From b4f7e48972db75b74d729f9b279f9548d242d9ee Mon Sep 17 00:00:00 2001 From: Michelle Han <112435805+MichelleHan7@users.noreply.github.com> Date: Fri, 9 Oct 2026 01:00:44 -0400 Subject: [PATCH 1/2] docs: publish verified benchmark results, charts and implementation report --- .gitattributes | 1 + README.md | 255 +++-- docs/architecture.md | 34 + docs/benchmark-results.md | 50 + docs/final-implementation-report.md | 106 +++ docs/resume-metrics.md | 29 + docs/v2-design-decisions.md | 45 + docs/v2-implementation.md | 13 +- docs/weather-providers.md | 40 + reports/benchmark-results.md | 50 + reports/benchmark_details.jsonl.gz | Bin 0 -> 124334 bytes reports/benchmark_environment.json | 98 ++ reports/benchmark_results.csv | 661 +++++++++++++ reports/benchmark_resume_environment.json | 14 + reports/benchmark_summary.json | 891 ++++++++++++++++++ reports/heat_reduction.png | Bin 0 -> 53612 bytes reports/profiling.csv | 11 + reports/scalability.png | Bin 0 -> 66941 bytes .../serial-partial/benchmark_details.jsonl.gz | Bin 0 -> 100965 bytes .../serial-partial/benchmark_environment.json | 94 ++ reports/tradeoff.png | Bin 0 -> 86157 bytes scripts/profile_scheduler.py | 49 + 22 files changed, 2307 insertions(+), 134 deletions(-) create mode 100644 .gitattributes create mode 100644 docs/benchmark-results.md create mode 100644 docs/final-implementation-report.md create mode 100644 docs/resume-metrics.md create mode 100644 docs/v2-design-decisions.md create mode 100644 docs/weather-providers.md create mode 100644 reports/benchmark-results.md create mode 100644 reports/benchmark_details.jsonl.gz create mode 100644 reports/benchmark_environment.json create mode 100644 reports/benchmark_results.csv create mode 100644 reports/benchmark_resume_environment.json create mode 100644 reports/benchmark_summary.json create mode 100644 reports/heat_reduction.png create mode 100644 reports/profiling.csv create mode 100644 reports/scalability.png create mode 100644 reports/serial-partial/benchmark_details.jsonl.gz create mode 100644 reports/serial-partial/benchmark_environment.json create mode 100644 reports/tradeoff.png create mode 100644 scripts/profile_scheduler.py diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..1d18472 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +*.csv text eol=lf diff --git a/README.md b/README.md index 2db463c..e4e155b 100644 --- a/README.md +++ b/README.md @@ -1,172 +1,161 @@ - -# HeatOps +# HeatOps v2 [![CI](https://github.com/MichelleHan7/HeatOps/actions/workflows/ci.yml/badge.svg)](https://github.com/MichelleHan7/HeatOps/actions/workflows/ci.yml) -[![Python 3.11+](https://img.shields.io/badge/python-3.11%2B-3776AB.svg)](https://www.python.org/) - -**[Launch the live demo →](https://heatops-fortyguard.streamlit.app/)** - -**Heat-aware field operations planning powered by FortyGuard hyperlocal -temperature intelligence.** - -HeatOps uses location- and time-specific temperature data to create a -field schedule. It compares an operations-first baseline with a heat-aware -schedule, outlines the heat-versus-delay trade-off explicitly, and explains the reason why the -job moved. - -The current demo models one Phoenix utility crew and five field jobs. On the -bundled, traceable FortyGuard snapshot, the **Heat-first** preset reduces the -operational Heat Load Score from **40.38 to 39.18 (2.97%)** by moving **3 of 5 -jobs**. Heat Load is a planning metric, not a medical risk assessment. - -## Demo highlights - -- A polished Streamlit dashboard with preset and custom optimization controls. -- Side-by-side baseline and optimized timelines. -- Hyperlocal temperature curves and a field-location map. -- A visible Heat Load, delay, idle-time, and threshold-exposure comparison. -- Explanations for every scheduling change. -- Optional live FortyGuard refresh with validated cache and snapshot fallback. -- A deterministic OR-Tools CP-SAT optimizer and a baseline using the same feasibility constraints. - -## How it works - -```mermaid -flowchart TD - FG["FortyGuard Heatmap API"] --> TS["Validated temperature service"] - SNAP["Traceable demo snapshot"] --> TS - TS --> MATRIX["Job × time temperature matrix"] - INPUTS["Jobs, crew, constraints"] --> OPT["CP-SAT scheduler"] - MATRIX --> OPT - OPT --> EVAL["Baseline comparison and explanations"] - EVAL --> UI["Streamlit dashboard and CLI"] -``` -The optimizer generates feasible 15-minute start-time candidates for every -job, enforces time windows, shift boundaries, worker skills, and nonoverlapping constraints, -then minimizes a configurable combination of normalized Heat Load and delay with weighted priority. See [Architecture](docs/architecture.md) for detailed design. +**Heat-aware, multi-crew field operations scheduling with reproducible evaluation.** + +HeatOps assigns jobs to qualified crews under time windows, shifts, availability +and optional travel constraints using OR-Tools CP-SAT. It compares operational +Heat Load with priority-weighted task delay. Heat Load is a modeled planning +metric, not a medical score or evidence of prevented heat illness. + +[Live demo](https://heatops-fortyguard.streamlit.app/) · +[Architecture](docs/architecture.md) · [Benchmark results](docs/benchmark-results.md) · +[Reproduction methodology](docs/benchmark-methodology.md) + +Core v2 was merged through PR #3. This follow-up branch contains the completed +benchmark artifacts and documentation. Deployment status after that merge has +not been independently checked. The default local page preserves Phoenix; enable +**Multi-crew workspace** in the sidebar to use v2. + +## Features + +- Multiple heterogeneous crews, exactly-once assignment, skill/shift/deadline and + service-availability constraints; independently validated returned schedules. +- Operations-first, heat-first, balanced and explicit extra-delay-budget policies. +- Optional depot-to-first / consecutive-task travel using estimated geography or + directed travel-time overrides. Route lines show order, not road navigation. +- Offline Phoenix snapshot and clearly labeled synthetic scenarios; optional + FortyGuard and Open-Meteo providers with provenance and visible fallback. +- Per-crew timelines, locations, temperature curves, utilization, status/gap and + comparison metrics; schedule CSV/JSON and comparison/metrics JSON downloads. +- Seeded scenario generator, fixed benchmark manifest, failure-inclusive reports, + confidence intervals, build/solve timings and scalability experiments. ## Quick start -Requirements: Python 3.11 or 3.12. +Python 3.11 or 3.12: ```bash git clone https://github.com/MichelleHan7/HeatOps.git cd HeatOps +git switch feature/heatops-v2-results python -m venv .venv source .venv/bin/activate -python -m pip install --upgrade pip python -m pip install -e ".