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2025-11-30 00:44:13 +08:00 · 2025-11-30 00:44:13 +08:00 · e4c411bbe2
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--- a/README.md
+++ b/README.md
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 # gw
--- a/app.py
+++ b/app.py
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 import json
 import os
 import matplotlib
 import pandas as pd
 import solara
 from mesa.visualization import SolaraViz, make_plot_component
 from simulation_model import SimulationModel
 # Configure matplotlib to support Chinese labels.
 matplotlib.rcParams["font.family"] = ["Microsoft YaHei", "SimHei", "sans-serif"]
 matplotlib.rcParams["axes.unicode_minus"] = False
 # Load default parameters from data/model_params.json
 with open("data/model_params.json", "r", encoding="utf-8") as f:
    _cfg = json.load(f)
 def _load_factory_name_map():
    try:
        with open("data/factory_mapping.json", "r", encoding="utf-8") as f:
            data = json.load(f)
    except FileNotFoundError:
        return {}
    # data is Chinese name -> code; invert it
    return {v: k for k, v in data.items()}
 FACTORY_NAME_MAP = _load_factory_name_map()
 def _load_product_catalog():
    try:
        try:
            df = pd.read_csv("data/product.csv", encoding="utf-8")
        except UnicodeDecodeError:
            df = pd.read_csv("data/product.csv", encoding="gbk")
    except FileNotFoundError:
        return pd.DataFrame()
    df.columns = [c.strip() for c in df.columns]
    rename_map = {
        df.columns[0]: "平台",
        df.columns[1]: "标准机型",
        df.columns[2]: "叶片型号",
    }
    df = df.rename(columns=rename_map)
    df["平台"] = df["平台"].astype(str).str.strip()
    df["标准机型"] = df["标准机型"].astype(str).str.strip()
    df["叶片型号"] = df["叶片型号"].astype(str).str.strip()
    return df
 PRODUCT_DF = _load_product_catalog()
 ALL_PLATFORMS = sorted(PRODUCT_DF["平台"].dropna().unique()) if not PRODUCT_DF.empty else []
 ALL_STANDARDS = sorted(PRODUCT_DF["标准机型"].dropna().unique()) if not PRODUCT_DF.empty else []
 def _platform_options():
    return ["全部"] + ALL_PLATFORMS
 def _standard_options(platform):
    if PRODUCT_DF.empty:
        return ["全部"]
    if platform and platform != "全部":
        stds = sorted(PRODUCT_DF.loc[PRODUCT_DF["平台"] == platform, "标准机型"].dropna().unique())
    else:
        stds = ALL_STANDARDS
    return ["全部"] + [s for s in stds if s]
 def _product_set_for_selection(platform, standard):
    if PRODUCT_DF.empty:
        return []
    df = PRODUCT_DF
    if platform and platform != "全部":
        df = df[df["平台"] == platform]
    if standard and standard != "全部":
        df = df[df["标准机型"] == standard]
    return sorted(df["叶片型号"].dropna().unique())
 def _legend_postprocess(label_map, ylabel=None):
    def _post(ax):
        handles, labels = ax.get_legend_handles_labels()
        if labels:
            mapped = [label_map.get(label, label) for label in labels]
            ax.legend(handles, mapped, loc="best")
        if ylabel:
            ax.set_ylabel(ylabel)
    return _post
 def _split_product_set(text: str):
    return [p.strip() for p in text.split(",") if p.strip()]
@solara.component
 def Page():
    platform_selected = solara.use_reactive("全部")
    standard_selected = solara.use_reactive("全部")
    initial_product_set_text = ",".join(_cfg.get("product_set", []))
    product_set_value = solara.use_reactive(initial_product_set_text)
    def sync_product_set(platform_val, standard_val):
        blades = _product_set_for_selection(platform_val, standard_val)
        if blades:
            product_set_value.set(",".join(blades))
        elif PRODUCT_DF.empty:
            product_set_value.set(initial_product_set_text)
        else:
            product_set_value.set("")
    def on_platform_change(val):
        platform_selected.set(val)
        std_opts = _standard_options(val)
        if standard_selected.value not in std_opts:
            standard_selected.set("全部")
            active_std = "全部"
        else:
            active_std = standard_selected.value
        sync_product_set(val, active_std)
    def on_standard_change(val):
        standard_selected.set(val)
        sync_product_set(platform_selected.value, val)
    # Build model params fresh each render so product_set value stays in sync
    model_params = {
        "product_set": {"type": "InputText", "value": product_set_value, "label": "产品集合（逗号分隔）"},
        "is_within_region_allocation_only": {
            "type": "Select",
            "value": bool(_cfg.get("is_within_region_allocation_only", False)),
            "values": [False, True],
            "label": "仅区域内调拨？（布尔）",
        },
        "month1": {"type": "InputText", "value": _cfg.get("month1", 220), "label": "第1个月效率（小时/支）"},
        "month2": {"type": "InputText", "value": _cfg.get("month2", 100), "label": "第2个月效率（小时/支）"},
        "month3": {"type": "InputText", "value": _cfg.get("month3", 45), "label": "第3个月效率（小时/支）"},
        "month4": {"type": "InputText", "value": _cfg.get("month4", 36), "label": "第4个月及以后效率（小时/支）"},
        "holiday_days_1": {"type": "InputText", "value": _cfg.get("holiday_days_1", 2), "label": "1月假日天数（天）"},
        "holiday_days_2": {"type": "InputText", "value": _cfg.get("holiday_days_2", 2), "label": "2月假日天数（天）"},
        "holiday_days_3": {"type": "InputText", "value": _cfg.get("holiday_days_3", 2), "label": "3月假日天数（天）"},
        "holiday_days_4": {"type": "InputText", "value": _cfg.get("holiday_days_4", 2), "label": "4月假日天数（天）"},
        "holiday_days_5": {"type": "InputText", "value": _cfg.get("holiday_days_5", 2), "label": "5月假日天数（天）"},
        "holiday_days_6": {"type": "InputText", "value": _cfg.get("holiday_days_6", 2), "label": "6月假日天数（天）"},
        "holiday_days_7": {"type": "InputText", "value": _cfg.get("holiday_days_7", 2), "label": "7月假日天数（天）"},
