diff --git a/README.md b/README.md
index 61ad886..9d466f0 100644
--- a/README.md
+++ b/README.md
@@ -21,3 +21,9 @@ Optimize a 365-day schedule with at most 2 visits per day and minimum gap constr
 
 - `python3 scheduling_optimization.py --days 365 --daily-capacity 2 --gap-min 14`
 - Outputs are written to `data/` (e.g., `data/schedule_optimized_kmin6.8_gap14.csv`), using `data/kmin_effectiveness_data.csv` as the frequency source.
+
+### Visualization (Plan A)
+
+- `python3 visualize_schedule.py`
+- Outputs: `data/schedule_barcode_*.png` and `data/schedule_gap_deviation_*.png`
+- Site label rule: remove first 4 chars, then take 12 chars.
diff --git a/data/schedule_barcode_kmin6.8_gap14.png b/data/schedule_barcode_kmin6.8_gap14.png
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diff --git a/data/schedule_gap_deviation_kmin6.8_gap14.png b/data/schedule_gap_deviation_kmin6.8_gap14.png
new file mode 100644
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diff --git a/visualize_schedule.py b/visualize_schedule.py
new file mode 100644
index 0000000..2080916
--- /dev/null
+++ b/visualize_schedule.py
@@ -0,0 +1,228 @@
+"""
+Schedule Visualization (Plan A)
+
+Produces:
+1) Barcode/Raster plot: site vs day visits
+2) Gap deviation plot: (gap - ideal_gap) grouped by frequency
+
+Inputs:
+- data/schedule_long_*.csv from scheduling_optimization.py
+- data/kmin_effectiveness_sites.csv (site metadata)
+
+Site short name rule (per user request):
+- remove first 4 characters, then take next 12 characters.
+"""
+
+from __future__ import annotations
+
+import argparse
+import glob
+import os
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+
+try:
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    _HAS_MPL = True
+except ModuleNotFoundError:
+    plt = None
+    _HAS_MPL = False
+
+
+OUTPUT_DIR = "data"
+DEFAULT_SITES_CSV = os.path.join(OUTPUT_DIR, "kmin_effectiveness_sites.csv")
+
+
+def short_site_name(name: str) -> str:
+    s = (name or "").strip()
+    if len(s) <= 4:
+        return s[:12]
+    return s[4:][:12]
+
+
+def find_latest_file(pattern: str) -> str:
+    matches = glob.glob(pattern)
+    if not matches:
+        raise FileNotFoundError(f"No files match: {pattern}")
+    matches.sort(key=lambda p: os.path.getmtime(p), reverse=True)
+    return matches[0]
+
+
+def stem_from_filename(path: str) -> str:
+    base = os.path.basename(path)
+    for prefix in ("schedule_long_", "schedule_optimized_", "site_visits_"):
+        if base.startswith(prefix) and base.endswith(".csv"):
+            return base[len(prefix) : -len(".csv")]
+    if base.endswith(".csv"):
+        return base[:-len(".csv")]
+    return base
+
+
+def load_schedule_long(path: str) -> pd.DataFrame:
+    df = pd.read_csv(path)
+    if "day" not in df.columns or "site_idx" not in df.columns:
+        raise ValueError(f"Expected columns day, site_idx in {path}")
+    df["day"] = df["day"].astype(int)
+    df["site_idx"] = df["site_idx"].astype(int)
+    return df
+
+
+def load_sites(path: str) -> pd.DataFrame:
+    df = pd.read_csv(path)
+    needed = {"site_idx", "site_name"}
+    if not needed.issubset(df.columns):
+        raise ValueError(f"Expected columns {sorted(needed)} in {path}")
+    df = df.copy()
+    df["site_idx"] = df["site_idx"].astype(int)
+    df["site_name"] = df["site_name"].astype(str)
+    if "total_demand" in df.columns:
+        df["total_demand"] = pd.to_numeric(df["total_demand"], errors="coerce")
+    return df
+
+
+def compute_gaps(schedule_long: pd.DataFrame) -> pd.DataFrame:
+    gaps_rows: List[Dict[str, float]] = []
+    for site_idx, g in schedule_long.groupby("site_idx"):
+        days = sorted(g["day"].tolist())
+        if len(days) < 2:
+            continue
+        for a, b in zip(days, days[1:]):
+            gaps_rows.append({"site_idx": int(site_idx), "gap": int(b - a)})
+    return pd.DataFrame(gaps_rows)
+
+
+def plot_barcode(
+    schedule_long: pd.DataFrame,
+    sites: pd.DataFrame,
+    *,
+    days: int,
+    sort_by: str,
+    out_path: str,
+) -> None:
+    if not _HAS_MPL:
+        raise RuntimeError("Missing dependency: matplotlib (cannot plot).")