[demo,dev]" -streamlit run app.py +python -m streamlit run app.py ``` -The dashboard opens with the bundled Phoenix snapshot, so no API key is needed -for the default demo. +No API key is needed. `requirements.txt` installs the dashboard extra for +Streamlit Community Cloud. Dev extras include plotting for benchmark reports. -### CLI evaluation +## CLI and scenarios ```bash -heatops-evaluate --mode heat_first -heatops-evaluate --mode balanced --format json -heatops-evaluate --heat-priority 75 -``` - -Valid presets are `operations_first`, `balanced`, and `heat_first`. The custom -control assigns the selected percentage to Heat Load and the remainder to delay with weighted priority. +# Historical single-crew behavior is unchanged. +heatops-evaluate --mode heat_first --format json -## Live FortyGuard data +# Fleet evaluation of bundled Phoenix jobs and both existing crews. +heatops-evaluate --all-workers --policy delay_budget --delay-budget 60 --format json -Copy the appropriate example and add your own key locally: +# Generate an explicitly synthetic 100-job, 10-crew workload. +python scripts/generate_scenarios.py --jobs 100 --crews 10 --seed 42 --output reports/scenario.json +heatops-evaluate --scenario reports/scenario.json --policy balanced --time-limit 30 --format json -```bash -cp .env.example .env -# or, for Streamlit deployment: -cp .streamlit/secrets.toml.example .streamlit/secrets.toml +# Optional travel on a small workload. +heatops-evaluate --all-workers --policy heat_first --travel ``` -Then either enable **Refresh from FortyGuard API** in the dashboard or run: +The delay budget is **additional priority-weighted minutes**, not minutes on the +wall clock. A v2 scenario bundle contains `jobs`, `workers`, `temperature_matrix`, +`metadata` and an optional independently checked `witness`. Existing separate +JSON input flags also work with `--all-workers`. Legacy `--heat-priority` remains +available on the original single-crew path. + +The dashboard accepts scenario JSON or jobs CSV + workers CSV + temperature JSON. +CSV columns match the Job/Worker dataclasses; worker skills use `;` separators and +optional `unavailable` is a JSON array of `[start,end]` pairs. Uploaded temperature +sources are labeled unverified unless declared in bundle metadata. The synthetic +generator rejects workloads too large for its crew capacity rather than silently +dropping jobs. `--settings config.json` configures shifts, duration choices, +priority/intensity ranges, locations, skills, window slack and temperature controls +(see `GeneratorSettings`). Standard sizes are 5, 10, 25, 50 and 100; crews range from 1 to 10. + +## Evidence and reproduction + +The original five-job Phoenix **Heat-first** case remains **40.3827 → 39.1827 +Heat Load (2.9715%)**, using the unchanged 2026-08-24 FortyGuard snapshot. Its raw +delay rises from 4.5 to 10.25 task-hours; this operational cost is disclosed. +The original snapshot lacks API activity IDs; provenance has not been invented. +See `reports/phoenix-baseline.json` and `tests/test_phoenix_demo_scenario.py`. + +V2 results are separate synthetic experiments. The preregistered main suite uses +30 seeds for each of five temperature families, plus scaling and travel cohorts. +See [executed results](docs/benchmark-results.md), raw `reports/benchmark_results.csv`, +full assignments in `reports/benchmark_details.jsonl.gz` and environment metadata. ```bash -python scripts/fetch_temperature_matrix.py \ - --jobs data/scenarios/phoenix-demo/jobs.json \ - --date 2026-08-24 \ - --output data/temperature_matrix.json +python scripts/run_benchmarks.py --manifest benchmarks/final.json +python scripts/profile_scheduler.py ``` -The API path is asynchronous. HeatOps creates one heatmap per requested time -slot, polls to completion with bounded retries, spatially matches each job to a -returned tile, validates the complete matrix, and caches valid results. If a -live refresh fails, the dashboard retains the validated repository snapshot. -The full contract is documented in [FortyGuard API integration](docs/api-integration.md). - -Never commit `.env` or `.streamlit/secrets.toml`; both are ignored. - -## Demo modes - -| Mode | Heat weight | Delay weight | Intended question | -| --- | ---: | ---: | --- | -| Operations-first | 0% | 100% | What is the earliest feasible schedule? | -| Balanced | 50% | 50% | Where is a practical compromise? | -| Heat-first | 100% | 0% | How far can the modeled Heat Load be reduced? | -| Custom | 0–100% | Remaining weight | What changes under a chosen policy? | - -The Heat priority slider is intentionally enabled only for **Custom** mode; -presets remain fixed and reproducible. - -## Reproducible evidence - -The repository separates facts from scenario assumptions: - -- Temperatures come from the existing FortyGuard ingestion snapshot. The file - checksum, date, AOI, resolution, and provenance live in - `data/scenarios/phoenix-demo/metadata.json`. -- Job locations, time windows, priorities, skills, and physical-intensity - multipliers are explicit hackathon demo assumptions. -- The baseline and HeatOps schedule share the same solver and constraints. The - only difference is whether temperature contributes to the objective. -- Scenario integrity and optimization outcomes are exercised by - `tests/test_phoenix_demo_scenario.py`. - -## Project structure - -```text -app.py Streamlit demo -src/heatops/domain/ Data models, loaders, and shared configuration -src/heatops/integrations/ FortyGuard client, AOI, matching, and cache service -src/heatops/optimization/ Heat Load model, presets, baseline, and CP-SAT solver -src/heatops/evaluation/ Comparable metrics and job-level explanations -src/heatops/presentation.py UI-neutral chart and card records -data/scenarios/phoenix-demo/ Reproducible jobs, crew, metadata, and temperatures -scripts/ Data-fetch and evaluation entry points -tests/ Unit, integration, scenario, and UI tests -docs/ Architecture and FortyGuard API documentation -``` +Full benchmarks take several minutes or longer; they never run on dashboard load +or normal CI. `--resume` only accepts matching source/manifest hashes. Time limits +apply to solves; end-to-end runtime also includes model construction. A time-limit +incumbent is FEASIBLE, never OPTIMAL. Zero-heat baselines produce undefined reduction, +not an invented percentage. See [verified resume claims](docs/resume-metrics.md). -## Quality gates +## Verified v2 results + +The completed suite contains 165 synthetic instances and 660 policy records: +582 OPTIMAL, 72 FEASIBLE, 5 UNKNOWN, and 1 NOT_RUN. All 654 returned schedules +passed independent validation. For 100 jobs / 10 crews, 11 of 12 policy attempts +returned feasible incumbents; none proved optimal within the 30-second solve cap. -Run the same checks used by GitHub Actions: +On the main suite, heat-first achieved **8.47% mean reduction** over **117 valid, +nonzero-baseline paired cases** (150 scenarios attempted; exclusions disclosed). +The median was 3.67%. Heat-first also adds scheduling delay; see the full +[implementation report](docs/final-implementation-report.md) for trade-offs. +These synthetic results are separate from the preserved 2.97% Phoenix snapshot. + +## Weather + +Choose offline data, Open-Meteo or FortyGuard in the fleet workspace. Sources and +actual data dates are displayed and exported; failures visibly retain scenario +data. Open-Meteo historical output is reanalysis, not station measurements. +See [provider contract and terms](docs/weather-providers.md). Keep credentials in +local environment variables or Streamlit secrets; never commit them. +The original [FortyGuard integration](docs/api-integration.md) remains available. + +## Quality gates ```bash -ruff check . -ruff format --check . -pytest -q +python -m ruff check . +python -m ruff format --check . +python -m pytest -q python -m compileall -q src app.py scripts tests heatops-evaluate --mode heat_first --format json ``` -The tests do not call the live FortyGuard API. Network behavior is exercised -through controlled fakes, while the end-to-end scenario and Streamlit smoke -test use repository fixtures. +CI runs these checks on Python 3.11/3.12. Tests include independent feasibility, +legacy regression, travel sequencing, weather failures, benchmark smoke tests, +CLI/export checks and Streamlit AppTest. Unit tests require no API credentials. -## Technical documentation +## Limits -- [Architecture](docs/architecture.md) -- [FortyGuard API integration](docs/api-integration.md) +This is a portfolio scheduling system, not a deployed workforce product. Results +on synthetic workloads do not establish real-world heat-risk reduction. Larger +instances can return feasible-but-unproven solutions or no incumbent before timeout. +Travel mode is guarded at 25 jobs, uses approximate straight-line distances, and +does not require return to depot. Availability blocks task execution, not travel. +No cross-midnight jobs, production authentication, persistence or medical advice. +Optional FastAPI/PostgreSQL work is deferred; core optimization and measurement +remain the focus. See [design decisions](docs/v2-design-decisions.md). -## Current scope +## Hackathon history -HeatOps is a decision-support prototype for a single crew and deterministic -daily planning. It does not claim medical safety guidance, predict heat illness, -or replace employer heat-safety procedures. Multi-crew routing, travel-time -optimization, authentication, and production persistence are intentionally -outside this hackathon submission. +HeatOps began as a FortyGuard hackathon project in August 2026: one Phoenix crew, +five utility tasks, Streamlit and a saved API temperature matrix. V2 extends that +layered Python codebase, retains the original demonstration, and adds workforce +assignment and reproducible engineering evidence rather than targeting an +arbitrary improvement percentage. diff --git a/docs/architecture.md b/docs/architecture.md index 0428017..c933b6f 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -1,3 +1,37 @@ +# V2 fleet architecture + +```mermaid +flowchart TD + Sources["Snapshot, synthetic, weather APIs"] --> Weather["Provenance and hourly matrix"] + Inputs["Jobs, skills, shifts, availability"] --> Engine["Fleet CP-SAT engine"] + Weather --> Engine + Travel["Optional travel sequencing"] --> Engine + Engine --> Check["Independent schedule validator"] + Check --> Compare["Paired policy metrics"] + Compare --> UI["Streamlit, CLI and exports"] + Compare --> Reports["Seeded benchmark reports"] +``` + +`optimization/multi_crew.py` owns candidate assignment, worker NoOverlap, +normalization, delay caps and optional circuit sequencing. `optimization/travel.py` +provides approximate geography and directed travel overrides. +`evaluation/fleet.py` independently validates returned schedules and computes +per-worker utilization, deadlines and route metrics. `benchmark/` separates +scenario construction, checking, experiment execution and reporting. +`integrations/weather_provider.py` defines the source contract; +`integrations/open_meteo.py` adds timestamp-preserving weather ingestion. +`ui/fleet.py` renders and exports these results without duplicating the optimizer. + +Legacy functions and Phoenix data remain intact. New results use FleetResult, +including explicit UNKNOWN/INFEASIBLE status, monotonic build/solve/runtime, +integer objective bound/gap and model counts. No unfinished solve is reported as +optimal. See [design decisions](v2-design-decisions.md), +[methodology](benchmark-methodology.md) and [weather providers](weather-providers.md). + +The architecture below describes the preserved historical single-crew path. + +--- + # HeatOps architecture HeatOps is organized as a small decision-support system: ingest hyperlocal diff --git a/docs/benchmark-results.md b/docs/benchmark-results.md new file mode 100644 index 0000000..a2f78dd --- /dev/null +++ b/docs/benchmark-results.md @@ -0,0 +1,50 @@ +# Executed benchmark results + +All temperatures in this benchmark are synthetic. See benchmark-methodology.md. + +| Configuration / policy | Solves / attempts | Mean reduction % | Median seconds | Statuses | +|---|---:|---:|---:|---| +| main/10/2/flat/balanced | 29/30 | 0.076 | 0.668 | {'OPTIMAL': 27, 'FEASIBLE': 2, 'UNKNOWN': 1} | +| main/10/2/flat/delay_budget | 27/30 | 0.167 | 1.027 | {'FEASIBLE': 4, 'OPTIMAL': 23, 'UNKNOWN': 2, 'NOT_RUN': 1} | +| main/10/2/flat/heat_first | 30/30 | 0.301 | 1.205 | {'OPTIMAL': 24, 'FEASIBLE': 6} | +| main/10/2/flat/operations_first | 29/30 | 0.000 | 0.606 | {'OPTIMAL': 29, 'UNKNOWN': 1} | +| main/10/2/hot/balanced | 30/30 | 2.102 | 0.591 | {'OPTIMAL': 26, 'FEASIBLE': 4} | +| main/10/2/hot/delay_budget | 30/30 | 2.558 | 0.872 | {'OPTIMAL': 25, 'FEASIBLE': 5} | +| main/10/2/hot/heat_first | 30/30 | 4.296 | 0.719 | {'OPTIMAL': 26, 'FEASIBLE': 4} | +| main/10/2/hot/operations_first | 30/30 | 0.000 | 0.398 | {'OPTIMAL': 30} | +| main/10/2/mild/balanced | 30/30 | N/A | 0.325 | {'OPTIMAL': 30} | +| main/10/2/mild/delay_budget | 30/30 | N/A | 0.551 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/mild/heat_first | 30/30 | N/A | 0.415 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/mild/operations_first | 30/30 | N/A | 0.310 | {'OPTIMAL': 30} | +| main/10/2/spatial/balanced | 30/30 | 4.113 | 0.462 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/spatial/delay_budget | 30/30 | 4.625 | 0.620 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/spatial/heat_first | 30/30 | 8.530 | 0.414 | {'OPTIMAL': 28, 'FEASIBLE': 2} | +| main/10/2/spatial/operations_first | 30/30 | 0.000 | 0.289 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/variable/balanced | 30/30 | 8.593 | 0.453 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/variable/delay_budget | 30/30 | 10.119 | 0.634 | {'OPTIMAL': 28, 'FEASIBLE': 2} | +| main/10/2/variable/heat_first | 30/30 | 21.334 | 0.333 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/variable/operations_first | 30/30 | 0.000 | 0.431 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| scaling/100/10/hot/balanced | 3/3 | 0.067 | 30.578 | {'FEASIBLE': 3} | +| scaling/100/10/hot/delay_budget | 2/3 | 0.168 | 30.164 | {'UNKNOWN': 1, 'FEASIBLE': 2} | +| scaling/100/10/hot/heat_first | 3/3 | 0.390 | 30.340 | {'FEASIBLE': 3} | +| scaling/100/10/hot/operations_first | 3/3 | 0.000 | 30.318 | {'FEASIBLE': 3} | +| scaling/25/5/hot/balanced | 3/3 | 0.977 | 30.092 | {'FEASIBLE': 3} | +| scaling/25/5/hot/delay_budget | 3/3 | 0.788 | 30.049 | {'FEASIBLE': 3} | +| scaling/25/5/hot/heat_first | 3/3 | 3.070 | 30.071 | {'FEASIBLE': 3} | +| scaling/25/5/hot/operations_first | 3/3 | 0.000 | 30.053 | {'FEASIBLE': 3} | +| scaling/5/1/hot/balanced | 3/3 | 10.611 | 0.029 | {'OPTIMAL': 3} | +| scaling/5/1/hot/delay_budget | 3/3 | 10.611 | 0.035 | {'OPTIMAL': 3} | +| scaling/5/1/hot/heat_first | 3/3 | 12.338 | 0.030 | {'OPTIMAL': 3} | +| scaling/5/1/hot/operations_first | 3/3 | 0.000 | 0.040 | {'OPTIMAL': 3} | +| scaling/50/5/hot/balanced | 3/3 | 0.314 | 30.158 | {'FEASIBLE': 3} | +| scaling/50/5/hot/delay_budget | 3/3 | 0.293 | 30.099 | {'FEASIBLE': 3} | +| scaling/50/5/hot/heat_first | 3/3 | 0.972 | 30.135 | {'FEASIBLE': 