        "holiday_days_8": {"type": "InputText", "value": _cfg.get("holiday_days_8", 2), "label": "8月假日天数（天）"},
        "holiday_days_9": {"type": "InputText", "value": _cfg.get("holiday_days_9", 2), "label": "9月假日天数（天）"},
        "holiday_days_10": {"type": "InputText", "value": _cfg.get("holiday_days_10", 2), "label": "10月假日天数（天）"},
        "holiday_days_11": {"type": "InputText", "value": _cfg.get("holiday_days_11", 2), "label": "11月假日天数（天）"},
        "holiday_days_12": {"type": "InputText", "value": _cfg.get("holiday_days_12", 2), "label": "12月假日天数（天）"},
    }
    for key, val in _cfg.items():
        if key.startswith("factor_"):
            suffix = key.replace("factor_", "")
            label_name = FACTORY_NAME_MAP.get(suffix, suffix)
            model_params[key] = {"type": "InputText", "value": val, "label": f"{label_name} 磨合系数（倍率）"}
    active_model = solara.use_memo(
        lambda: SimulationModel(product_set=_split_product_set(product_set_value.value)),
        dependencies=[product_set_value.value],
    )
    production_label_map = {region: f"{region}-产出[套]" for region in active_model.region_names}
    demand_label_map = {f"demand_{region}": f"{region}-需求[套]" for region in active_model.demand_regions}
    inventory_label_map = {f"inventory_{region}": f"{region}-库存[套]" for region in active_model.region_names}
    fulfill_pct_label_map = {f"fulfill_pct_{region}": f"{region}-满足率[%]" for region in active_model.demand_regions}
    unmet_label_map = {f"unmet_{region}": f"{region}-未满足需求[套]" for region in active_model.demand_regions}
    overall_fulfill_label_map = {"fulfill_pct_overall": "总体满足率[%]"}
    transport_cost_label_map = {"transport_cost": "运输成本[万元]"}
    fulfill_month_label_map = {f"fulfill_month_{region}": f"{region}-当月满足率[%]" for region in active_model.demand_regions}
    fulfill_cum_label_map = {f"fulfill_cum_{region}": f"{region}-累计满足率[%]" for region in active_model.demand_regions}
    transport_units_label_map = {f"transport_units_{region}": f"{region}-运输量[套]" for region in active_model.demand_regions}
    RegionPlot = make_plot_component(
        {region: f"C{idx}" for idx, region in enumerate(active_model.region_names)},
        post_process=_legend_postprocess(production_label_map, ylabel="产出[套]"),
    )
    DemandPlot = make_plot_component(
        {f"demand_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
        post_process=_legend_postprocess(demand_label_map, ylabel="需求量[套]"),
    )
    InventoryPlot = make_plot_component(
        {f"inventory_{region}": f"C{idx}" for idx, region in enumerate(active_model.region_names)},
        post_process=_legend_postprocess(inventory_label_map, ylabel="库存量[套]"),
    )
    FulfillPlot = make_plot_component(
        {f"fulfill_pct_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
        post_process=_legend_postprocess(fulfill_pct_label_map, ylabel="满足率[%]"),
    )
    UnmetPlot = make_plot_component(
        {f"unmet_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
        post_process=_legend_postprocess(unmet_label_map, ylabel="未满足需求[套]"),
    )
    OverallFulfillPlot = make_plot_component(
        {"fulfill_pct_overall": "tab:purple"},
        post_process=_legend_postprocess(overall_fulfill_label_map, ylabel="总体满足率[%]"),
    )
    TransportCostPlot = make_plot_component(
        {"transport_cost": "tab:brown"},
        post_process=_legend_postprocess(transport_cost_label_map, ylabel="运输成本[万元]"),
    )
    FulfillMonthPlot = make_plot_component(
        {f"fulfill_month_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
        post_process=_legend_postprocess(fulfill_month_label_map, ylabel="当月满足率[%]"),
    )
    FulfillCumPlot = make_plot_component(
        {f"fulfill_cum_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
        post_process=_legend_postprocess(fulfill_cum_label_map, ylabel="累计满足率[%]"),
    )
    TransportUnitsPlot = make_plot_component(
        {f"transport_units_{region}": f"C{idx}" for idx, region in enumerate(active_model.demand_regions)},
        post_process=_legend_postprocess(transport_units_label_map, ylabel="运输量[套]"),
    )
    def get_total_production(model):
        return solara.Markdown(f"**累计产量（机组）：{model.cumulative_production:.2f}**")
    def get_mean_abs_error(model):
        return solara.Markdown(f"**工厂年产量均值绝对误差：{model.mean_abs_error:.2f}**")
    with solara.Column() as main:
        with solara.Card("产品筛选"):
            solara.Select(
                label="平台",
                value=platform_selected,
                values=_platform_options(),
                on_value=on_platform_change,
            )
            solara.Select(
                label="标准机型",
                value=standard_selected,
                values=_standard_options(platform_selected.value),
                on_value=on_standard_change,
            )
            solara.Text(f"叶片型号数：{len(_split_product_set(product_set_value.value))}")
        SolaraViz(
            active_model,
            renderer=None,
            components=[
                RegionPlot,
                DemandPlot,
                InventoryPlot,
                FulfillPlot,
                UnmetPlot,
                OverallFulfillPlot,
                TransportCostPlot,
                FulfillMonthPlot,
                FulfillCumPlot,
                TransportUnitsPlot,
                get_total_production,
                get_mean_abs_error,
            ],
            model_params=model_params,
            name="产线生产与需求可视化",
            show_parameter_values=True,
        )
    return main
 page = Page()
--- a/build_distance_matrix.py
+++ b/build_distance_matrix.py
@ -0,0 +1,65 @@
 import math
 from pathlib import Path
 import pandas as pd
 def haversine(lat1, lon1, lat2, lon2):
    R = 6371.0  # Earth radius in km
    phi1, phi2 = math.radians(lat1), math.radians(lat2)
    dphi = math.radians(lat2 - lat1)
    dlambda = math.radians(lon2 - lon1)
