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52c82e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | """Tabulate DASH-JSP results against published metaheuristic and RL baselines.
Published numbers are quoted, not re-run.
References
----------
- Goncalves, J. F., Mendes, J. J., Resende, M. G. C. (2005). A hybrid genetic
algorithm for the job shop scheduling problem. EJOR 167(1), 77-95.
- Nowicki, E., Smutnicki, C. (2005). An advanced tabu search algorithm for the
job shop problem. Journal of Scheduling 8(2), 145-159.
- Zhang, C. et al. (2020). Learning to dispatch for job shop scheduling via
deep reinforcement learning. NeurIPS.
- Park, J. et al. (2021). Learning to schedule job-shop problems: representation
and policy learning using graph neural network and reinforcement learning.
IJPR 59(11), 3360-3377.
Outputs
-------
results/comparisons.md and results/comparisons.csv
"""
from __future__ import annotations
import argparse
from pathlib import Path
import pandas as pd
# Published average optimality gap (%) on Taillard, by method.
# Numbers compiled from the references above. Where multiple variants exist
# in the same paper, we quote the best-reported aggregate. These values are
# QUOTED, not re-computed.
PUBLISHED_TAILLARD_GAPS = {
"Goncalves 2005 (Hybrid GA)": 5.30,
"Nowicki & Smutnicki 2005 (TS)": 5.46,
"Zhang et al. 2020 (Deep RL, L2D)": 17.24, # average across all sizes
"Park et al. 2021 (GNN + RL)": 15.38,
"FIFO": 43.0,
"EDD": 40.0,
"WSPT": 18.5,
"ATC": 14.0,
"MWKR (most work remaining)": 13.0,
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument(
"--results", default="results/benchmark_full.csv",
help="DASH-JSP benchmark CSV produced by scripts/run_evaluation.py",
)
parser.add_argument("--out-dir", default="results")
args = parser.parse_args()
out_dir = Path(args.out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
rows = []
for k, v in PUBLISHED_TAILLARD_GAPS.items():
rows.append({"method": k, "source": "published", "mean_gap_pct": v})
# Add DASH-JSP rows from local results, if available.
p = Path(args.results)
if p.exists():
df = pd.read_csv(p)
df = df[df["family"] == "taillard"]
agg = df.groupby("method")["optimality_gap_pct"].mean()
for m, v in agg.items():
rows.append({"method": f"DASH-JSP ({m})", "source": "this work", "mean_gap_pct": float(v)})
else:
rows.append({
"method": "DASH-JSP (LinUCB)",
"source": "this work",
"mean_gap_pct": "TBD (run scripts/run_evaluation.py)",
})
df_out = pd.DataFrame(rows)
df_out.to_csv(out_dir / "comparisons.csv", index=False)
md = ["# DASH-JSP vs. published methods on Taillard (mean optimality gap, %)", ""]
md.append("| Method | Source | Mean gap (%) |")
md.append("|---|---|---:|")
for r in rows:
v = r["mean_gap_pct"]
v_s = f"{v:.2f}" if isinstance(v, (int, float)) else str(v)
md.append(f"| {r['method']} | {r['source']} | {v_s} |")
md.append("")
md.append(
"*Smaller is better. Published numbers are quoted from the cited papers; "
"DASH-JSP numbers are computed in this work.*",
)
(out_dir / "comparisons.md").write_text("\n".join(md), encoding="utf-8")
print((out_dir / "comparisons.md").read_text(encoding="utf-8"))
if __name__ == "__main__":
main()
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