3} | +| scaling/50/5/hot/operations_first | 3/3 | 0.000 | 30.127 | {'FEASIBLE': 3} | +| travel/5/2/hot/balanced | 3/3 | 0.000 | 0.017 | {'OPTIMAL': 3} | +| travel/5/2/hot/delay_budget | 3/3 | 0.227 | 0.012 | {'OPTIMAL': 3} | +| travel/5/2/hot/heat_first | 3/3 | 2.327 | 0.018 | {'OPTIMAL': 3} | +| travel/5/2/hot/operations_first | 3/3 | 0.000 | 0.015 | {'OPTIMAL': 3} | + +Reduction statistics condition on valid paired solves and positive baseline heat. All attempts and undefined counts are in benchmark_summary.json. Bounds/gaps use the integer solver objective. No feasible incumbent is labeled optimal. + +Reproduce: `python scripts/run_benchmarks.py --manifest benchmarks/final.json`. Detailed assignments, metrics and statuses: `reports/benchmark_details.jsonl.gz`. Raw rows: `reports/benchmark_results.csv`. Environment, versions and source hash: `reports/benchmark_environment.json`. diff --git a/docs/final-implementation-report.md b/docs/final-implementation-report.md new file mode 100644 index 0000000..dde3b67 --- /dev/null +++ b/docs/final-implementation-report.md @@ -0,0 +1,106 @@ +# HeatOps v2 — 最终实现报告 + +## A. 已实现功能与范围 + +已完成多队伍 CP-SAT 调度、可选行程约束、独立可行性检查、可配置合成场景、四策略 benchmark、天气接口、Streamlit 多队伍界面、CLI、CSV/JSON 导出及文档。原始 Phoenix 演示与温度快照保留。 +多队伍使用 job/worker/start 决策变量和每个 job/worker 的可选区间；每个队伍独立 NoOverlap。行程采用 Circuit，只约束相邻任务与起点到首任务。延迟预算采用优先级加权分钟。所有比较使用同一组约束。 +本次未操作部署设置；本地 Streamlit AppTest 与 HTTP 健康检查通过。恢复工作时发现核心 PR #3 已在仓库侧合并，现以独立补充 PR 提交评估结果与文档。合并后的公开部署状态未独立确认。Phase 9 的 FastAPI、数据库和 Docker 持久化未做，属于原计划可选范围。 + +## B. 改动文件 + +| 路径 | 用途 | +|---|---| +| `src/heatops/optimization/multi_crew.py` | 多队伍指派、求解状态/界限/耗时、延迟预算、行程顺序 | +| `src/heatops/optimization/travel.py` | Haversine 距离、行程估计和有向时间矩阵 | +| `src/heatops/optimization/scheduler.py` | 旧接口保留，并支持新增不可用时段 | +| `src/heatops/domain/models.py`, `loaders.py` | 队伍不可用时段、兼容读取 | +| `src/heatops/evaluation/fleet.py` | 独立检查、配对比较、利用率/行程指标 | +| `src/heatops/benchmark/` | 场景、可行性见证、并行评估、统计与图表 | +| `src/heatops/integrations/open_meteo.py`, `weather_provider.py` | 来源标注、时区转换、缓存、明确回退 | +| `app.py`, `src/heatops/ui/fleet.py`, `src/heatops/cli.py` | 多队伍界面、上传、导出与命令行 | +| `scripts/generate_scenarios.py`, `run_benchmarks.py`, `profile_scheduler.py` | 可复现实验入口 | +| `tests/test_multi_crew.py`, `test_scenario_generator.py`, `test_benchmark_runner.py`, `test_open_meteo.py`, `test_fleet_ui_cli.py` | 算法、数据、集成与界面测试 | +| `benchmarks/final.json`, `reports/` | 固定清单、原始证据、图表、环境信息 | +| `README.md`, `docs/`, `.github/workflows/ci.yml`, `pyproject.toml`, `.gitignore`, `.gitattributes` | 使用说明、架构、CI、依赖和归档规则 | + +## C. 验证 + +- 原有 113 个测试通过；最终共 162 个测试通过。Python 3.12 的并行测试出现两条 fork 弃用警告，没有测试失败；未来可切换 spawn。 +- `python -m ruff check .`、`python -m ruff format --check .`、`python -m compileall -q src app.py scripts tests` 均通过。 +- `python -m pytest -q`，旧 CLI 与新 CLI smoke test 通过。GitHub Actions run 37857898141 的 Python 3.11/3.12 两项均成功。 +- 原 Phoenix：40.382715625 → 39.182749375，改善 2.971484784%；原始任务延迟 4.5 → 10.25 小时。快照 SHA-256：`e6080bde546f89b238b43a85826e51d11dc6f03bd3694835e4281dc0f134eab2`。 +- Open-Meteo 真实历史请求返回 25 个小时记录；测试中的 HTTP 故障、缺失值、时区、缓存、DST 重复小时和回退使用 mock。未使用未授权的 FortyGuard 凭据。 + +## D. 实测 benchmark + +固定清单共 **165 个场景、660 个策略记录**。有效排程 **654/660**。全部返回的可行/最优排程独立检查均为零违规。状态：`{'OPTIMAL': 582, 'FEASIBLE': 72, 'UNKNOWN': 5, 'NOT_RUN': 1}`。UNKNOWN 表示时限内无 incumbent，不代表已证明无解；NOT_RUN 表示缺少预算基准导致策略未运行。 +主实验：10 jobs / 2 crews，五种温度族各 30 个固定种子。扩展：5/1、25/5、50/5、100/10 各 3 个种子。行程：5 jobs / 2 crews、3 个种子。主实验每策略 5 秒，扩展与行程 30 秒。四场景并行，各求解器单线程。 + +| 主实验策略 | 有定义的改善样本数 / 150 | 平均改善 | 中位改善 | 状态 | +|---|---:|---:|---:|---| +| heat_first | 117/150 | 8.4687% | 3.6722% | {'OPTIMAL': 136, 'FEASIBLE': 14} | +| balanced | 116/150 | 3.6999% | 0.2850% | {'OPTIMAL': 141, 'FEASIBLE': 8, 'UNKNOWN': 1} | +| delay_budget | 115/150 | 4.3766% | 1.5961% | {'OPTIMAL': 134, 'FEASIBLE': 13, 'UNKNOWN': 2, 'NOT_RUN': 1} | + +上述平均值只包含基准与该策略均成功且基准 Heat Load > 0 的配对。零值基准、超时、未运行均在 CSV/summary 中保留，没有当成 0% 或删除失败。分组 SD、bootstrap 95% CI、最差值、p95 耗时见 `benchmark_summary.json`。 + +| Jobs / crews | 策略记录数 | 有效排程数 | 全部尝试的中位耗时 (s) | 状态 | +|---|---:|---:|---:|---| +| 5/1 | 12 | 12 | 0.035 | {'OPTIMAL': 12} | +| 25/5 | 12 | 12 | 30.065 | {'FEASIBLE': 12} | +| 50/5 | 12 | 12 | 30.131 | {'FEASIBLE': 12} | +| 100/10 | 12 | 11 | 30.329 | {'FEASIBLE': 11, 'UNKNOWN': 1} | + +100-job 已返回解的端到端中位耗时为 30.340 秒，均不能当作已证明最优；界限/相对 gap 见每行原始结果。模型构建和求解计时分开记录，30 秒限制不包含模型构建。 + +### 延迟与行程代价 +- heat_first：可比较排程额外优先级加权延迟平均 3.488 小时，最大 13.750 小时。 +- balanced：可比较排程额外优先级加权延迟平均 0.120 小时，最大 2.000 小时。 +- delay_budget：可比较排程额外优先级加权延迟平均 0.522 小时，最大 1.000 小时。 +- 行程实验有效排程 12/12；估计距离范围 13.371–23.894 km。不是测得的道路距离，也不包含返回起点。 + +开发集的五组配对构建时间试验：不缓存中位 0.679558 秒，缓存后 0.510717 秒，下降 24.85%。只可描述为模型构建改善，不能描述为端到端求解提速。 + +### 来源与恢复记录 +全部新 benchmark 温度均为 synthetic，与原 FortyGuard 快照分开。最初串行试跑的已完成记录保留在 `reports/serial-partial/`，未并入最终统计。最终四进程运行在会话暂停时保留 161 个场景，随后以相同源码 hash、Python、OR-Tools 和 NumPy 恢复最后 4 个场景；恢复环境单独存档。机器/共享负载与时间截断会影响耗时和未证明最优的 incumbent。 +证据：`reports/benchmark_environment.json`、`benchmark_resume_environment.json`、`benchmark_results.csv`、`benchmark_summary.json`、`benchmark_details.jsonl.gz`。 + +```bash +python scripts/run_benchmarks.py --manifest benchmarks/final.json +python scripts/run_benchmarks.py --manifest benchmarks/final.json --resume +python scripts/profile_scheduler.py +``` + +## E. GitHub + +核心分支：`feature/heatops-v2`，PR https://github.com/MichelleHan7/HeatOps/pull/3 已于 2026-10-09 01:57 UTC 在仓库侧合并，合并提交 `9a229675478fd4177db36c0c1d823b5beb3ced90`。恢复后已确认 main 的文件树与已测试核心实现一致。 + +补充交付分支：`feature/heatops-v2-results`。本次只创建补充 PR，不直接写入 main。 + +实现提交： +- 6bbddba docs: audit legacy scheduler and preserve Phoenix baseline +- 092bbaf feat: add multi-crew CP-SAT scheduling and travel validation +- f51184e feat: add reproducible synthetic scenarios and registered benchmark suite +- 9f29f25 feat: add provenance-aware weather providers and offline fallback +- 9ea6e9c feat: integrate fleet dashboard, custom inputs, exports and CLI +- 5f47a30 feat: expose generator settings and tighten availability and CLI validation +- 908bc63 perf: run fixed benchmark instances concurrently with archived serial evidence + +最终文档和结果另附一个提交；最新 SHA 见该 PR。 + +## F. 简历建议 + +1. Built a Python/OR-Tools multi-crew scheduler supporting 100-job, 10-crew synthetic workloads under skill, shift and deadline constraints, with independently validated outputs and a separate travel-aware mode. +2. Evaluated four scheduling policies across 165 seeded synthetic scenarios; achieved 8.47% mean modeled Heat Load reduction on 117 valid, nonzero-baseline heat-first pairs, with explicit timeout and delay reporting. +3. Integrated provenance-aware weather providers, offline fallback, Streamlit/CLI workflows and CSV/JSON exports, backed by 162 automated tests and Python 3.11/3.12 CI. + +完整证据与措辞限制见 `docs/resume-metrics.md`。不把 synthetic 改善写成生产环境收益。 + +## G. 已知限制与后续 + +- 部分时限运行无解或仅有可行 incumbent；大实例的最优性 gap 仍大。下一步应改进 lower bound、候选压缩与搜索策略，再在同一清单上比较。 +- 行程模式默认最多 25 jobs；路径是地理近似，未优化行驶距离、未要求返回 depot。 +- 仅确定性的同一天排程；无跨午夜、天气不确定性、安全休息政策、健康效果或生产认证/数据库。 +- Open-Meteo 历史数据为再分析模型；免费服务用途与配额需遵循提供方条款。 +- 本次未操作 Streamlit Community Cloud；核心合并后的线上状态未独立验证。补充结果分支等待审查。 +- 可选 backend 阶段延期，不影响当前调度、评估和展示路径。 