    a = math.sin(dphi / 2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(dlambda / 2) ** 2
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
    return R * c
 def _read_csv_flexible(path: str, header: int = 0):
    for enc in ["utf-8", "utf-8-sig", "gbk", "latin1"]:
        try:
            return pd.read_csv(path, encoding=enc, engine="python")
        except UnicodeDecodeError:
            continue
    # fallback with ignoring errors
    return pd.read_csv(path, encoding="utf-8", engine="python", encoding_errors="ignore")
 def _infer_coords(df, name_col, lat_col, lon_col):
    # Cast numeric columns
    df[lat_col] = pd.to_numeric(df[lat_col], errors="coerce")
    df[lon_col] = pd.to_numeric(df[lon_col], errors="coerce")
    lat_range = df[lat_col].between(-90, 90).mean()
    lon_range = df[lon_col].between(-180, 180).mean()
    # If swapped, switch
    if lat_range < lon_range:
        df[lat_col], df[lon_col] = df[lon_col], df[lat_col]
    df = df.rename(columns={name_col: "name", lat_col: "lat", lon_col: "lon"})
    return df[["name", "lat", "lon"]].dropna()
 def main():
    demand = _read_csv_flexible("data/DemandLocation.csv")
    factory = _read_csv_flexible("data/FactoryLocation.csv")
    # Demand: assume first column name, last two numeric
    demand_coords = _infer_coords(demand, demand.columns[0], demand.columns[-2], demand.columns[-1])
    # Factory: assume first column name, last two numeric
    factory_coords = _infer_coords(factory, factory.columns[0], factory.columns[-2], factory.columns[-1])
    rows = []
    for _, drow in demand_coords.iterrows():
        for _, frow in factory_coords.iterrows():
            dist = haversine(drow["lat"], drow["lon"], frow["lat"], frow["lon"])
            rows.append({"demand_city": drow["name"], "factory": frow["name"], "distance_km": dist})
    df = pd.DataFrame(rows)
    out_path = Path("data") / "distance_matrix.csv"
    df.to_csv(out_path, index=False, encoding="utf-8-sig")
    print(f"Saved {len(df)} rows to {out_path}")
 if __name__ == "__main__":
    main()
--- a/data/DemandLocation.csv
+++ b/data/DemandLocation.csv
@ -0,0 +1,98 @@
 需求方,经度,纬度
 三门峡市,111.581892,34.592412
 中卫市,104.326348,37.42582674
 乌兰察布市,112.583248,41.859146
 乌鲁木齐市,88.3526,43.4871
 佳木斯市,132.578341,46.90926
 兴安盟,122.089308,45.921339
 南阳市,112.97302,32.488644
 吐鲁番地区,88.26158,42.931764
 吴忠市,105.999463,37.823582
 哈密市,93.2830057,43.96107218
 哈尔滨市,128.1882308,45.77706491
 商丘市,115.17386,34.39346
 国际,101.091124,38.787478
 塔城地区,85.047175,46.867906
 大同市,114.3823027,39.46432744
 天津市,117.408432,40.046544
 定西市,104.24555,34.360151
 巴音郭楞蒙古自治州,87.795193,39.202885
 庆阳市,106.578249,36.363741
 延安市,107.7202223,36.9307021
 张家口市,114.366182,41.115161
 德州市,116.7061394,37.21128042
 忻州市,111.320864,38.89993
 恩施土家族苗族自治州,108.789522,30.452828
 承德市,117.306009,41.817294
 新疆生产建设兵团第十师,87.659628,47.373723
 昆明市,103.3556505,25.74601202
 昌吉回族自治州,90.524934,44.753213
 晋中市,113.9980185,37.59244878
 曲靖市,104.066905,25.501188
 朔州市,111.95946,39.612056
 朝阳市,120.960563,42.159688
 来宾市,108.8706184,23.53272696
 松原市,123.5325177,44.3099525
 梧州市,110.8023439,23.92578726
 榆林市,109.424847,38.670326
 永州市,111.470832,25.975085
 沈阳市,123.350854,42.471099
 沧州市,117.354557,38.27852
 河池市,106.764545,25.04219
 济源市,112.1719039,35.04617738
 海南藏族自治州,99.66212099,36.11799294
 海西蒙古族藏族自治州,96.085432,37.13638947
 渭南市,109.5518929,35.41186149
 烟台市,121.0760873,36.62456081
 玉林市,110.140777,22.17541
 白城市,123.906296,45.50445
 百色市,105.9323029,24.4875734
 秦皇岛市,119.2400992,39.53770068
 绥化市,127.581712,46.566521
 聊城市,115.4099,36.5642
 荆州市,112.2145266,29.71230666
 荆门市,112.590853,31.324363
 葫芦岛市,120.69668,40.701877
 衡水市,115.432929,38.006891
 赣州市,114.879181,25.768879
 辽阳市,123.438258,41.423986
 运城市,111.8791446,35.53009921
 通辽市,121.079033,43.025639
 邢台市,114.9568513,37.40531178
 鄂尔多斯市,110.669035,40.003893
 酒泉市,97.142116,40.792238
 重庆市,108.012898,29.179982
 钦州市,109.3893375,22.15819114
 铜仁市,108.504004,28.563993
 锡林郭勒盟,114.8256,44.5873
 锦州市,121.510124,40.97325273
 阜阳市,114.967423,33.016503
 阳江市,112.0157107,20.9886087
 阳泉市,113.148888,38.211682
 阿勒泰地区,85.62480544,47.14043099
 鞍山市,122.3749923,41.31678205
 黄冈市,115.937974,29.922599
 黔西南布依族苗族自治州,105.87635,24.965761
 济南市,117.02,36.40
 安阳市,114,35.72
 白银市,104.09,36.41
 毕节市,106,26.86
 邯郸市,114.47,36.60
 七台河市,131.01,45.78
 通化市,125.95,41.73
 盐城市,120.17,33.36
 本溪市,123.69,41.49
 鹤岗市,130.30,47.36
 开封市,114.32,34.80
 洛阳市,112.45,34.72
 莆田市,119.01,25.46
 新乡市,113.81,35.20
 伊犁哈萨克自治州,43.916823,81.324136
 镇江市,119.43,32.19
 上海市,121.29,31.14
 桂林市,110.296647,25.27981
 呼和浩特市,111.41,40.48
 济宁市,116.35,35.41
 天门市,113.162071,30.647308
 黔南布依族苗族自治州,106.45,25.45
 黔东南苗族侗族自治州,107.1720,25.1920
--- a/data/FactoryLocation.csv
+++ b/data/FactoryLocation.csv
@ -0,0 +1,28 @@
 工厂,经度,纬度
 艾郎玉门,97.142116,40.792238
 艾郎张北,114.366182,41.115161
 时代巴里坤,93.2830057,43.96107218
 时代百色,105.9323029,24.4875734
 时代射阳,119.5548,33.3112
 时代松原,123.5325177,44.3099525
 时代株洲,113.151737,27.835806
 双瑞大丰,120.43,33.11
 双瑞东营,118.07,36.55
 天顺濮阳,115.0742,35.7773
 天顺沙洋,112.590853,31.324363
 中材白城,123.906296,45.50445
 中材阜宁,119.8,33.8
 中材哈密,93.2830057,43.96107218
 中材邯郸,113.27,36.02
 中材酒泉,97.142116,40.792238
 中材连云港,119.13,34.36
 中材萍乡,113.89,27.66
 中材锡林,114.8256,44.5873
 中材兴安盟,122.089308,45.921339
 中材阳江,112.0157107,20.9886087
 中材伊吾,93.2830057,43.96107218
 中材玉溪,101.16,23.19
 重通昌吉,90.524934,44.753213
 重通大安,123.906296,45.50445
 重通如东,120.42,32.12
 重通武威,103.9680036,39.09623555
--- a/data/ProductionLine.csv
+++ b/data/ProductionLine.csv
@ -0,0 +1,113 @@
 区域名,工厂名,是否新工厂,产线ID,生产型号,开始月份,结束月份
 西北东部,艾郎玉门,是,艾郎玉门1,GWBD-A2,1,12
 西北东部,艾郎玉门,是,艾郎玉门2,GWBD-A2,1,12
 华北,艾郎张北,是,艾郎张北1,GWBD-A2,1,12
 华北,艾郎张北,是,艾郎张北2,GWBD-A2,1,12
 西北西部,时代巴里坤,是,时代巴里坤1,GWBD-A2,5,12
 西北西部,时代巴里坤,是,时代巴里坤2,GWBD-A2,5,12
 西北西部,时代巴里坤,是,时代巴里坤3,GW99A,3,4
 西北西部,时代巴里坤,是,时代巴里坤4,GW99A,6,9
 华南,时代百色,是,时代百色1,GWBD-A3,5,12
 华南,时代百色,是,时代百色2,GWBD-A3,5,12
 华东南北部,时代射阳,是,时代射阳1,GW130,9,12
 华东南北部,时代射阳,是,时代射阳2,GW130,9,12