+
+    sites2 = sites.copy()
+    sites2["short_name"] = sites2["site_name"].map(short_site_name)
+
+    if sort_by == "site_idx":
+        sites2 = sites2.sort_values(["site_idx"])
+    elif sort_by == "total_demand":
+        if "total_demand" not in sites2.columns:
+            raise ValueError("sites CSV missing total_demand; cannot sort by total_demand")
+        sites2 = sites2.sort_values(["total_demand", "site_idx"], ascending=[False, True])
+    else:
+        raise ValueError("sort_by must be 'site_idx' or 'total_demand'")
+
+    order = sites2["site_idx"].tolist()
+    y_pos = {idx: i for i, idx in enumerate(order)}
+    y = schedule_long["site_idx"].map(y_pos).to_numpy()
+    x = schedule_long["day"].to_numpy()
+
+    fig, ax = plt.subplots(figsize=(14, 8))
+    ax.scatter(x, y, s=18, marker="|", linewidths=1.5, alpha=0.7, color="black")
+    ax.set_xlim(1, days)
+    ax.set_ylim(-1, len(order))
+    ax.set_xlabel("Day (1..365)")
+    ax.set_ylabel("Sites (sorted)")
+    ax.set_title("Schedule Barcode (Visits over 365 days)")
+    ax.grid(True, axis="x", alpha=0.15)
+
+    # Show a small subset of y tick labels for readability.
+    step = max(1, len(order) // 12)
+    tick_idx = list(range(0, len(order), step))
+    tick_labels = sites2["short_name"].tolist()
+    ax.set_yticks(tick_idx)
+    ax.set_yticklabels([tick_labels[i] for i in tick_idx], fontsize=9)
+
+    fig.tight_layout()
+    os.makedirs(os.path.dirname(out_path) or ".", exist_ok=True)
+    fig.savefig(out_path, dpi=160)
+    plt.close(fig)
+
+
+def plot_gap_deviation(
+    schedule_long: pd.DataFrame,
+    sites: pd.DataFrame,
+    *,
+    days: int,
+    gap_min: int,
+    out_path: str,
+) -> None:
+    if not _HAS_MPL:
+        raise RuntimeError("Missing dependency: matplotlib (cannot plot).")
+
+    # Infer f_i from schedule itself (more robust than requiring the frequency CSV).
+    freq = schedule_long.groupby("site_idx")["day"].size().rename("f_i").reset_index()
+    gaps = compute_gaps(schedule_long)
+    df = gaps.merge(freq, on="site_idx", how="left").merge(sites[["site_idx", "site_name"]], on="site_idx", how="left")
+    df["ideal_gap"] = df["f_i"].apply(lambda f: (days / f) if f and f > 0 else np.nan)
+    df["dev"] = df["gap"] - df["ideal_gap"]
+
+    # Group deviations by frequency for a boxplot.
+    freq_levels = sorted(df["f_i"].dropna().unique().astype(int).tolist())
+    data = [df.loc[df["f_i"] == f, "dev"].dropna().to_numpy() for f in freq_levels]
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5), gridspec_kw={"width_ratios": [2.2, 1.0]})
+
+    ax1.boxplot(data, labels=[str(f) for f in freq_levels], showfliers=False)
+    ax1.axhline(0, color="black", lw=1, alpha=0.6)
+    ax1.set_xlabel("Frequency f_i (visits/year)")
+    ax1.set_ylabel("Gap deviation (gap - 365/f_i) in days")
+    ax1.set_title("Gap Regularity by Frequency")
+    ax1.grid(True, axis="y", alpha=0.2)
+
+    # Quick diagnostics: min gap violations and deviation histogram.
+    violations = int((df["gap"] < gap_min).sum())
+    ax2.hist(df["dev"].dropna().to_numpy(), bins=20, color="tab:blue", alpha=0.85)
+    ax2.axvline(0, color="black", lw=1, alpha=0.6)
+    ax2.set_xlabel("Deviation (days)")
+    ax2.set_ylabel("Count")
+    ax2.set_title(f"Deviation Histogram\nGap_min<{gap_min} violations: {violations}")
+    ax2.grid(True, axis="y", alpha=0.2)
+
+    fig.tight_layout()
+    os.makedirs(os.path.dirname(out_path) or ".", exist_ok=True)
+    fig.savefig(out_path, dpi=160)
+    plt.close(fig)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Visualize optimized schedule outputs.")
+    parser.add_argument(
+        "--schedule-long",
+        default=None,
+        help="Path to data/schedule_long_*.csv. If omitted, uses the latest matching file in data/.",
+    )
+    parser.add_argument("--sites-csv", default=DEFAULT_SITES_CSV)
+    parser.add_argument("--days", type=int, default=365)
+    parser.add_argument("--gap-min", type=int, default=14)
+    parser.add_argument("--sort-by", choices=["site_idx", "total_demand"], default="total_demand")
+    args = parser.parse_args()
+
+    if args.schedule_long is None:
+        args.schedule_long = find_latest_file(os.path.join(OUTPUT_DIR, "schedule_long_*.csv"))
+
+    schedule_long = load_schedule_long(args.schedule_long)
+    sites = load_sites(args.sites_csv)
+
+    stem = stem_from_filename(args.schedule_long)
+    out_barcode = os.path.join(OUTPUT_DIR, f"schedule_barcode_{stem}.png")
+    out_gaps = os.path.join(OUTPUT_DIR, f"schedule_gap_deviation_{stem}.png")
+
+    plot_barcode(schedule_long, sites, days=args.days, sort_by=args.sort_by, out_path=out_barcode)
+    plot_gap_deviation(schedule_long, sites, days=args.days, gap_min=args.gap_min, out_path=out_gaps)
+
+    print(f"Saved: {out_barcode}")
+    print(f"Saved: {out_gaps}")
+
+
+if __name__ == "__main__":
+    main()
+