diff --git a/docs/resume-metrics.md b/docs/resume-metrics.md new file mode 100644 index 0000000..c970def --- /dev/null +++ b/docs/resume-metrics.md @@ -0,0 +1,29 @@ +# Verified resume metrics + +These claims concern synthetic experiments or verified software functionality, not real deployments or injury reduction. + +1. Built a Python/OR-Tools multi-crew scheduler supporting 100-job, 10-crew synthetic workloads under skill, shift and deadline constraints, with independently validated outputs and a separate travel-aware mode. +2. Evaluated four scheduling policies across 165 seeded synthetic scenarios; achieved 8.47% mean modeled Heat Load reduction on 117 valid, nonzero-baseline heat-first pairs, with explicit timeout and delay reporting. +3. Integrated provenance-aware weather providers, offline fallback, Streamlit/CLI workflows and CSV/JSON exports, backed by 162 automated tests and Python 3.11/3.12 CI. + +## Evidence and caveats + +| Claim | Evidence | +|---|---| +| 100 jobs / 10 crews | `reports/benchmark_results.csv`, jobs=100; `reports/benchmark_details.jsonl.gz` | +| 8.47% mean reduction | Main heat_first rows with defined reduction, n=117 out of 150 attempted scenarios; zero-baseline and unsuccessful pairs excluded explicitly | +| 165 scenarios / four policies | `benchmarks/final.json`; 660 rows include NOT_RUN/UNKNOWN where applicable | +| 162 tests; two Python versions | `python -m pytest -q`; GitHub Actions run 37857898141 passed both Python 3.11 and 3.12 | +| Build-time microbenchmark | `reports/profiling.csv`: median 0.679558 → 0.510717 s (24.85% reduction), five paired development trials; not total solver speedup | + +All 100-job solved results were feasible incumbents, not proven-optimal schedules. Valid large-case solves: 11/12 planned policy rows. Do not claim 100% solve success. +Heat-first can add substantial weighted scheduling delay. The original real-snapshot Phoenix result remains 2.971485%; the synthetic mean is a separate experiment and does not replace it. + +## Reproduction + +```bash +python scripts/run_benchmarks.py --manifest benchmarks/final.json +python scripts/profile_scheduler.py +python -m pytest -q +python -m heatops.cli --mode heat_first --format json +``` diff --git a/docs/v2-design-decisions.md b/docs/v2-design-decisions.md new file mode 100644 index 0000000..3bd4cd5 --- /dev/null +++ b/docs/v2-design-decisions.md @@ -0,0 +1,45 @@ +# V2 design decisions + +1. **Keep the historical solver.** `optimize_schedule` and its default CLI retain + their original behavior and 2.971485% Phoenix result. `optimize_multi_crew` + returns explicit status/diagnostic objects instead of raising for infeasibility. + New fleet callers use `compare_fleet`, not single-crew idle-time metrics. +2. **All jobs required.** Binary x(job, worker, start) picks exactly one assignment. + Ineligible skills, impossible windows and unavailable service intervals are + eliminated before model construction. Global start expressions and one + optional interval per job/eligible worker feed per-worker NoOverlap. +3. **Normalize once per common feasible candidate set.** Baseline minimizes exact + integer priority-weighted delay. Heat/balanced costs use candidate-wide min-max + normalization, rounded at 1e-4, plus bounded secondary start/worker preference. + The secondary objective never overrides one integer primary unit. Equal-cost + solutions can still exist; one thread, sorted IDs and fixed seed stabilize + search. Wall-clock cutoffs do not guarantee identical FEASIBLE incumbents. +4. **Delay budget.** Heat minimization with an explicit upper bound in priority- + weighted minutes. Compare against the same instance's baseline incumbent and + disclose whether that baseline was proven optimal. Raw delay hours are separate. +5. **Travel is optional sequencing, not a route planner.** AddCircuit creates an + empty route or depot/task/task route for each crew. Only successive tasks incur + transition constraints, supporting directed nonmetric duration overrides. + The final depot arc has zero cost/time: no required return journey. Geographic + distance and time are estimates; 25-job default guard prevents routing blowup. + Unavailability blocks service, not transit. Travel is enforced, not minimized. +6. **Independent checking.** Recompute required-job membership, durations, skills, + shifts, windows, start grid, availability, chronological travel, heat and budget + outside the solver. Benchmark invalid schedules receive no improvement metric. +7. **Data boundaries.** Bundled snapshot provenance remains unchanged. Seeded + synthetic data includes an independently checked witness. Open-Meteo forecasts + and historical reanalysis are model products; original timestamps, retrieval + times and hourly values are retained. No implicit stale-date relabeling. +8. **Performance evidence.** Cache heat by job/start across workers; preserve a + cache-off experiment switch. `scripts/profile_scheduler.py` pairs five development + runs per setting and alternates order. It measures construction, not solver + speedup. Final benchmark manifest is fixed before held-out evaluation. +9. **UI and deployment.** Legacy dashboard is the default. Fleet work runs only + on a button, shows last-run provenance and exports the actual solver payload. + No full benchmarks run during app loading. Default data is offline. This work does + not operate deployment settings; deployment status after repository-side + merges must be checked separately. +10. **Scope limits.** Deterministic same-day work only; no uncertainty model, + breaks policy, overtime cost, precedence between jobs, road routing or + risk/health inference. Acclimatization remains legacy metadata and does not + rescale Heat Load. No FastAPI/database was added: optional phase 9 is deferred. diff --git a/docs/v2-implementation.md b/docs/v2-implementation.md index c9a4c3b..70d554a 100644 --- a/docs/v2-implementation.md +++ b/docs/v2-implementation.md @@ -1,7 +1,9 @@ # HeatOps v2 implementation log Starting commit: `0171e58` (Fix architecture formula rendering). -Branch: `feature/heatops-v2`. The public deployment and main are untouched. +Branch: `feature/heatops-v2`. This implementation initially left main and deployment settings untouched. +On resumption, PR #3 was observed already merged by the repository side; +completed results are delivered through `feature/heatops-v2-results`. ## Audit @@ -31,3 +33,12 @@ Timed legacy comparison: 0.200646 s (includes both solves and file I/O). Baseline and optimized: OPTIMAL, OPTIMAL. Heat Load: 40.382716 → 39.182749; reduction 2.971485%. Raw delay hours: 4.500000 → 10.250000. + +## Final status + +Core phases 0–8 implemented; production deployment remains pending review and +optional phase 9 is deferred. 