 东北,时代松原,是,时代松原1,GWBD-A3,6,12
 东北,时代松原,是,时代松原2,GWBD-A3,6,12
 华东南南部,时代株洲,是,时代株洲1,GW99A,4,12
 华东南南部,时代株洲,是,时代株洲2,GW99A,4,12
 华东南北部,双瑞大丰,是,双瑞大丰1,GW110.5,2,11
 华东南北部,双瑞大丰,是,双瑞大丰2,GW110.5,2,11
 华东南北部,双瑞大丰,是,双瑞大丰3,GW93,1,1
 华东南北部,双瑞东营,否,双瑞东营1,GWBD-A2,10,12
 华东南北部,双瑞东营,否,双瑞东营2,GWBD-A2,10,12
 华东南北部,双瑞东营,否,双瑞东营3,GWBD-A2,10,12
 华东南北部,双瑞东营,否,双瑞东营4,GWBD-A2,10,12
 华东南北部,天顺濮阳,否,天顺濮阳1,GW99A,8,12
 华东南北部,天顺濮阳,否,天顺濮阳2,GW99A,8,12
 华东南北部,天顺沙洋,否,天顺沙洋1,GW93,2,2
 华东南北部,天顺沙洋,是,天顺沙洋2,GW93,5,5
 华东南北部,天顺沙洋,否,天顺沙洋3,GW93,11,11
 华东南北部,天顺沙洋,否,天顺沙洋4,GW99,10,11
 华东南北部,天顺沙洋,否,天顺沙洋5,GW99A,5,12
 华东南北部,天顺沙洋,否,天顺沙洋6,GW99A,5,12
 东北,中材白城,否,中材白城1,GWBD-A3,8,8
 华东南北部,中材阜宁,是,中材阜宁1,GW110.5,4,11
 华东南北部,中材阜宁,是,中材阜宁2,GW110.5,4,11
 华东南北部,中材阜宁,是,中材阜宁3,GW110.5,4,11
 华东南北部,中材阜宁,是,中材阜宁4,GW83.4,1,11
 华东南北部,中材阜宁,否,中材阜宁5,GW93,6,8
 华东南北部,中材阜宁,否,中材阜宁6,GW99A,1,7
 华东南北部,中材阜宁,否,中材阜宁7,GW99A,1,7
 华东南北部,中材阜宁,否,中材阜宁8,SI90.2,1,12
 华东南北部,中材阜宁,否,中材阜宁9,SI90.2,1,12
 华东南北部,中材阜宁,是,中材阜宁10,SI90.2,3,12
 华东南北部,中材阜宁,是,中材阜宁11,SI90.2,3,12
 华东南北部,中材阜宁,是,中材阜宁12,SI90.2,3,12
 华东南北部,中材阜宁,是,中材阜宁13,SI90.2,3,12
 西北西部,中材哈密,否,中材哈密1,GWBD-A3,7,11
 西北西部,中材哈密,否,中材哈密2,GWBD-A3,7,11
 华北,中材邯郸,否,中材邯郸1,GW99A,1,12
 华北,中材邯郸,否,中材邯郸2,GW99A,1,12
 华北,中材邯郸,是,中材邯郸3,GWBD-A2,4,12
 华北,中材邯郸,是,中材邯郸4,GWBD-A2,4,12
 西北东部,中材酒泉,否,中材酒泉1,GW99,1,12
 西北东部,中材酒泉,否,中材酒泉2,GW99,1,12
 西北东部,中材酒泉,否,中材酒泉3,GWBD-A2,2,12
 西北东部,中材酒泉,否,中材酒泉4,GWBD-A2,2,12
 西北东部,中材酒泉,否,中材酒泉5,GWBD-A2,2,12
 西北东部,中材酒泉,是,中材酒泉6,GWBD-A2,2,12
 西北东部,中材酒泉,否,中材酒泉7,SI90.2,1,1
 西北东部,中材酒泉,否,中材酒泉8,SI90.2,3,4
 西北东部,中材酒泉,否,中材酒泉9,SI90.2,3,4
 西北东部,中材酒泉,否,中材酒泉10,SI90.2,9,9
 西北东部,中材酒泉,是,中材酒泉11,SI90.2,4,12
 西北东部,中材酒泉,是,中材酒泉12,SI90.2,4,12
 华东南北部,中材连云港,是,中材连云港1,GW93,4,4
 华东南北部,中材连云港,否,中材连云港2,GW93,1,12
 华东南北部,中材连云港,否,中材连云港3,GW93,1,12
 华东南南部,中材萍乡,否,中材萍乡1,GW83.4,2,3
 华东南南部,中材萍乡,是,中材萍乡2,GW93,1,10
 华东南南部,中材萍乡,是,中材萍乡3,GW93,1,10
 华东南南部,中材萍乡,是,中材萍乡4,GWBD-A3,2,12
 华东南南部,中材萍乡,是,中材萍乡5,GWBD-A3,2,12
 东北,中材锡林,否,中材锡林1,GW99,1,12
 东北,中材锡林,否,中材锡林2,GW99,1,12
 东北,中材锡林,是,中材锡林3,GWBD-A2,2,12
 东北,中材锡林,是,中材锡林4,GWBD-A2,2,12
 东北,中材锡林,是,中材锡林5,GWBD-A2,2,12
 东北,中材锡林,否,中材锡林6,SI90.2,1,1
 东北,中材锡林,否,中材锡林7,SI90.2,1,1
 东北,中材兴安盟,是,中材兴安盟1,GW93,1,9
 东北,中材兴安盟,是,中材兴安盟2,GW93,1,9
 华南,中材阳江,是,中材阳江1,GW130,11,12
 华南,中材阳江,是,中材阳江2,GW130,11,12
 华南,中材阳江,否,中材阳江3,SI122,1,12
 华南,中材阳江,否,中材阳江4,SI122,1,12
 西北西部,中材伊吾,否,中材伊吾1,GWBD-A2,3,12
 西北西部,中材伊吾,否,中材伊吾2,GWBD-A2,3,12
 西北西部,中材伊吾,是,中材伊吾3,GWBD-A3,7,9
 华南,中材玉溪,否,中材玉溪1,GW93,3,3
 西北西部,重通昌吉,否,重通昌吉1,GW99,11,11
 西北西部,重通昌吉,是,重通昌吉2,GW99A,2,10
 西北西部,重通昌吉,否,重通昌吉3,GWBD-A2,1,12
 西北西部,重通昌吉,否,重通昌吉4,GWBD-A2,1,12
 西北西部,重通昌吉,否,重通昌吉5,GWBD-A2,1,12
 东北,重通大安,是,重通大安1,GWBD-A2,3,12
 东北,重通大安,是,重通大安2,GWBD-A2,4,12
 华东南北部,重通如东,是,重通如东1,GW110.5,6,10
 华东南北部,重通如东,是,重通如东2,GW110.5,6,10
 华东南北部,重通如东,否,重通如东3,GW76,1,6
 华东南北部,重通如东,否,重通如东4,GW76,1,6
 华东南北部,重通如东,否,重通如东5,GW76,11,12
 华东南北部,重通如东,否,重通如东6,GW76,11,12
 华东南北部,重通如东,否,重通如东7,GW81,1,12
 华东南北部,重通如东,否,重通如东8,GW81,1,12
 华东南北部,重通如东,否,重通如东9,GW86,10,12
 华东南北部,重通如东,否,重通如东10,GWBD-D,10,10
 华东南北部,重通如东,是,重通如东11,GW83.3,7,9
 西北东部,重通武威,否,重通武威1,GW83.4,1,1
 西北东部,重通武威,否,重通武威2,GW99A,1,1
 西北东部,重通武威,否,重通武威3,GWBD-A2,1,12
 西北东部,重通武威,否,重通武威4,GWBD-A2,1,12
 西北东部,重通武威,否,重通武威5,GWBD-A3,4,12
 西北东部,重通武威,否,重通武威6,GWBD-A3,4,12
--- a/data/ProductionLine未修改.csv
+++ b/data/ProductionLine未修改.csv
@ -0,0 +1,106 @@
 区域名,工厂名,是否新工厂,产线ID,生产型号,开始月份,结束月份
 西北东部,艾郎玉门,否,艾郎玉门1,GWBD-A2,1,12
 西北东部,艾郎玉门,否,艾郎玉门2,GWBD-A2,1,12
 华北,艾郎张北,否,艾郎张北1,GWBD-A2,1,12
 华北,艾郎张北,否,艾郎张北2,GWBD-A2,1,12
 西北西部,时代巴里坤,是,时代巴里坤1,GWBD-A2,5,12
 西北西部,时代巴里坤,是,时代巴里坤2,GWBD-A2,5,12
 西北西部,时代巴里坤,是,时代巴里坤3,GW99A,3,9
 华南,时代百色,是,时代百色1,GWBD-A3,5,12
 华南,时代百色,是,时代百色2,GWBD-A3,5,12
 华东南北部,时代射阳,是,时代射阳1,GW130,9,12
 华东南北部,时代射阳,是,时代射阳2,GW130,9,12
 东北,时代松原,是,时代松原1,GWBD-A3,5,12
 东北,时代松原,是,时代松原2,GWBD-A3,5,12
 华东南南部,时代株洲,否,时代株洲1,GW99A,4,12
 华东南南部,时代株洲,否,时代株洲2,GW99A,4,12
 华东南北部,双瑞大丰,否,双瑞大丰1,GW110.5,2,11
 华东南北部,双瑞大丰,否,双瑞大丰2,GW110.5,2,11
 华东南北部,双瑞大丰,否,双瑞大丰3,GW93,1,1
 华东南北部,双瑞东营,是,双瑞东营1,GWBD-A2,9,12
 华东南北部,双瑞东营,是,双瑞东营2,GWBD-A2,9,12
 华东南北部,双瑞东营,是,双瑞东营3,GWBD-A2,9,12
 华东南北部,双瑞东营,是,双瑞东营4,GWBD-A2,9,12
 华东南北部,天顺濮阳,是,天顺濮阳1,GW99A,8,12
 华东南北部,天顺濮阳,是,天顺濮阳2,GW99A,8,12
 华东南北部,天顺沙洋,否,天顺沙洋1,GW93,2,11
 华东南北部,天顺沙洋,否,天顺沙洋2,GW99,10,11
 华东南北部,天顺沙洋,是,天顺沙洋3,GW99A,5,12
 华东南北部,天顺沙洋,是,天顺沙洋4,GW99A,5,12
 东北,中材白城,是,中材白城1,GWBD-A3,8,8
 华东南北部,中材阜宁,否,中材阜宁1,GW110.5,4,12
 华东南北部,中材阜宁,是,中材阜宁2,GW110.5,4,12
 华东南北部,中材阜宁,是,中材阜宁3,GW110.5,4,12
 华东南北部,中材阜宁,否,中材阜宁4,GW83.4,1,11
 华东南北部,中材阜宁,否,中材阜宁5,GW93,1,8
 华东南北部,中材阜宁,否,中材阜宁6,GW99A,1,7
 华东南北部,中材阜宁,否,中材阜宁7,GW99A,1,7
 华东南北部,中材阜宁,否,中材阜宁8,SI90.2,1,12
 华东南北部,中材阜宁,否,中材阜宁9,SI90.2,1,12
 华东南北部,中材阜宁,否,中材阜宁10,SI90.2,1,12
 华东南北部,中材阜宁,否,中材阜宁11,SI90.2,1,12
 华东南北部,中材阜宁,是,中材阜宁12,SI90.2,3,12
 华东南北部,中材阜宁,是,中材阜宁13,SI90.2,3,12
 西北西部,中材哈密,是,中材哈密1,GWBD-A3,7,12
 西北西部,中材哈密,是,中材哈密2,GWBD-A3,7,12
 华北,中材邯郸,否,中材邯郸1,GW99A,1,12
 华北,中材邯郸,否,中材邯郸2,GW99A,1,12
 华北,中材邯郸,是,中材邯郸3,GWBD-A2,4,12
 华北,中材邯郸,是,中材邯郸4,GWBD-A2,4,12
 西北东部,中材酒泉,否,中材酒泉1,GW99,1,12
 西北东部,中材酒泉,否,中材酒泉2,GW99,1,12
 西北东部,中材酒泉,否,中材酒泉3,GWBD-A2,1,12
 西北东部,中材酒泉,否,中材酒泉4,GWBD-A2,1,12
 西北东部,中材酒泉,否,中材酒泉5,GWBD-A2,1,12
 西北东部,中材酒泉,是,中材酒泉6,GWBD-A2,2,12
 西北东部,中材酒泉,否,中材酒泉7,SI90.2,1,12
 西北东部,中材酒泉,否,中材酒泉8,SI90.2,1,12
 西北东部,中材酒泉,否,中材酒泉9,SI90.2,4,12
 西北东部,中材酒泉,否,中材酒泉10,SI90.2,4,12
 华东南北部,中材连云港,否,中材连云港1,GW93,4,4
 华东南北部,中材连云港,否,中材连云港2,GW93,1,12
 华东南北部,中材连云港,否,中材连云港3,GW93,1,12
 华东南南部,中材萍乡,否,中材萍乡1,GW83.4,2,6
 华东南南部,中材萍乡,否,中材萍乡2,GW93,1,10
 华东南南部,中材萍乡,否,中材萍乡3,GW93,1,10
 华东南南部,中材萍乡,否,中材萍乡4,GWBD-A3,2,12
 华东南南部,中材萍乡,否,中材萍乡5,GWBD-A3,2,12
 东北,中材锡林,否,中材锡林1,GW99,1,12
 东北,中材锡林,否,中材锡林2,GW99,1,12
 东北,中材锡林,否,中材锡林3,GWBD-A2,1,12
 东北,中材锡林,否,中材锡林4,GWBD-A2,1,12
 东北,中材锡林,是,中材锡林5,GWBD-A2,4,12
 东北,中材锡林,否,中材锡林6,SI90.2,1,1
 东北,中材锡林,否,中材锡林7,SI90.2,1,1
 东北,中材兴安盟,否,中材兴安盟1,GW93,1,9
 东北,中材兴安盟,否,中材兴安盟2,GW93,1,9
 华南,中材阳江,是,中材阳江1,GW130,11,12
 华南,中材阳江,是,中材阳江2,GW130,11,12
 华南,中材阳江,否,中材阳江3,SI122,1,12
 华南,中材阳江,否,中材阳江4,SI122,1,12
 西北西部,中材伊吾,否,中材伊吾1,GWBD-A2,3,12
 西北西部,中材伊吾,否,中材伊吾2,GWBD-A2,3,12
 西北西部,中材伊吾,是,中材伊吾3,GWBD-A3,7,9
 华南,中材玉溪,否,中复玉溪1,GW93,3,3
 西北西部,重通昌吉,否,重通昌吉1,GW99,11,11
 西北西部,重通昌吉,否,重通昌吉2,GW99A,2,10
 西北西部,重通昌吉,否,重通昌吉3,GWBD-A2,1,12
 西北西部,重通昌吉,否,重通昌吉4,GWBD-A2,1,12
