7400],
   [[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]],
  ['regression: cost uses slot tariff',
   [[12, 15, 15, 8, 12, 15, 10, 25, 25, 20, 20], 0, 14, 9000, 3700],
   [[[0, 925], [1, 925], [2, 925], [3, 925], [4, 925], [5, 925], [6, 925], [7, 675], [9, 925],
     [10, 925]],
    134350, 0]],
  ['regression: cost uses slot tariff (partial repair)',
   [[8, 25, 15, 12, 12, 12, 20, 25, 10, 12, 20, 20], 10, 14, 9000, 11000],
   [[[10, 2750], [11, 2750]], 110000, 3500]],
  ['control 1', [[12, 12, 8, 15, 10, 12, 20, 15, 12, 10, 12, 12, 15, 15], 12, 13, 1500, 3700],
   [[[12, 925]], 13875, 575]],
  ['control 2', [[10, 12, 10, 8, 10, 12, 12], 2, 5, 4000, 11000],
   [[[2, 1250], [3, 2750]], 34500, 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 fixtureActualExpectedOutcome
boundary: nothing needed[[], 0, 0][[], 0, 0]Passed
boundary: empty window[[], 0, 5000][[], 0, 5000]Passed
boundary: departure beyond tariff horizon[[[1, 1850], [2, 1850], [3, 1300]], 63000, 0][[[1, 1850], [2, 1850], [3, 1300]], 63000, 0]Passed
regression: cost uses slot tariff[[[5, 575], [8, 925]], 13150, 0][[[5, 575], [8, 925]], 13150, 0]Passed
regression: cost uses slot tariff (partial repair)[[[3, 1500]], 22500, 0][[[3, 1500]], 22500, 0]Passed
control 1[[], 0, 20000][[], 0, 20000]Passed
control 2[[[0, 925], [2, 925], [3, 925], [4, 925], [6, 525], [7, 925], [8, 925], [13, 925]], 63650, 0][[[0, 925], [2, 925], [3, 925], [4, 925], [6, 525], [7, 925], [8, 925], [13, 925]], 63650, 0]Passed

SHA-256 / 49356f2b947998dd94ed4d76d0c81257b3215332d76d223d63e174bf81a2dc77

Verification & scope

Deterministic stipulated toy contract for teaching; no claim of conformance with any standard, vendor protocol or production controller. 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 2026-09-29T14:51:49.885174+00:00.

Case digest / bc9fd67bcf6f89d37e63cab0742838f4da8b9781640065f7666ad03d268d1cd6