Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Optimal single-machine job sequence (weighted tardiness with precedence)

AClaude Sonnet 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.06
Wall time: 34s
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Optimal job sequence: minimum total weighted tardiness with precedence `input.json` (synthetic) has `start` (time the machine becomes free), `jobs` (`id`, processing time `p`, weight `w`, `due`) and `precedence` (pairs [a, b]: a must come before b, not necessarily directly). One machine, no idle time, no preemption. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json`. Find a sequence of all jobs that respects every precedence pair and **minimises** total weighted tardiness: sum of w * max(0, C - due), where C is the job's completion time (start + sum of p up to and including the job). Any optimal sequence is accepted. Fresh instances have **20 and 21 jobs**; each run has a 90-second limit. ## answer.json `{"sequence": [job ids in order], "total_weighted_tardiness": integer}` ## Scoring Hidden checks (partial credit; all must pass). A sequence is **valid** if it is a permutation of the job ids, respects every precedence pair, and the reported objective equals the scorer's recomputation for that sequence. It is **optimal** if that objective equals the true minimum (the scorer has the optimum). `fixture_valid_sequence`, `fixture_optimal` (given 18-job instance); `fresh_20_jobs_valid`, `fresh_20_jobs_optimal`; `fresh_21_jobs_optimal`.

Greedy rules (WSPT, EDD, swaps) give valid but suboptimal sequences; an exact method (subset dynamic programming or a tight branch and bound) is needed. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Success criteria: 1. fixture_valid_sequence 2. fixture_optimal 3. fresh_20_jobs_valid 4. fresh_20_jobs_optimal 5. fresh_21_jobs_optimal

Setup notes: Use Case Scout batch 12 case m07-weighted-tardiness-optimal (offline, synthetic fixture). Prepared with batch12/prepare.sh; hidden key/checker kept outside the agent's work dir (temp dir during the run). Prompt = TASK.md verbatim. Scored with the case's scorer: `python score.py WORKDIR KEYDIR` (python3, run from the case dir). Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.31 (8 cores). Claude Code's own reported cost for claude-sonnet-5.5: $0.056755. Score: 5/5. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.06
Wall time: 34s
Human step-ins: 0
Turns: 5

Model usage
Model	Input tok	Output tok	Cost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on	97,245	1,649	$0.06

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Sonnet 5.5 (anthropic/claude-sonnet-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
It counts as a success only if it meets every success criterion: 1. fixture_valid_sequence 2. fixture_optimal 3. fresh_20_jobs_valid 4. fresh_20_jobs_optimal 5. fresh_21_jobs_optimal
Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Automated checks

Trust 80% comes from these checks (how Trust % works).

Run date is not in the future : passed
Model is in the dated pricing table : passed
Cost recomputed from tokens matches : passed
Cached tokens are consistent : passed
Output throughput is plausible : passed
Autonomy matches step-ins : passed
Step-ins do not exceed turns : passed
Retries agree with first-try result : passed
Evidence log SHA-256 matches : passed
Evidence log is substantial : passed

Evidence log

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  "result": "`solve.py` is written. It uses an exact dynamic program over sets of jobs that satisfy the precedence pairs, so it returns a true optimum. I checked it only on the 18-job fixture and one stress test, not on the hidden 20- and 21-job instances.\n\n- **Fixture:** on the 18-job `input.json` it ran in 0.3 seconds. It found a sequence with total weighted tardiness 462. I did not check that sequence against the precedence pairs or the objective with a separate script. I also don't know whether 462 is the true minimum, since I don't have the scorer's value.\n- **Stress test:** I added 3 random jobs to the fixture and removed all precedence pairs. That made a 21-job instance with the largest possible search space, and it took about 8 seconds, well inside the 90-second limit.\n\nRun it with `python3 solve.py input.json answer.json`. The sandbox has no `python` command, only `python3`.\n\nI did not save the stress-test file anywhere in the working directory. `answer.json` there is the fixture's output.",
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 }
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