 西北西部,重通昌吉,是,重通昌吉5,GWBD-A2,11,12
 东北,重通大安,是,重通大安1,GWBD-A2,3,12
 东北,重通大安,是,重通大安2,GWBD-A2,4,12
 华东南北部,重通如东,是,重通如东1,GW110.5,6,10
 华东南北部,重通如东,是,重通如东2,GW110.5,6,10
 华东南北部,重通如东,否,重通如东3,GW76,1,12
 华东南北部,重通如东,否,重通如东4,GW76,1,12
 华东南北部,重通如东,否,重通如东5,GW81,1,12
 华东南北部,重通如东,否,重通如东6,GW81,1,12
 华东南北部,重通如东,否,重通如东7,GW86,3,12
 华东南北部,重通如东,是,重通如东8,GWBD-D,6,11
 华东南北部,重通如东,否,重通如东9,GW83.3,7,9
 西北东部,重通武威,否,重通武威1,GW83.4,1,1
 西北东部,重通武威,否,重通武威2,GW99A,1,1
 西北东部,重通武威,否,重通武威3,GWBD-A2,1,12
 西北东部,重通武威,否,重通武威4,GWBD-A2,1,12
 西北东部,重通武威,否,重通武威5,GWBD-A3,4,12
 西北东部,重通武威,否,重通武威6,GWBD-A3,4,12
--- a/data/benchmark.csv
+++ b/data/benchmark.csv
@ -0,0 +1,28 @@
 工厂,1月,2月,3月,4月,5月,6月,7月,8月,9月,10月,11月,12月,全年
 艾郎玉门,2,6,12,13,12,12,9,14,12,12,12,12,128
 艾郎张北,3,6,12,14,12,12,12,12,12,12,12,12,131
 时代巴里坤,0,0,2,4,5,9,6,16,14,13,12,12,93
 时代百色,0,0,0,0,5,9,12,12,12,12,12,12,86
 时代射阳,0,0,0,0,0,0,0,0,1,2,4,6,13
 时代松原,0,0,0,0,0,5,6,10,15,16,12,12,76
 时代株洲,0,0,0,7,9,14,14,14,14,14,14,14,114
 双瑞大丰,1,2,3,6,7,10,8,10,10,10,7,7,81
 双瑞东营,0,0,0,0,0,0,0,0,0,15,15,16,46
 天顺濮阳,2,0,0,0,0,0,0,12,11,9,10,11,55
 天顺沙洋,1,4,0,0,9,13,13,13,13,17,19,11,113
 中材白城,0,0,0,0,0,0,0,3,0,0,0,0,3
 中材阜宁,34,18,31,50,42,45,52,48,46,43,49,31,489
 中材哈密,0,0,0,0,0,0,8,9,9,6,7,0,39
 中材邯郸,12,6,3,17,18,27,18,21,23,19,20,14,198
 中材酒泉,11,20,52,56,43,45,50,53,59,52,52,40,533
 中材连云港,6,9,13,16,13,12,5,5,5,0,0,7,91
 中材萍乡,2,17,21,19,24,27,29,28,17,13,14,5,216
 中材锡林,28,6,17,25,36,32,27,36,27,30,32,21,317
 中材兴安盟,2,8,10,18,14,18,12,11,14,0,0,0,107
 中材阳江,4,1,5,5,5,5,5,5,5,5,7,7,59
 中材伊吾,0,0,10,14,5,4,8,16,16,8,15,10,106
 中材玉溪,0,0,3,0,0,0,0,0,0,0,0,0,3
 重通昌吉,13,9,19,21,22,23,23,28,27,18,23,19,245
 重通大安,0,0,2,9,6,12,11,10,11,11,13,12,97
 重通如东,24,7,24,19,23,26,27,24,30,33,34,35,306
 重通武威,22,7,14,27,31,29,29,28,30,28,29,29,303
--- a/data/benchmark未修改.csv
+++ b/data/benchmark未修改.csv
@ -0,0 +1,28 @@
 工厂,1月,2月,3月,4月,5月,6月,7月,8月,9月,10月,11月,12月,全年
 艾郎玉门,2,6,12,13,12,12,9,14,12,12,12,12,128
 艾郎张北,3,6,12,14,12,12,12,12,12,12,12,12,131
 时代巴里坤,0,0,7,2,2,9,6,16,14,13,12,12,93
 时代百色,0,0,0,0,5,9,12,8,15,13,12,12,86
 时代射阳,0,0,0,0,0,0,0,0,1,0,6,6,13
 时代松原,0,0,0,0,0,6,5,10,15,16,12,12,76
 时代株洲,0,0,0,7,9,14,14,14,14,14,14,14,114
 双瑞大丰,1,2,11,6,7,10,8,10,10,10,6,0,81
 双瑞东营,0,0,0,0,0,0,0,0,3,15,14,14,46
 天顺濮阳,2,0,0,0,0,0,0,12,11,9,10,11,55
 天顺沙洋,1,4,0,0,9,13,13,13,13,17,19,11,113
 中材白城,0,0,0,0,0,0,0,3,0,0,0,0,3
 中材阜宁,34,18,31,50,42,45,52,48,46,43,49,31,489
 中材哈密,0,0,0,0,0,0,8,9,9,6,7,0,39
 中材邯郸,12,6,3,17,18,27,18,21,23,19,20,14,198
 中材酒泉,11,20,52,56,43,45,50,53,59,52,52,40,533
 中材连云港,6,9,13,16,13,12,5,5,5,0,0,7,91
 中材萍乡,2,17,21,19,24,27,29,28,17,13,14,5,216
 中材锡林,28,6,17,25,36,32,27,36,27,30,32,21,317
 中材兴安盟,2,8,10,18,14,18,12,11,14,0,0,0,107
 中材阳江,4,1,5,5,5,5,5,5,5,5,7,7,59
 中材伊吾,0,0,10,14,5,4,8,16,16,8,15,10,106
 中材玉溪,0,0,3,0,0,0,0,0,0,0,0,0,3
 重通昌吉,13,9,19,21,22,23,23,28,27,18,23,19,245
 重通大安,0,0,2,9,6,12,11,10,11,11,13,12,97
 重通如东,24,7,24,19,23,26,27,24,30,33,34,35,306
 重通武威,22,7,14,27,31,29,29,28,30,28,29,29,303
--- a/data/demand.csv
+++ b/data/demand.csv
@ -0,0 +1,350 @@
 生产型号,省份,市,需求区域,1月,2月,3月,4月,5月,6月,7月,8月,9月,10月,11月,12月,总量
 GW83.4,辽宁,葫芦岛市,东北,,,,,,,,,,1,,,1
 GW83.4,广西,桂林市,华南,3,,3,,,2,,,,,,,8
 GW93,广西,桂林市,华南,,2,2,,,,,,,,,,4
 GW93,广西,桂林市,华南,,,3,,,,,,,,,,3
 GW93,广西,桂林市,华南,1,3,3,,,,,,,,,,7
 GW99A,广西,玉林市,华南,1,,,,,,,,,,,,1
 GW130,江苏,盐城市,华东南北部,,,1,,,,,,,,,,1
 GW110.5,江苏,盐城市,华东南北部,,,1,2,13,12,9,20,20,17,6,,100
 GW93,江苏,盐城市,华东南北部,,,,,4,4,5,,,,,,13
 GW93,辽宁,沈阳市,东北,1,3,7,8,,,,,,,,,19
 GW93,新疆,昌吉回族自治州,西北西部,,1,12,8,10,,,,,,,,31
 SI122,广东,阳江市,华南,,,,,1,5,15,,,,,,21
 SI122,广东,阳江市,华南,,,,,,,1,,,,,,1
 SI122,广东,阳江市,华南,,,,,,5,13,4,,,,,22
 GW110.5,上海,上海市,华东,,,,4,16,8,8,,,,,,36
 GW110.5,江苏,盐城市,华东南北部,,,,,,,,,10,8,6,,24
 GW110.5,江苏,盐城市,华东南北部,,,,,8,10,2,4,,,,,24
 GW110.5,江苏,盐城市,华东南北部,,,,,,1,1,4,8,9,,,23
 GW93,广西,钦州市,华南,2,,1,,,,,,,,,,3
 GW93,广西,钦州市,华南,,1,1,1,,,,,,,,,3
 GW93,广西,钦州市,华南,2,,3,1,,,,,,,,,6
 GW93,广西,钦州市,华南,2,2,7,2,,,,,,,,,13
 GW99A,陕西,渭南市,华北,,,3,3,3,,,,,,,,9
 GW93,山西,太原市,华北,1,3,5,,,,,,,,,,9
 GW93,河南,新乡市,华东南北部,1,,,,,,,,,,,,1
 GW93,湖北,天门市,华东南北部,,,1,,,,,,,,,,1
 GW81,国际,国际,国际,1,,,,,,,,,,,,1
 GW76,国际,国际,国际,,,,,,,,,,,7,8,15
 SI90.2,国际,国际,国际,,,,,,,,4,5,4,4,,17
 SI90.2,新疆,乌鲁木齐市,西北西部,,,,,1,2,,,,,,,3
 GW93,新疆,乌鲁木齐市,西北西部,,,,3,3,2,2,3,,,,,13
 GW93,新疆,乌鲁木齐市,西北西部,,,,1,3,2,2,,,,,,8
 GWBD-A2,新疆,昌吉回族自治州,西北西部,,,,,,,,,5,2,9,,16
 GW99A,内蒙古,鄂尔多斯市,华北,,,,,,,,,,,5,6,11
 GW93,河北,承德市,华北,6,5,1,,,,,,,,,,12
 GWBD-A2,河北,承德市,华北,1,,,2,,,1,1,,,5,3,13
 GWBD-A2,河北,张家口市,华北,1,,,2,2,1,5,2,,,,1,14
 GWBD-A2,河北,张家口市,华北,,,,,,,,,,,,3,3
 GWBD-A2,新疆,哈密市,西北西部,,,,,,,,,,,1,,1
 GWBD-A2,新疆,哈密市,西北西部,,,,2,6,2,4,5,2,4,9,21,55
 GW93,广西,钦州市,华南,,,1,1,1,1,1,,,,,,5
 GW93,广西,钦州市,华南,2,1,4,2,2,4,1,,,,,,16
 GW93,辽宁,朝阳市,东北,,,3,3,2,4,2,5,5,5,8,5,42
 GW93,辽宁,朝阳市,东北,,,,2,1,1,,,1,2,4,3,14
 GW93,贵州,黔东南苗族侗族自治州,华南,1,,3,3,4,3,2,,,,,,16
 GW99A,湖北,荆门市,华东南北部,,,,,,,,,,2,6,8,16
 GW99A,湖北,荆门市,华东南北部,,,,,,2,4,4,6,3,8,,27
 GWBD-A3,辽宁,朝阳市,东北,,,,,1,,,,,,,,1
 GW93,辽宁,朝阳市,东北,,,2,5,2,9,2,3,2,,,,25
 SI90.2,国际,国际,国际,,7,,9,,,,,,,,,16
 GW93,内蒙古,通辽市,华北,,,,,,,,,,,,3,3
 GW93,黑龙江,哈尔滨市,东北,1,,,,,,,,,,,,1
 GW99A,辽宁,鞍山市,东北,1,,3,,2,1,1,1,1,,,,10
 GW93,重庆,重庆市,华南,,,,2,,,,,,,,,2
 GW93,重庆,重庆市,华南,,,1,4,,,,,,,,,5
 GW99A,天津,天津市,华北,1,2,,,,,,,,,,,3
 GW93,天津,天津市,华北,,2,1,,,,,,,,,,3
 GW99A,河北,秦皇岛市,华北,,,1,1,1,5,,,,,,,8
 GW93,河北,秦皇岛市,华北,,,,1,1,,,,,,,,2
 GW93,贵州,黔西南布依族苗族自治州,华南,,,4,3,,,1,,,,1,1,10
 GW93,贵州,黔南布依族苗族自治州,华南,1,,,,,,,,,,,,1
 GW99A,河南,济源市,华东南北部,,,,2,1,,,,3,,,1,7
 GW83.4,国际,国际,国际,4,,6,4,,,,,,,,,14
 GW99A,甘肃,定西市,西北东部,,,2,2,1,,,,,1,4,3,13
 GW99,黑龙江,七台河市,东北,4,4,1,,,,,,,,,,9
 GWBD-A3,湖北,黄冈市,华东南北部,,,,,,,,,,,,2,2
 GW99A,黑龙江,绥化市,东北,,,,2,2,5,2,,,,,,11
 GW99,天津,天津市,华北,,,,,,,,,,,,1,1
 GWBD-A3,天津,天津市,华北,,,,,,,,,,,,1,1
 GWBD-A3,天津,天津市,华北,,,,,,,,,,,,1,1
 GW81,国际,国际,国际,,,,,,,8,8,4,,4,8,32
 GW99A,河南,南阳市,华东南北部,,,2,1,,,,,,,,,3
 GW99,河南,南阳市,华东南北部,2,,1,2,1,,,,,,,,6
 GW99A,安徽,阜阳市,华东南北部,,,,,,,,,2,2,2,2,8
 GW93,贵州,毕节市,华南,,,,,2,,,,,,,,2
 GW99,贵州,毕节市,华南,,,2,3,1,2,,,,,,,8
 GW83.4,黑龙江,哈尔滨市,东北,2,,2,5,1,,,,,,,,10
 GW99A,河南,开封市,华东南北部,2,,,,,,,,,,,,2