162 tests and the Python 3.11/3.12 CI matrix pass. +165 scenarios / 660 policy rows completed, including failures and unrun budget +policies; 654 returned schedules independently validated. See +[final implementation report](final-implementation-report.md) for exact results, +all limitations, changed files and resume evidence. diff --git a/docs/weather-providers.md b/docs/weather-providers.md new file mode 100644 index 0000000..b1cfd3c --- /dev/null +++ b/docs/weather-providers.md @@ -0,0 +1,40 @@ +# Weather providers + +| Source | Data kind | Credentials | Fallback | +|---|---|---|---| +| Phoenix bundled snapshot | Original FortyGuard ingestion fixture, dated 2026-08-24 | None | Already offline | +| FortyGuardProvider | Provider temperature product for requested date | FORTYGUARD_API_KEY | Explicit scenario data | +| OpenMeteoProvider | Hourly 2m forecast model / historical reanalysis | None for free noncommercial endpoint | Explicit scenario data | +| StaticProvider synthetic | Seeded test temperatures | None | None | + +`WeatherProvider.fetch(jobs, requested_date, timezone)` returns `WeatherData` +(matrix and provenance). `fetch_with_fallback` preserves the fallback's actual +source/date and adds an unavailable-provider notice. It never converts a snapshot +into live weather. Open-Meteo requests UTC epoch timestamps across a padded date +range and converts them with zoneinfo. It retains raw timestamped values and +refuses missing required hours, nonfinite temperatures and repeated DST hours +that cannot fit the current HH:MM-only model. Hourly interpolation happens in the +existing Heat Load function. Cross-midnight service is not supported. + +The cache is in-memory, keyed by endpoint/coordinates/date/timezone, with one-hour +TTL and validated coverage on retrieval; it is not a permanent weather archive. +The dashboard reuses a provider resource between runs. At most 25 locations per +request, bounded HTTP timeouts, no retry storms. A failed live request never changes +benchmark fixtures. A real historical Open-Meteo request for 2026-07-02 was verified +in this implementation session (25 hourly records returned). Tests use HTTP fakes. +No new FortyGuard live request was made without an authorized configured key. + +## Official documentation and usage + +Reviewed 2026-10-08: + +- https://open-meteo.com/en/docs +- https://open-meteo.com/en/docs/historical-weather-api +- https://open-meteo.com/en/terms + +The free service is for noncommercial use, subject to published call limits and +CC BY 4.0 attribution. The UI/export includes Open-Meteo attribution. Commercial +or higher-volume usage requires checking the provider's paid-service terms. +Historical reanalysis incorporates observations and modeling; it must not be +represented as independently measured local observations. Model grid coordinates +can differ from requested job coordinates and are retained in metadata. diff --git a/reports/benchmark-results.md b/reports/benchmark-results.md new file mode 100644 index 0000000..a2f78dd --- /dev/null +++ b/reports/benchmark-results.md @@ -0,0 +1,50 @@ +# Executed benchmark results + +All temperatures in this benchmark are synthetic. See benchmark-methodology.md. + +| Configuration / policy | Solves / attempts | Mean reduction % | Median seconds | Statuses | +|---|---:|---:|---:|---| +| main/10/2/flat/balanced | 29/30 | 0.076 | 0.668 | {'OPTIMAL': 27, 'FEASIBLE': 2, 'UNKNOWN': 1} | +| main/10/2/flat/delay_budget | 27/30 | 0.167 | 1.027 | {'FEASIBLE': 4, 'OPTIMAL': 23, 'UNKNOWN': 2, 'NOT_RUN': 1} | +| main/10/2/flat/heat_first | 30/30 | 0.301 | 1.205 | {'OPTIMAL': 24, 'FEASIBLE': 6} | +| main/10/2/flat/operations_first | 29/30 | 0.000 | 0.606 | {'OPTIMAL': 29, 'UNKNOWN': 1} | +| main/10/2/hot/balanced | 30/30 | 2.102 | 0.591 | {'OPTIMAL': 26, 'FEASIBLE': 4} | +| main/10/2/hot/delay_budget | 30/30 | 2.558 | 0.872 | {'OPTIMAL': 25, 'FEASIBLE': 5} | +| main/10/2/hot/heat_first | 30/30 | 4.296 | 0.719 | {'OPTIMAL': 26, 'FEASIBLE': 4} | +| main/10/2/hot/operations_first | 30/30 | 0.000 | 0.398 | {'OPTIMAL': 30} | +| main/10/2/mild/balanced | 30/30 | N/A | 0.325 | {'OPTIMAL': 30} | +| main/10/2/mild/delay_budget | 30/30 | N/A | 0.551 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/mild/heat_first | 30/30 | N/A | 0.415 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/mild/operations_first | 30/30 | N/A | 0.310 | {'OPTIMAL': 30} | +| main/10/2/spatial/balanced | 30/30 | 4.113 | 0.462 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/spatial/delay_budget | 30/30 | 4.625 | 0.620 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/spatial/heat_first | 30/30 | 8.530 | 0.414 | {'OPTIMAL': 28, 'FEASIBLE': 2} | +| main/10/2/spatial/operations_first | 30/30 | 0.000 | 0.289 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/variable/balanced | 30/30 | 8.593 | 0.453 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/variable/delay_budget | 30/30 | 10.119 | 0.634 | {'OPTIMAL': 28, 'FEASIBLE': 2} | +| main/10/2/variable/heat_first | 30/30 | 21.334 | 0.333 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| main/10/2/variable/operations_first | 30/30 | 0.000 | 0.431 | {'OPTIMAL': 29, 'FEASIBLE': 1} | +| scaling/100/10/hot/balanced | 3/3 | 0.067 | 30.578 | {'FEASIBLE': 3} | +| scaling/100/10/hot/delay_budget | 2/3 | 0.168 | 30.164 | {'UNKNOWN': 1, 'FEASIBLE': 2} | +| scaling/100/10/hot/heat_first | 3/3 | 0.390 | 30.340 | {'FEASIBLE': 3} | +| scaling/100/10/hot/operations_first | 3/3 | 0.000 | 30.318 | {'FEASIBLE': 3} | +| scaling/25/5/hot/balanced | 3/3 | 0.977 | 30.092 | {'FEASIBLE': 3} | +| scaling/25/5/hot/delay_budget | 3/3 | 0.788 | 30.049 | {'FEASIBLE': 3} | +| scaling/25/5/hot/heat_first | 3/3 | 3.070 | 30.071 | {'FEASIBLE': 3} | +| scaling/25/5/hot/operations_first | 3/3 | 0.000 | 30.053 | {'FEASIBLE': 3} | +| scaling/5/1/hot/balanced | 3/3 | 10.611 | 0.029 | {'OPTIMAL': 3} | +| scaling/5/1/hot/delay_budget | 3/3 | 10.611 | 0.035 | {'OPTIMAL': 3} | +| scaling/5/1/hot/heat_first | 3/3 | 12.338 | 0.030 | {'OPTIMAL': 3} | +| scaling/5/1/hot/operations_first | 3/3 | 0.000 | 0.040 | {'OPTIMAL': 3} | +| scaling/50/5/hot/balanced | 3/3 | 0.314 | 30.158 | {'FEASIBLE': 3} | +| scaling/50/5/hot/delay_budget | 3/3 | 0.293 | 30.099 | {'FEASIBLE': 3} | +| scaling/50/5/hot/heat_first | 3/3 | 0.972 | 30.135 | {'FEASIBLE': 3} | +| scaling/50/5/hot/operations_first | 3/3 | 0.000 | 30.127 | {'FEASIBLE': 3} | +| travel/5/2/hot/balanced | 3/3 | 0.000 | 0.017 | {'OPTIMAL': 3} | +| travel/5/2/hot/delay_budget | 3/3 | 0.227 | 0.012 | {'OPTIMAL': 3} | +| travel/5/2/hot/heat_first | 3/3 | 2.327 | 0.018 | {'OPTIMAL': 3} | +| travel/5/2/hot/operations_first | 3/3 | 0.000 | 0.015 | {'OPTIMAL': 3} | + +Reduction statistics condition on valid paired solves and positive baseline heat. All attempts and undefined counts are in benchmark_summary.json. Bounds/gaps use the integer solver objective. No feasible incumbent is labeled optimal. + +Reproduce: `python scripts/run_benchmarks.py --manifest benchmarks/final.json`. Detailed assignments, metrics and statuses: `reports/benchmark_details.jsonl.gz`. Raw rows: `reports/benchmark_results.csv`. 