44 lines
2.8 KiB
Markdown
44 lines
2.8 KiB
Markdown
# PROGRESS
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## 2026-06-02 T-ALNS-RRD reproduction fidelity fix
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### Findings addressed
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- The route travel component previously counted route elapsed time, which included waiting and service duration, instead of only `sum t_ij(T_i)` from the paper objective.
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- RRD event generation and rollout used unseeded RNGs, so the same algorithm seed could produce different event streams and dispatch outcomes.
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- Traffic incidents mutated the shared traffic tensor during detection, which could leak into later evaluations when an event was not dispatched.
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- Rollout Tabu adjustment used the wrong sign for a cost-minimizing score: Tabu actions were rewarded and non-Tabu actions were penalized.
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- Urgent-order actions could insert a negative placeholder node into fixed-size traffic tensors, causing invalid route evaluation.
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- T-ALNS traffic aspiration did not compare against the current solution, and frequency memory did not use congestion-weighted updates from Eq.34.
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### Changes made
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- Corrected Eq.1 cost accounting so travel cost is only arc traversal time; waiting and service remain part of time propagation.
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- Documented hold-last-value traffic bucket behavior for customer windows after the paper's 6:00-18:00 traffic horizon.
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- Restored congestion penalty generation to Eq.9 semantics: `rho = theta * gamma`.
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- Threaded deterministic seeds through `EventGenerator`, `RolloutEngine`, and `Dispatch`.
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- Made traffic incident severity temporary during dispatch evaluation and restored the base tensor with `try/finally`.
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- Corrected rollout Tabu penalty/bonus direction for a cost-minimizing dispatch value.
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- Reworked unsupported urgent-order insertion into fixed-graph penalty actions instead of invalid synthetic nodes.
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- Added congestion-weighted frequency memory updates and current-solution traffic aspiration checks.
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- Added `configs/paper.yaml` as the canonical paper-aligned server experiment config.
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### Local validation
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- Per user instruction, no local Python tests, smoke tests, or full experiments were run in this round.
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- Static review only: inspect source changes, config shape, and git diff before commit.
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### Server run command
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```bash
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cd t_alns_rrd_reproduction
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pip install -r requirements.txt
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python src/experiments/run_main_comparison.py --config paper --seeds 30 --iterations 1000 --time-limit 600 --output results/paper_fixed
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```
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### Expected server outputs
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- `results/paper_fixed/tables/main_comparison.csv`
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- `results/paper_fixed/tables/per_seed_costs.csv`
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- `results/paper_fixed/tables/statistical_tests.csv`
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- `results/paper_fixed/logs/convergence.npz`
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### Interpretation rule
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- Check whether Static > TA-Greedy > ALNS-Base > T-ALNS > T-ALNS-RRD in total cost, CES decreases, and OTDR improves.
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- If the paper trend is not reproduced, keep the generated tables and record the failed metrics honestly instead of tuning results by hand.
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