 GW99A,广西,玉林市,华南,,,,,,,,,1,,,,1
 GW99,广西,玉林市,华南,,,,,,,,,,1,2,1,4
 GW99A,甘肃,定西市,西北东部,,,1,1,6,3,1,5,1,,,,18
 GW99,河南,商丘市,华东南北部,,,,,,,,2,3,4,5,1,15
 GW99A,河南,三门峡市,华东南北部,6,,,,,,,,,,,,6
 GWBD-A2,新疆,哈密市,西北西部,,3,7,6,,6,6,5,3,3,9,,48
 GWBD-A2,新疆,塔城地区,西北西部,,,,,,,4,12,10,19,,,45
 GW93,新疆,塔城地区,西北西部,,,,,,4,14,2,1,,,,21
 GWBD-A2,新疆,塔城地区,西北西部,,,,,,4,8,8,4,,,,24
 GWBD-A2,新疆,哈密市,西北西部,,,,,,,,,,,,1,1
 GWBD-A2,新疆,哈密市,西北西部,,,,,,,,,,,,1,1
 GWBD-A2,新疆,哈密市,西北西部,,,,,,,,,,,,5,5
 GW83.4,辽宁,鞍山市,东北,,,,1,,,,,,,,,1
 GWBD-A2,新疆,昌吉回族自治州,西北西部,,,5,8,3,,,,,,,,16
 GWBD-A2,新疆,昌吉回族自治州,西北西部,,,,2,3,,,,,,,,5
 GW93,内蒙古,呼和浩特市,华北,,,,,,4,,,1,,,,5
 GW99A,广西,钦州市,华南,7,6,1,2,2,,,,,,,,18
 GW99A,黑龙江,七台河市,东北,,,,,1,4,8,10,9,,,,32
 GWBD-A2,河北,张家口市,华北,,,,,,,,,,1,4,,5
 GWBD-A2,河北,张家口市,华北,,,,,,,,,,,4,3,7
 GW99,河南,安阳市,华东南北部,1,1,3,3,2,3,3,1,,,,,17
 GW99A,河南,安阳市,华东南北部,,1,,2,3,3,1,1,1,,,,12
 GWBD-A2,甘肃,酒泉市,西北东部,,,5,4,4,6,5,6,,,,,30
 GWBD-A2,甘肃,酒泉市,西北东部,,,,,,,3,3,,,,,6
 GWBD-A2,新疆,哈密市,西北西部,,,,5,13,37,11,24,,,,,90
 GW99A,贵州,黔西南布依族苗族自治州,华南,,,,1,1,,,1,,,,3,6
 GWBD-A2,吉林,松原市,东北,,1,2,1,1,1,2,,,,,,8
 GWBD-A2,吉林,松原市,东北,3,6,7,10,20,5,5,,,,,,56
 GWBD-A2,河北,承德市,华北,2,,,,,,,,,,,,2
 GW83.4,辽宁,鞍山市,东北,,,,,1,,,,,,,,1
 GW99A,江西,赣州市,华东南南部,1,,3,3,9,,,,,,,,16
 GWBD-A3,广西,钦州市,华南,,,,,,,,,,,,4,4
 GW99A,湖北,恩施土家族苗族自治州,华东南北部,,1,3,3,1,,,,,,,,8
 GW99A,湖南,永州市,华东南南部,,,,1,,1,1,,1,1,2,2,9
 GW99A,河北,邢台市,华北,,,,,1,4,7,5,5,7,11,9,49
 GWBD-A2,河北,邢台市,华北,,,,,,,,,,,2,,2
 GW99A,重庆,重庆市,华南,,,,,,,,2,,2,1,,5
 GW99A,河南,新乡市,华东南北部,,,,2,5,2,2,2,2,,1,,16
 GWBD-A2,新疆,昌吉回族自治州,西北西部,,,,3,9,1,5,2,,,,,20
 GW99,新疆,昌吉回族自治州,西北西部,,,,3,2,4,9,2,6,1,,,27
 GWBD-A2,河北,张家口市,华北,,,,4,1,,,,,,,,5
 GWBD-A2,河北,张家口市,华北,,,,,1,,,,,,,,1
 GW99,广西,来宾市,华南,,,4,8,9,15,2,,,,,,38
 GWBD-A3,黑龙江,鹤岗市,东北,,,,,3,9,7,8,3,,,,30
 GWBD-A2,新疆,哈密市,西北西部,,,8,,15,15,10,5,32,15,,,100
 SI90.2,国际,国际,国际,,,9,,11,,5,6,8,8,11,,58
 GW81,国际,国际,国际,3,,10,10,9,,,,,,,,32
 GW81,国际,国际,国际,,8,,,,9,,,,,,,17
 SI90.2,国际,国际,国际,,,5,,,,,,,,,,5
 GW83.4,国际,国际,国际,,,,,,5,8,8,,8,4,,33
 GWBD-A2,吉林,通化市,东北,,,,,3,1,,,,,,,4
 GW99,吉林,通化市,东北,,,,,1,,,,,,,,1
 GWBD-A2,河北,秦皇岛市,华北,,,,,,,1,,,,3,4,8
 GW99A,甘肃,白银市,西北东部,4,,2,7,,,,,,,,,13
 GW99A,甘肃,白银市,西北东部,,,,12,4,,,,,,,,16
 GW81,国际,国际,国际,,,4,,,,,,,,,,4
 GWBD-A3,天津,天津市,华北,,,,,,,,,5,1,,,6
 GW93,天津,天津市,华北,,,,,,,1,1,,,,,2
 GW99,河北,承德市,华北,1,1,,,1,,,,,,,,3
 GW99,河北,承德市,华北,2,,2,2,7,,,,,,,,13
 SI122,福建,莆田市,华东南南部,,,,,,3,7,2,,,,,12
 GW130,广东,阳江市,华南,,,,,,,,,,3,3,1,7
 GW93,河北,承德市,华北,,,,,,2,1,,,,,,3
 SI90.2,河北,承德市,华北,1,,4,2,,10,3,,1,,,,21
 GW93,河北,承德市,华北,,,2,1,3,,,,,,,,6
 SI90.2,河北,承德市,华北,3,,3,,8,,,,,,,,14
 GW81,国际,国际,国际,,,,,,,2,,,,,,2
 GWBD-A3,河南,洛阳市,华东南北部,,1,2,,,,,,,,,,3
 GW99A,山西,晋中市,华北,,,,,,,,,,,2,1,3
 GW99,山西,晋中市,华北,,,,,,,,,,,2,3,5
 GWBD-A2,黑龙江,佳木斯市,东北,6,3,3,,,6,5,5,,1,3,4,36
 GWBD-A2,宁夏,吴忠市,西北东部,1,,,,,,,,,,,,1
 SI90.2,国际,国际,国际,,,,,,,,,8,10,8,,26
 SI90.2,国际,国际,国际,10,,,,,,,,,,,,10
 GW81,国际,国际,国际,,,,,,,7,,,,,,7
 GW83.3,国际,国际,国际,,,,,,,,,5,,,,5
 GW93,山西,运城市,华北,,,,,,,,,,,,4,4
 GWBD-A2,山西,朔州市,华北,,,,,,,,,,2,8,3,13
 GWBD-A2,新疆,哈密市,西北西部,,,,,,1,4,8,3,8,,,24
 GW99,新疆,伊犁哈萨克自治州,西北西部,,,12,5,8,6,6,,11,7,10,,65
 GWBD-A2,云南,昆明市,华南,,,,,,,,,,,2,2,4
 GW93,陕西,渭南市,华北,1,2,2,,,,,,,,,,5
 GWBD-A2,山西,忻州市,华北,,,,,,,,,1,3,6,3,13
 GWBD-A2,山西,大同市,华北,,,,1,,2,,2,1,3,3,,12
 GW76,国际,国际,国际,2,,,,,,,,,,,,2
 GW99,内蒙古,鄂尔多斯市,华北,,,,,,3,5,2,1,4,10,5,30
 GW76,国际,国际,国际,1,,,,,,,,,,,,1
 GWBD-A2,新疆,乌鲁木齐市,西北西部,,,,6,7,,,,,,,,13
 GW99,青海,海南藏族自治州,西北东部,,,,,,,,2,3,,,,5
 GW76,国际,国际,国际,7,,,,,,,,,,,,7
 GWBD-A3,贵州,毕节市,华南,,,,2,1,2,,1,,,,,6
 GWBD-A2,甘肃,庆阳市,西北东部,,,,,2,,,,,,,,2
 SI90.2,新疆,吐鲁番地区,西北西部,5,,,,,,,,,,,,5
 GW93,辽宁,沈阳市,东北,,,1,,,,,,,,,,1
 GWBD-A2,内蒙古,兴安盟,东北,,,,,1,3,2,2,3,4,5,,20
 GWBD-A2,内蒙古,兴安盟,东北,,,,,,,,,3,,1,,4
 GWBD-A2,内蒙古,兴安盟,东北,,,,1,8,6,1,3,,,,,19
 GWBD-A2,内蒙古,兴安盟,东北,,,,,,4,,,,,,,4
 GWBD-A2,辽宁,锦州市,东北,,,,,,1,,,,,,,1
 GW76,国际,国际,国际,,,,4,,,,,,,,,4
 GW86,国际,国际,国际,,8,,,,,,,,,,,8
 GW76,国际,国际,国际,3,,,,,,,,,,,,3
 GW81,国际,国际,国际,,,,5,,,,,,,,,5
 GW76,国际,国际,国际,7,,,,,,,,,,,,7
 GW76,国际,国际,国际,,,,7,,,,,,,,,7
 SI90.2,国际,国际,国际,,,,7,10,,10,5,,,,,32
 SI90.2,国际,国际,国际,,7,16,10,,,,15,12,,,,60
 GW83.4,国际,国际,国际,14,7,,,,7,6,,,,,,34
 GW76,国际,国际,国际,,,,,,4,,,,,,,4
 GW81,国际,国际,国际,,,,,,,1,,,,,,1
 GW130,广东,阳江市,华南,,,,,,,,,,5,10,10,25
 GW81,国际,国际,国际,,,,,,10,,5,,10,,,25
 GWBD-A2,新疆,哈密市,西北西部,,,,,,,8,7,2,6,5,15,43
 SI90.2,新疆,吐鲁番地区,西北西部,,,,,1,4,7,12,3,15,14,,56
 GW99A,甘肃,酒泉市,西北东部,,,,,,,,,,3,9,3,15
 GWBD-A2,内蒙古,通辽市,华北,2,2,5,4,4,,,,,,,,17
 GW99A,河北,邢台市,华北,,,,1,3,2,3,5,1,1,,,16
 GW99,重庆,重庆市,华南,,,,,,,,,,3,,2,5
 GWBD-A2,重庆,重庆市,华南,,,,,,,,,1,4,2,,7
 GWBD-A2,新疆,昌吉回族自治州,西北西部,,,,,,,2,5,2,5,4,,18
 GWBD-A2,内蒙古,锡林郭勒盟,华北,,,4,4,,,,,,,,,8
 GWBD-A3,山东,聊城市,华东南北部,,,,,,,1,2,4,5,8,,20
 GWBD-A2,新疆,巴音郭楞蒙古自治州,西北西部,1,,,,,,,,,,,,1
 GWBD-A2,新疆,塔城地区,西北西部,,,,,,3,8,12,7,7,13,,50
 GW93,天津,天津市,华北,,,1,7,2,,,,,,,,10
 GWBD-A3,广西,梧州市,华南,,,,1,3,6,2,8,8,,,,28
 SI90.2,国际,国际,国际,,,,,9,,,,9,,,7,25
 GWBD-A2,新疆,乌鲁木齐市,西北西部,,,,,,,2,3,6,6,3,4,24
 SI90.2,国际,国际,国际,,,,,,,,,,,2,,2
 GW93,吉林,白城市,东北,,,,,,,,,,,,2,2
 GW110.5,江苏,盐城市,华东南北部,,,,,1,,6,6,,,,,13
 SI122,江苏,盐城市,华东南北部,,,,,,,1,,,,,,1
 GWBD-A2,内蒙古,乌兰察布市,华北,,2,3,5,6,2,8,,,,,,26
 GWBD-A2,新疆,哈密市,西北西部,,,2,16,11,6,12,8,30,25,,,110
 GW99,广西,河池市,华南,,,,,,,,,2,1,,,3
 GW99A,广西,河池市,华南,,,,,,,,,2,3,,1,6
 GWBD-A3,宁夏,吴忠市,西北东部,,,,,,,,1,3,,2,,6
 GWBD-A3,新疆,昌吉回族自治州,西北西部,,,5,7,11,10,10,2,7,7,1,,60
 GW99,内蒙古,锡林郭勒盟,华北,,,,,,,,7,,,,,7
 GW99,内蒙古,锡林郭勒盟,华北,,,,,,,3,,,,,,3
 GW99,内蒙古,锡林郭勒盟,华北,,,,,,,,5,12,,,,17
 GW99,内蒙古,锡林郭勒盟,华北,,,,,,,,,3,10,3,1,17
 GWBD-A2,山东,济南市,华东南北部,,,,,,,1,,,,,,1
 GWBD-A3,山东,济南市,华东南北部,,,2,5,3,2,2,6,7,1,4,,32
 GWBD-A2,山东,济南市,华东南北部,,,,,,,5,3,,,,,8
 GWBD-A2,山东,济南市,华东南北部,,,,1,2,,,,,,,,3
 GWBD-A3,山东,济南市,华东南北部,,,2,3,1,,,,,,,,6
 GWBD-A2,内蒙古,锡林郭勒盟,华北,,,,1,2,1,1,,,,,,5
 GW99,云南,昆明市,华南,,,,,,,,,,,1,2,3
 GWBD-A2,新疆,新疆生产建设兵团第十师,西北西部,,,,,1,7,1,1,,,,,10
 GWBD-A2,新疆,阿勒泰地区,西北西部,,,5,16,21,,,1,2,,,,45
 GWBD-A3,广西,百色市,华南,,,,,,,,,,,6,6,12
 GW99,新疆,昌吉回族自治州,西北西部,,,10,12,,,,,,,,,22
 GWBD-A3,新疆,昌吉回族自治州,西北西部,,,,2,,,,,,,,,2
 GWBD-A2,内蒙古,兴安盟,东北,,,,,,,,,,,3,4,7
 GWBD-A2,内蒙古,兴安盟,东北,,,,,,,,,,,3,3,6
 GWBD-A3,新疆,昌吉回族自治州,西北西部,,,,,2,8,9,13,14,20,9,,75