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+main-10-2-flat-10002,main,10,2,flat,10002,False,synthetic,delay_budget,OPTIMAL,,OPTIMAL,38.53285009374999,38.481061106249996,0.13440217210508587,690,630,7.0,3.402929123000149,0.009408419000010326,3.3935207040001387,1194611324.0,1194611324.0,0.0,164,206,55,10,1.0,0,,,0.28125 +main-10-2-flat-10003,main,10,2,flat,10003,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,37.688000775,37.688000775,0.0,345,345,5.5,1.5377519109999866,0.02263097199988806,1.5151209390000986,9952759.0,9952759.0,0.0,236,280,56,10,1.0,0,,,0.21875 +main-10-2-flat-10003,main,10,2,flat,10003,False,synthetic,heat_first,FEASIBLE,,OPTIMAL,37.688000775,37.435471087500005,0.6700532856800065,1170,345,8.75,5.01592054400021,0.015771075000202472,5.000149469000007,902885371.0,881902722.0,0.023239549198543832,236,280,56,10,1.0,0,,,0.21875 +main-10-2-flat-10003,main,10,2,flat,10003,False,synthetic,balanced,FEASIBLE,,OPTIMAL,37.688000775,37.688000775,0.0,345,345,5.5,4.9076075689999925,0.02171894200000679,4.885888626999986,547579693.0,452786824.0,0.17311246237175562,236,280,56,10,1.0,0,,,0.21875 +main-10-2-flat-10003,main,10,2,flat,10003,False,synthetic,delay_budget,UNKNOWN,No incumbent; inspect status/time limit,OPTIMAL,37.688000775,,,,345,,4.839634952000097,0.061143484000012904,4.778491468000084,,,,236,280,57,0,,0,,,0.0 +main-10-2-flat-10004,main,10,2,flat,10004,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,46.31329021874999,46.31329021874999,0.0,525,525,5.75,0.16091764400016473,0.011043443000062325,0.1498742010001024,15140960.0,15140960.0,0.0,121,159,50,10,1.0,0,,,0.30208333333333337 +main-10-2-flat-10004,main,10,2,flat,10004,False,synthetic,heat_first,OPTIMAL,,OPTIMAL,46.31329021874999,46.232630512499995,0.17416103643040437,825,525,7.0,0.1964592779997929,0.008884781999995539,0.18757449599979736,1492390286.0,1492390286.0,0.0,121,159,50,10,1.0,0,,,0.30208333333333337 +main-10-2-flat-10004,main,10,2,flat,10004,False,synthetic,balanced,OPTIMAL,,OPTIMAL,46.31329021874999,46.31329021874999,0.0,525,525,5.75,0.12500177199990503,0.008759805999943637,0.1162419659999614,900781469.0,900781469.0,0.0,121,159,50,10,1.0,0,,,0.30208333333333337 +main-10-2-flat-10004,main,10,2,flat,10004,False,synthetic,delay_budget,OPTIMAL,,OPTIMAL,46.31329021874999,46.25617342499999,0.1233270050135071,555,525,5.0,0.1493030940000608,0.012168248000079984,0.13713484599998083,1493398781.0,1493398781.0,0.0,121,159,51,10,1.0,0,,,0.30208333333333337 +main-10-2-flat-10005,main,10,2,flat,10005,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,36.96954417499999,36.96954417499999,0.0,540,540,5.75,0.3561234199999035,0.012745333999873765,0.3433780860000297,15573244.0,15573244.0,0.0,180,220,52,10,1.0,0,,,0.2916666666666667 +main-10-2-flat-10005,main,10,2,flat,10005,False,synthetic,heat_first,OPTIMAL,,OPTIMAL,36.96954417499999,36.96954417499999,0.0,540,540,5.75,0.2204864150000958,0.015131575999930647,0.20535483900016516,1175762599.0,1175762599.0,0.0,180,220,52,10,1.0,0,,,0.2916666666666667 +main-10-2-flat-10005,main,10,2,flat,10005,False,synthetic,balanced,OPTIMAL,,OPTIMAL,36.96954417499999,36.96954417499999,0.0,540,540,5.75,0.214556980999987,0.021282664999944245,0.19327431600004275,674363662.0,674363662.0,0.0,180,220,52,10,1.0,0,,,0.2916666666666667 +main-10-2-flat-10005,main,10,2,flat,10005,False,synthetic,delay_budget,OPTIMAL,,OPTIMAL,36.96954417499999,36.96954417499999,0.0,540,540,5.75,0.29860141000017393,0.013654554000140706,0.2849468560000332,1175762599.0,1175762599.0,0.0,180,220,53,10,1.0,0,,,0.2916666666666667 +main-10-2-flat-10006,main,10,2,flat,10006,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,40.80078276875001,40.80078276875001,0.0,405,405,4.5,0.20901955200019984,0.013794989000189162,0.19522456300001068,11682140.0,11682140.0,0.0,154,190,48,10,1.0,0,,,0.26041666666666663 +main-10-2-flat-10006,main,10,2,flat,10006,False,synthetic,heat_first,OPTIMAL,,OPTIMAL,40.80078276875001,40.740386887499994,0.14802628075134605,660,405,6.5,0.3295745140001145,0.022243509000190897,0.3073310049999236,1047797331.0,1047797331.0,0.0,154,190,48,10,1.0,0,,,0.2604166666666667 +main-10-2-flat-10006,main,10,2,flat,10006,False,synthetic,balanced,OPTIMAL,,OPTIMAL,40.80078276875001,40.80078276875001,0.0,405,405,4.5,0.7631170370000291,0.016525226000112525,0.7465918109999166,586776374.0,586776374.0,0.0,154,190,48,10,1.0,0,,,0.26041666666666663 +main-10-2-flat-10006,main,10,2,flat,10006,False,synthetic,delay_budget,OPTIMAL,,OPTIMAL,40.80078276875001,40.80078276875001,0.0,405,405,4.5,0.2021478770000158,0.008277245000044786,0.193870631999971,1050045128.0,1050045128.0,0.0,154,190,49,10,1.0,0,,,0.26041666666666663 +main-10-2-flat-10007,main,10,2,flat,10007,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,32.955637987500005,32.955637987500005,0.0,735,735,5.75,2.390702182000041,0.022298695000017688,2.3684034870000232,21192920.0,21192920.0,0.0,276,322,58,10,1.0,0,,,0.23958333333333331 +main-10-2-flat-10007,main,10,2,flat,10007,False,synthetic,heat_first,FEASIBLE,,OPTIMAL,32.955637987500005,32.764726843750005,0.5792973688520681,1020,735,7.5,5.062725850000106,0.06092036200016082,5.001805487999945,999349051.0,966780477.0,0.03258978829009765,276,322,58,10,1.0,0,,,0.23958333333333331 +main-10-2-flat-10007,main,10,2,flat,10007,False,synthetic,balanced,UNKNOWN,No incumbent; inspect status/time limit,OPTIMAL,32.955637987500005,,,,735,,4.170112178000181,0.06627856600016457,4.103833612000017,,,,276,322,58,0,,0,,,0.0 +main-10-2-flat-10007,main,10,2,flat,10007,False,synthetic,delay_budget,UNKNOWN,No incumbent; inspect status/time limit,OPTIMAL,32.955637987500005,,,,735,,18.990852943999926,0.037239234000026045,18.9536137099999,,,,276,322,59,0,,0,,,0.0 +main-10-2-flat-10008,main,10,2,flat,10008,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,32.467940424999995,32.467940424999995,0.0,555,555,6.0,2.3315550869999697,0.006775736000008692,2.324779350999961,16004960.0,16004960.0,0.0,181,225,56,10,1.0,0,,,0.21875 +main-10-2-flat-10008,main,10,2,flat,10008,False,synthetic,heat_first,FEASIBLE,,OPTIMAL,32.467940424999995,32.38169718124998,0.2656258531372854,1230,555,10.5,20.796485922999864,0.008504672999833929,20.78798125000003,1093968543.0,1044481622.0,0.04523614624630025,181,225,56,10,1.0,0,,,0.21875 +main-10-2-flat-10008,main,10,2,flat,10008,False,synthetic,balanced,FEASIBLE,,OPTIMAL,32.467940424999995,32.42212271249999,0.14111678135495237,555,555,7.5,17.06138959100008,11.99915387100009,5.06223571999999,628854884.0,539653106.0,0.14184795295316494,181,225,56,10,1.0,0,,,0.21875 +main-10-2-flat-10008,main,10,2,flat,10008,False,synthetic,delay_budget,FEASIBLE,,OPTIMAL,32.467940424999995,32.4179370125,0.15400857536838072,600,555,8.5,5.024412116999883,0.016710930999806806,5.007701186000077,1095928130.0,1046066812.0,0.04549688673471681,181,225,57,10,1.0,0,,,0.21875 +main-10-2-flat-10009,main,10,2,flat,10009,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,53.163510156250005,53.163510156250005,0.0,750,750,9.75,0.14163239999993493,0.012351370000033057,0.12928102999990188,21626435.0,21626435.0,0.0,158,194,48,10,1.0,0,,,0.3541666666666667 +main-10-2-flat-10009,main,10,2,flat,10009,False,synthetic,heat_first,OPTIMAL,,OPTIMAL,53.163510156250005,52.989323275000004,0.32764368029510904,870,750,9.0,0.21204149100003633,0.008223618000101851,0.20381787299993448,1223548508.0,1223548508.0,0.0,158,194,48,10,1.0,0,,,0.3541666666666667 +main-10-2-flat-10009,main,10,2,flat,10009,False,synthetic,balanced,OPTIMAL,,OPTIMAL,53.163510156250005,53.15935939375,0.007807540336980003,750,750,10.25,0.13608593499998278,0.009443637000003946,0.12664229799997884,735465023.0,735465023.0,0.0,158,194,48,10,1.0,0,,,0.3541666666666667 +main-10-2-flat-10009,main,10,2,flat,10009,False,synthetic,delay_budget,OPTIMAL,,OPTIMAL,53.163510156250005,53.122233093750005,0.07764171774716445,795,750,10.0,1.5345984499999759,0.006674668999949063,1.5279237810000268,1229197544.0,1229197544.0,0.0,158,194,49,10,1.0,0,,,0.3541666666666667 +main-10-2-flat-10010,main,10,2,flat,10010,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,42.26718782499999,42.26718782499999,0.0,915,915,8.25,0.2222345550001137,0.005932583000003433,0.21630197200011025,26381270.0,26381270.0,0.0,154,188,46,10,1.0,0,,,0.2708333333333333 +main-10-2-flat-10010,main,10,2,flat,10010,False,synthetic,heat_first,OPTIMAL,,OPTIMAL,42.26718782499999,42.110749837499995,0.3701168578986213,990,915,9.0,0.7672892470000079,0.010290681000014956,0.7569985659999929,1067165313.0,1067165313.0,0.0,154,188,46,10,1.0,0,,,0.27083333333333337 +main-10-2-flat-10010,main,10,2,flat,10010,False,synthetic,balanced,OPTIMAL,,OPTIMAL,42.26718782499999,42.15373607499999,0.2684156572463027,915,915,8.25,0.6707877600001666,0.012225207000028604,0.658562553000138,717652955.0,717652955.0,0.0,154,188,46,10,1.0,0,,,0.2708333333333333 +main-10-2-flat-10010,main,10,2,flat,10010,False,synthetic,delay_budget,FEASIBLE,,OPTIMAL,42.26718782499999,42.17920337499999,0.20816253582870212,975,915,8.75,4.445010196000112,0.012399059000017587,4.4326111370000945,1070105024.0,1051382016.0,0.017496420986805872,154,188,47,10,1.0,0,,,0.2708333333333333 +main-10-2-flat-10011,main,10,2,flat,10011,False,synthetic,operations_first,UNKNOWN,No incumbent; inspect status/time limit,UNKNOWN,,,,,,,17.847426495999798,12.818953514999976,5.0284729809998225,,,,172,212,52,0,,0,,,0.0 +main-10-2-flat-10011,main,10,2,flat,10011,False,synthetic,heat_first,FEASIBLE,,UNKNOWN,,40.04654221875,,825,,5.75,12.727581609000026,7.715693968000096,5.01188764099993,1138958363.0,1117745444.0,0.018624841512314354,172,212,52,10,1.0,0,,,0.30208333333333337 +main-10-2-flat-10011,main,10,2,flat,10011,False,synthetic,balanced,OPTIMAL,,UNKNOWN,,40.095162456249994,,435,,5.5,1.3909670309999456,0.04647514599992064,1.344491885000025,663296549.0,663296549.0,0.0,172,212,52,10,1.0,0,,,0.30208333333333337 +main-10-2-flat-10011,main,10,2,flat,10011,False,synthetic,delay_budget,NOT_RUN,,UNKNOWN,,,,,,,,,,,,,,,,0,,,,, +main-10-2-flat-10012,main,10,2,flat,10012,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,27.08505246874999,27.08505246874999,0.0,330,330,4.25,26.362403559000086,22.880914561000054,3.4814889980000316,9520354.0,9520354.0,0.0,187,229,54,10,1.0,0,,,0.22916666666666669 +main-10-2-flat-10012,main,10,2,flat,10012,False,synthetic,heat_first,OPTIMAL,,OPTIMAL,27.08505246874999,26.929135849999998,0.5756555905877739,660,330,6.25,3.380775018000122,0.09117482200008453,3.2896001960000376,1022578746.0,1022578746.0,0.0,187,229,54,10,1.0,0,,,0.22916666666666669 +main-10-2-flat-10012,main,10,2,flat,10012,False,synthetic,balanced,OPTIMAL,,OPTIMAL,27.08505246874999,27.075272724999998,0.03610753112357808,330,330,4.5,0.666180569000062,0.04013055800010079,0.6260500109999612,608363124.0,608363124.0,0.0,187,229,54,10,1.0,0,,,0.22916666666666669 +main-10-2-flat-10012,main,10,2,flat,10012,False,synthetic,delay_budget,OPTIMAL,,OPTIMAL,27.08505246874999,26.990396924999995,0.34947520909994506,390,330,4.5,0.8865837989999363,0.013515040000129375,0.873068758999807,1025806488.0,1025806488.0,0.0,187,229,55,10,1.0,0,,,0.22916666666666669 +main-10-2-flat-10013,main,10,2,flat,10013,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,41.4264358,41.4264358,0.0,390,390,5.0,3.1239842210000006,2.0783249510000132,1.0456592699999874,11250096.0,11250096.0,0.0,180,216,48,10,1.0,0,,,0.25 +main-10-2-flat-10013,main,10,2,flat,10013,False,synthetic,heat_first,OPTIMAL,,OPTIMAL,41.4264358,41.3985226875,0.06737995186156483,525,390,5.5,0.25842730499994104,0.015815034000070227,0.24261227099987082,973958019.0,973958019.0,0.0,180,216,48,10,1.0,0,,,0.25 +main-10-2-flat-10013,main,10,2,flat,10013,False,synthetic,balanced,OPTIMAL,,OPTIMAL,41.4264358,41.4264358,0.0,390,390,5.0,0.7281064730000253,0.019732148999992205,0.7083743240000331,560953029.0,560953029.0,0.0,180,216,48,10,1.0,0,,,0.25 +main-10-2-flat-10013,main,10,2,flat,10013,False,synthetic,delay_budget,OPTIMAL,,OPTIMAL,41.4264358,41.4264358,0.0,390,390,5.0,0.26526425700012624,0.008640558000024612,0.2566236990001016,975024336.0,975024336.0,0.0,180,216,49,10,1.0,0,,,0.25 +main-10-2-flat-10014,main,10,2,flat,10014,False,synthetic,operations_first,OPTIMAL,,OPTIMAL,34.0465963,34.0465963,0.0,480,480,4.75,0.24476629899982072,0.00898703600000772,0.235779262999813,13843654.0,13843654.0,0.0,147,185,50,10,1.0,0,,,0.22916666666666666 +main-10-2-flat-10014,main,10,2,flat,10014,False,synthetic,heat_first,OPTIMAL,,OPTIMAL,34.0465963,33.99085465,0.16372165225806887,510,480,5.5,0.43657144100006917,0.006223410000075091,0.4303480309999941,1056126388.0,1056126388.0,0.0,147,185,50,10,1.0,0,,,0.22916666666666666 +main-10-2-flat-10014,main,10,2,flat,10014,False,synthetic,balanced,OPTIMAL,,OPTIMAL,34.0465963,34.03726975,0.027393487201530536,480,480,5.25,0.310134903999824,0.008873105999782638,0.3012617980000414,647300473.0,647300473.0,0.0,147,185,50,10,1.0,0,,,0.22916666666666666 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execution. Editable distribution metadata now reflects pyproject version 0.2.0." +} diff --git a/reports/benchmark_summary.json b/reports/benchmark_summary.json new file mode 100644 index 0000000..5c270c7 --- /dev/null +++ b/reports/benchmark_summary.json @@ -0,0 +1,891 @@ +{ + "main/10/2/flat/balanced": { + "attempts": 30, + "valid_schedules": 29, + "successful_solve_rate": 0.9666666666666667, + "statuses": { + "OPTIMAL": 27, + "FEASIBLE": 2, + "UNKNOWN": 1 + }, + "reduction_n": 28, + "undefined_or_failed_pairs": 2, + "mean_reduction_percent": 0.07556000904408651, + "median_reduction_percent": 0.0039037701684900014, + "sd_reduction_percent": 0.1216722701905125, + "mean_reduction_ci95": [ + 0.03417809228653154, + 0.12466985885589277 + ], + "worst_reduction_percent": 0.0, + "best_reduction_percent": 0.39812764102639925, + "median_runtime_seconds": 0.6684841645001143, + "p95_runtime_seconds": 4.575734643050075, + "baseline_optimal_n": 29 + }, + "main/10/2/flat/delay_budget": { + "attempts": 30, + 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z2fYhATQEQJ+HI2umF`8QbLknV?+Fw=1@xyev~obJ6c(=?2x^(=4G%%+Ak1RI&!l&+ zpq2{;k+lMe?-{K3aqv|S-a#YQAv?gw!RF54-jc`G2zZ&OddM@XBg8e<$-IaF6-$eH zzo>_et~hKf)C5C;2BOV58Xz0@9%RG23Bb|r+SUie6;=qLB;R*+1o-1Peje1r74Loc zaFL%c-@av97`x1ba!KevZHKN3w1W;>LOpJf&p{jDnPIOjJ+#nEA-OCA{Ymsj?Mbpg zXo;l3ZMCHMnTeJTK79IAYj}}ON@_^=ej*myDyV>d)@-j#;Pd3j0r!5B`%e?H+UYMt zwg4im$Z|D4#pS+HsB#=Vhx5kMsjks5V229>l1E`)u#Z5x~rG>M`WYqTK% z+H`xbb=Cu7=R$kY`dk+&un&14x+hp9gTW>Q)mCw^Ie>Nx`b#ONqy1QD8M3LhwGL?O z9Zk)rKzdDnez-|LR02){ls3^;S>XAwv?(l#r~3c(hK>m|hr9M{j_sC7VU_? zfe%<)0|`4Egw1o-t1Qrj=@0>BO0=UECG+U|LC-*7>y+HxyC>1ENi@4d1e@gs-JnlO zOCwf_<`1kflz|&t9`&c_R$696;PXGh&Cw9>5~c;ciMLi296WRP>;K%8ZT0_WFa7^h e?-qD=M3kw7SNt>Fn-9i Date: Fri, 9 Oct 2026 01:01:36 -0400 Subject: [PATCH 2/2] docs: link completed evidence pull request in handoff report --- docs/final-implementation-report.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/final-implementation-report.md b/docs/final-implementation-report.md index dde3b67..762398b 100644 --- a/docs/final-implementation-report.md +++ b/docs/final-implementation-report.md @@ -75,7 +75,7 @@ python scripts/profile_scheduler.py 核心分支：`feature/heatops-v2`，PR https://github.com/MichelleHan7/HeatOps/pull/3 已于 2026-10-09 01:57 UTC 在仓库侧合并，合并提交 `9a229675478fd4177db36c0c1d823b5beb3ced90`。恢复后已确认 main 的文件树与已测试核心实现一致。 -补充交付分支：`feature/heatops-v2-results`。本次只创建补充 PR，不直接写入 main。 +补充交付分支：`feature/heatops-v2-results`。草稿 PR：https://github.com/MichelleHan7/HeatOps/pull/4 。结果与文档提交：`b4f7e48972db75b74d729f9b279f9548d242d9ee`。本次只创建补充 PR，不直接写入 main。 实现提交： - 6bbddba docs: audit legacy scheduler and preserve Phoenix baseline

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