 GW99,河北,张家口市,华北,,,,,,,,,,,5,3,8
 GWBD-A3,湖北,荆州市,华东南北部,,,,,,,,,,,,2,2
 GW99A,河北,衡水市,华北,,,,,,,,,2,3,3,2,10
 GWBD-A3,河北,邢台市,华北,,,,,,,,,1,4,4,3,12
 GW99A,陕西,延安市,华北,,,,2,2,,,,,,,,4
 GW81,国际,国际,国际,,,,,,9,11,9,11,,11,,51
 SI90.2,国际,国际,国际,,,,,,,,,9,,,9,18
 GWBD-A2,内蒙古,锡林郭勒盟,华北,,,,,,,,,1,3,8,,12
 SI90.2,新疆,塔城地区,西北西部,,,,,,,,,4,,,,4
 GW99A,湖北,荆门市,华东南北部,,,,,,2,4,4,4,3,1,,18
 GWBD-A2,内蒙古,锡林郭勒盟,华北,,,,,,,1,5,12,9,3,,30
 GWBD-A2,内蒙古,通辽市,华北,,,,,,3,1,1,,,1,,6
 GWBD-A2,内蒙古,通辽市,华北,,,,,,,7,1,,,,,8
 GWBD-A3,山西,朔州市,华北,,,3,,,,,,,,,,3
 GWBD-A2,宁夏,中卫市,西北东部,,,,,,1,5,7,5,,,,18
 GWBD-A3,宁夏,中卫市,西北东部,,,,,,,4,4,7,,,,15
 GWBD-A2,宁夏,中卫市,西北东部,,,,,,,,2,3,,,,5
 GWBD-A3,宁夏,中卫市,西北东部,,,,,,,,1,2,,,,3
 GWBD-A2,新疆,哈密市,西北西部,,,,,,,,,,,4,,4
 GW99A,河北,秦皇岛市,华北,,,,,,,,1,4,2,3,,10
 GW93,辽宁,沈阳市,东北,,,,,1,,,,,,,,1
 GW81,国际,国际,国际,,,,,,,3,,,,,,3
 SI90.2,国际,国际,国际,,,,,,,6,5,3,3,,,17
 SI90.2,国际,国际,国际,,,,1,,,,,,,,,1
 GWBD-A2,新疆,塔城地区,西北西部,,,,4,8,8,13,8,13,13,,,67
 GW93,陕西,榆林市,华北,,,2,,,,,,,,,,2
 SI90.2,国际,国际,国际,,,,,,,,,9,,,,9
 GWBD-A3,河北,邯郸市,华北,,,,1,1,2,,,,,,,4
 GWBD-A2,河北,邯郸市,华北,,,,1,1,,1,,,,,,3
 SI90.2,国际,国际,国际,,,,,,6,,6,6,4,,,22
 GWBD-A3,宁夏,吴忠市,西北东部,,,,,,,,,2,1,2,,5
 GWBD-A3,宁夏,吴忠市,西北东部,,,,,,,,,,2,,,2
 GW99A,河北,沧州市,华北,,,,,1,3,1,2,1,,,,8
 GW99A,天津,天津市,华北,,,,1,4,,,,,,,,5
 GWBD-A3,宁夏,中卫市,西北东部,,,,,,,,,,,6,4,10
 GW83.4,国际,国际,国际,,,,7,,,,,,,,,7
 GW76,国际,国际,国际,,,,,,,10,,,,,,10
 SI90.2,国际,国际,国际,,,,,,,,,,,1,,1
 GWBD-A3,河北,邢台市,华北,,,,,,,,,4,4,4,,12
 GWBD-A2,新疆,昌吉回族自治州,西北西部,,,,,,,1,6,10,4,21,,42
 GWBD-A2,新疆,昌吉回族自治州,西北西部,,,,,,,,3,11,11,20,,45
 GWBD-A2,内蒙古,通辽市,华北,,,,,,,,,,2,11,3,16
 GWBD-A2,内蒙古,通辽市,华北,,,,,,,,,,,2,2,4
 GWBD-A2,内蒙古,通辽市,华北,,,,,,,,,2,2,1,,5
 GWBD-A2,内蒙古,通辽市,华北,,,,,,,,,1,,,,1
 GWBD-A2,内蒙古,通辽市,华北,,,,,,,,,4,5,6,5,20
 GWBD-A2,内蒙古,通辽市,华北,,,,,,,,,,,2,2,4
 GW99,新疆,乌鲁木齐市,西北西部,,,,2,,,,,,,,,2
 GW99,新疆,乌鲁木齐市,西北西部,,,,3,,,,,,,,,3
 GWBD-A3,河北,衡水市,华北,,,,1,2,3,2,,,,,,8
 GWBD-A2,新疆,塔城地区,西北西部,,,,,,,10,8,16,11,5,,50
 GWBD-A2,黑龙江,绥化市,东北,,,,,,,,,,,3,2,5
 GW93,天津,天津市,华北,,,,,4,,,,,,,,4
 GWBD-A2,内蒙古,兴安盟,东北,,,,,,,,,,1,13,11,25
 GWBD-A2,新疆,乌鲁木齐市,西北西部,,,,,,,,,,,2,3,5
 GWBD-A3,山西,大同市,华北,,,,,,,,3,6,10,5,,24
 GWBD-D,辽宁,朝阳市,东北,,,,,,,,,,,1,,1
 GW99A,辽宁,辽阳市,东北,,,,,,,,,,,,2,2
 GWBD-A2,辽宁,锦州市,东北,,,,,,,,,,,1,,1
 GW99,河北,张家口市,华北,,,,,,,,,,,1,,1
 GWBD-A3,河北,张家口市,华北,,,,,,,,,,,1,,1
 GW83.4,国际,国际,国际,,,,,,3,,,,,,,3
 GW93,内蒙古,锡林郭勒盟,华北,,,,2,,,,,,,,,2
 GWBD-A3,山东,德州市,华东南北部,,,,,,,,,,,,4,4
 GWBD-A3,河北,邢台市,华北,,,,,,1,,,,5,5,5,16
 GWBD-A3,河北,邢台市,华北,,,,,,,,,,,4,4,8
 GWBD-A3,河北,邢台市,华北,,,,,,,,,,,2,3,5
 GWBD-A3,河北,邢台市,华北,,,,,,,,,,,,1,1
 GWBD-D,陕西,渭南市,华北,,,,,,,,1,2,,1,,4
 GW83.4,江苏,镇江市,华东南北部,,,,,,,,,1,,,,1
 GW99A,湖北,恩施土家族苗族自治州,华东南北部,,,,,,,,1,4,3,3,2,13
 GW99A,山东,聊城市,华东南北部,,,,,,,,,,,5,5,10
 GW81,国际,国际,国际,,,,,,,1,,,,,,1
 GWBD-A3,河北,衡水市,华北,,,,,,,,,2,3,6,8,19
 GWBD-A3,河北,衡水市,华北,,,,,,,4,3,4,3,2,,16
 GWBD-A2,新疆,乌鲁木齐市,西北西部,,,,,6,,,,,,,,6
 GW99A,天津,天津市,华北,,,,,,,,,4,2,,,6
 GW99,天津,天津市,华北,,,,,,,,,,1,,,1
 GW93,内蒙古,兴安盟,东北,,,,,,,4,3,3,,,,10
 GW99A,河北,沧州市,华北,,,,,,,,,,,,7,7
 GWBD-D,河北,张家口市,华北,,,,,,,,,,2,,,2
 SI90.2,国际,国际,国际,,,,,,,,,,,,10,10
 GWBD-A3,河北,沧州市,华北,,,,,1,2,5,,,,,,8
 GWBD-A3,河北,邢台市,华北,,,,,,,,,,,2,4,6
 GWBD-A2,新疆,哈密市,西北西部,,,,,,,,,,,2,4,6
 GW81,国际,国际,国际,,,,,,,,,1,,,,1
 GW83.4,国际,国际,国际,,,,,,,,,,,1,,1
 GW99,新疆,乌鲁木齐市,西北西部,,,,,,,,,4,4,3,,11
 GWBD-A2,新疆,阿勒泰地区,西北西部,,,,,,,,1,,6,18,,25
 GWBD-A2,新疆,阿勒泰地区,西北西部,,,,,,,,1,,7,24,8,40
 SI90.2,国际,国际,国际,,,,,,,,,,,1,,1
 GWBD-A2,内蒙古,鄂尔多斯市,华北,,,,,,,,,,,1,,1
 GW99A,河北,张家口市,华北,,,,,,,,,1,1,,,2
 GWBD-D,辽宁,本溪市,东北,,,,,,,,,,1,,,1
 GW99,云南,曲靖市,华南,,,,,,,,,,,6,10,16
 GW93,新疆,巴音郭楞蒙古自治州,西北西部,,,,,,,,,,,,1,1
 GW99,甘肃,酒泉市,西北东部,,,,,,,,,,,1,,1
 GW93,山东,济宁市,华东南北部,,,,,,,,,,,,2,2
 GW99,新疆,巴音郭楞蒙古自治州,西北西部,,,,,1,,,,,,,,1
 GWBD-A3,甘肃,庆阳市,西北东部,,,,,,,,,,,,2,2
 GW93,天津,天津市,华北,,,,,,,,,,,,1,1
 GWBD-A2,黑龙江,哈尔滨市,东北,,,,,,,,,,,,2,2
 GW83.4,国际,国际,国际,,,,,,,,,,,1,,1
 GW99A,贵州,铜仁市,华南,,,,,,,,,,,2,2,4
 GWBD-A2,河北,张家口市,华北,,,,,,,,,,,,2,2
 GWBD-A2,河北,张家口市,华北,,,,,,,,,,,,10,10
 GWBD-A2,辽宁,朝阳市,东北,,,,,,,,,,,1,,1
 SI90.2,新疆,昌吉回族自治州,西北西部,,,,,,,,,,,1,,1
 GWBD-A2,新疆,昌吉回族自治州,西北西部,,,,,,,,,1,,,,1
 GW83.3,新疆,昌吉回族自治州,西北西部,,,,,,,,,1,,,,1
 GWBD-A2,青海,海西蒙古族藏族自治州,西北东部,,,,,,,,,,,,4,4
 GWBD-A2,山东,烟台市,华东南北部,,,,,,,,,,,,1,1
 GW99,内蒙古,通辽市,华北,,,,,,,,,,,,1,1
 GW99,内蒙古,锡林郭勒盟,华北,,,,,,,,,,,,1,1
--- a/data/distance_matrix.csv
+++ b/data/distance_matrix.csv
--- a/data/factory_mapping.json
+++ b/data/factory_mapping.json
@ -0,0 +1,29 @@
 {
  "艾郎玉门": "AilangYumen",
  "艾郎张北": "AilangZhangbei",
  "时代巴里坤": "ShidaiBalikun",
  "时代百色": "ShidaiBaise",
  "时代射阳": "ShidaiSheyang",
  "时代松原": "ShidaiSongyuan",
  "时代株洲": "ShidaiZhuzhou",
  "双瑞大丰": "ShuangruiDafeng",
  "双瑞东营": "ShuangruiDongying",
  "天顺濮阳": "TianshunPuyang",
  "天顺沙洋": "TianshunShayang",
  "中材白城": "ZhongcaiBaicheng",
  "中材阜宁": "ZhongcaiFuning",
  "中材哈密": "ZhongcaiHami",
  "中材邯郸": "ZhongcaiHandan",
  "中材酒泉": "ZhongcaiJiuquan",
  "中材连云港": "ZhongcaiLianyungang",
  "中材萍乡": "ZhongcaiPingxiang",
  "中材锡林": "ZhongcaiXilin",
  "中材兴安盟": "ZhongcaiXinganmeng",
  "中材阳江": "ZhongcaiYangjiang",
  "中材伊吾": "ZhongcaiYiwu",
  "中材玉溪": "ZhongcaiYuxi",
  "重通昌吉": "ChongtongChangji",
  "重通大安": "ChongtongDaan",
  "重通如东": "ChongtongRudong",
  "重通武威": "ChongtongWuwei"
 }
--- a/data/ga_best_params.json
+++ b/data/ga_best_params.json
@ -0,0 +1,34 @@
 {
  "best_score": 0.013780913115116374,
  "month1": 235.2024236560018,
  "month2": 80.13430957369465,
  "month3": 40.81815120444523,
  "month4": 35.573307322844045,
  "factor_AilangYumen": 1.1209670325377996,
  "factor_AilangZhangbei": 1.0869751796074336,
  "factor_ShidaiBalikun": 1.064344356385733,
  "factor_ShidaiBaise": 0.925644739081089,
  "factor_ShidaiSheyang": 2.2354053357800834,
  "factor_ShidaiSongyuan": 0.8850280359740502,
  "factor_ShidaiZhuzhou": 0.8,
  "factor_ShuangruiDafeng": 1.3485391587995974,
  "factor_ShuangruiDongying": 1.4720254802974653,
  "factor_TianshunPuyang": 1.1166692701637255,
  "factor_TianshunShayang": 1.060367420503057,
  "factor_ZhongcaiBaicheng": 1.8461095046937892,
  "factor_ZhongcaiFuning": 1.3124938381864457,
  "factor_ZhongcaiHami": 1.4843503931169255,
  "factor_ZhongcaiHandan": 1.210604184057787,
  "factor_ZhongcaiJiuquan": 0.9798174978126551,
  "factor_ZhongcaiLianyungang": 1.6036280376162886,
  "factor_ZhongcaiPingxiang": 1.132040522281658,
  "factor_ZhongcaiXilin": 1.0297971770720127,
  "factor_ZhongcaiXinganmeng": 0.9769716839603864,
  "factor_ZhongcaiYangjiang": 2.386768108574698,
  "factor_ZhongcaiYiwu": 1.2417395146436796,
  "factor_ZhongcaiYuxi": 1.7093165019081604,
  "factor_ChongtongChangji": 1.0895870479908367,
  "factor_ChongtongDaan": 1.0187316107483142,
  "factor_ChongtongRudong": 1.0270541553649646,
  "factor_ChongtongWuwei": 0.8811374580269447
 }
--- a/data/ga_two_best_params.json
+++ b/data/ga_two_best_params.json
@ -0,0 +1,5 @@
 {
  "best_score": 0.013780913115116374,
  "factor_ZhongcaiBaicheng": 1.8461095046937892,
  "factor_ZhongcaiHami": 1.4843503931169255
 }
--- a/data/model_params.json
+++ b/data/model_params.json
@ -0,0 +1,62 @@
 {
  "holiday_days_1": 5,
  "holiday_days_2": 12,
  "holiday_days_3": 2,
  "holiday_days_4": 2,
  "holiday_days_5": 2,
  "holiday_days_6": 2,
  "holiday_days_7": 2,
  "holiday_days_8": 2,
  "holiday_days_9": 2,
  "holiday_days_10": 7,
  "holiday_days_11": 2,
  "holiday_days_12": 2,
  "month1": 235.2024236560018,
  "month2": 80.13430957369465,
  "month3": 40.81815120444523,
  "month4": 35.573307322844045,
  "factor_AilangYumen": 1.1209670325377996,
  "factor_AilangZhangbei": 1.0869751796074336,
  "factor_ShidaiBalikun": 1.064344356385733,
  "factor_ShidaiBaise": 0.925644739081089,
  "factor_ShidaiSheyang": 2.2354053357800834,
  "factor_ShidaiSongyuan": 0.8850280359740502,
  "factor_ShidaiZhuzhou": 0.8,
  "factor_ShuangruiDafeng": 1.3485391587995974,
  "factor_ShuangruiDongying": 1.4720254802974653,
  "factor_TianshunPuyang": 1.1166692701637255,
  "factor_TianshunShayang": 1.060367420503057,
  "factor_ZhongcaiBaicheng": 1.8461095046937892,
  "factor_ZhongcaiFuning": 1.3124938381864457,
  "factor_ZhongcaiHami": 1.4843503931169255,
  "factor_ZhongcaiHandan": 1.210604184057787,
  "factor_ZhongcaiJiuquan": 0.9798174978126551,
  "factor_ZhongcaiLianyungang": 1.6036280376162886,
  "factor_ZhongcaiPingxiang": 1.132040522281658,
  "factor_ZhongcaiXilin": 1.0297971770720127,
  "factor_ZhongcaiXinganmeng": 0.9769716839603864,
  "factor_ZhongcaiYangjiang": 2.386768108574698,
  "factor_ZhongcaiYiwu": 1.2417395146436796,
  "factor_ZhongcaiYuxi": 1.7093165019081604,
  "factor_ChongtongChangji": 1.0895870479908367,
  "factor_ChongtongDaan": 1.0187316107483142,
  "factor_ChongtongRudong": 1.0270541553649646,
  "factor_ChongtongWuwei": 0.8811374580269447,
  "product_set": [
    "GWBD-A2",
    "GWBD-A3",
    "SI90.2",
    "GW99A",
    "GW110.5",
    "GW93",
    "GW130",
    "GW76",
    "GW99",
    "GW81",
    "GW86",
    "GWBD-D",
    "SI122",
    "GW83.3",
    "GW83.4"
  ]
 }
--- a/data/month_hours.json
+++ b/data/month_hours.json
@ -0,0 +1,14 @@
 {
  "1": 31,
  "2": 28,
  "3": 31,
  "4": 30,
  "5": 31,
  "6": 30,
  "7": 31,
  "8": 31,
  "9": 30,
  "10": 31,
  "11": 30,
  "12": 31
 }
--- a/data/product.csv
+++ b/data/product.csv
@ -0,0 +1,23 @@
 平台,标准机型,叶片型号
 GWH V20,GWH266-16.2,GW130
 4S,155/4500,GW76
 5S,165/5200,GW81
 GWH V15,GWH170-7.2,GW83.3
 GWH V11,171/5300,GW83.4
 GWH V15,175/7800,GW86
 GWH V12,204/6250H,GW99
 GWH V12,204/6250H,GW99A
 GWH V12,204/5600H,GW99A
 GWH V17,GWH221-7,GWBD-A2
 GWH V17,GWH221-8,GWBD-A2
 GWH V17,GWH221-8.34,GWBD-A2
 GWH V17,GWH221-11.1,GWBD-A2
 GWH V17,GWH221-6,GWBD-A3
 GWH V17,GWH221-6.7,GWBD-A3
 GWH V19,GWH221-6,GWBD-A3
 GWH V12,GWH204-5.0,GWBD-D
 GWH V20,252/13600,SI122
 GWH V12,182/7200H,SI90.2
 GWH V12,191/4550H,GW93
 GWH V12,191/6700H,GW93
 GW110.5,GW110.5,GW110.5
--- a/data/transportation_price.csv
+++ b/data/transportation_price.csv
@ -0,0 +1,24 @@
 产品型号,里程区间[公里], 平均单价[元/(公里*片)] , 平均整车价[元/片] 
 others,0-99,," 12,859.46 "
 others,100-199,," 14,946.73 "
 others,200-299,," 18,662.46 "
 others,300-599, 52.83 ,
 others,600-899, 38.36 ,
 others,900-1199, 32.89 ,
 others,1200-1499, 30.69 ,
 others,1500-1999, 28.49 ,
 others,2000-2499, 27.76 ,
 others,2500-3499, 22.87 ,
 others,3500-10000000, 21.02 ,
 "GWBD-A2, GWBD-A3, GWBD-B",0-99,," 15,802.31 "
 "GWBD-A2, GWBD-A3, GWBD-B",100-199,," 18,445.12 "
 "GWBD-A2, GWBD-A3, GWBD-B",200-299,," 22,391.35 "
 "GWBD-A2, GWBD-A3, GWBD-B",300-599, 62.29 ,
 "GWBD-A2, GWBD-A3, GWBD-B",600-899, 44.75 ,
 "GWBD-A2, GWBD-A3, GWBD-B",900-1199, 38.23 ,
 "GWBD-A2, GWBD-A3, GWBD-B",1200-1499, 36.08 ,
 "GWBD-A2, GWBD-A3, GWBD-B",1500-1999, 33.44 ,
 "GWBD-A2, GWBD-A3, GWBD-B",2000-2499, 32.76 ,
 "GWBD-A2, GWBD-A3, GWBD-B",2500-3499, 26.68 ,
 "GWBD-A2, GWBD-A3, GWBD-B",3500-10000000, 24.27 ,
 ,,,
--- a/demand_agent.py
+++ b/demand_agent.py
@ -0,0 +1,22 @@
 from mesa.agent import Agent
 class DemandAgent(Agent):
    """Demand agent holding monthly demand per city and product."""
    def __init__(self, model, city, region, product_monthly_demand):
        super().__init__(model)
        self.city = city
        self.region = region
        # product_monthly_demand: {product: {month: demand_units}}
        self.product_monthly_demand = product_monthly_demand
        self.fulfilled = 0.0
        self.total_demand = sum(sum(m.values()) for m in product_monthly_demand.values())
    def step(self):
        month = self.model.current_month
        total = 0
        for product, monthly in self.product_monthly_demand.items():
            total += monthly.get(month, 0)
        if total > 0:
            self.model.record_demand(self.region, total)
--- a/environment.yml
+++ b/environment.yml
@ -0,0 +1,103 @@
 name: gw
 channels:
  - defaults
  - conda-forge
 dependencies:
  - python=3.13.9
  - pip
  - pip:
      - altair==6.0.0
      - anyio==4.11.0
      - asttokens==3.0.1
      - attrs==25.4.0
      - cachetools==6.2.2
      - certifi==2025.11.12
      - chardet==5.2.0
      - charset-normalizer==3.4.4
      - click==8.3.1
      - colorama==0.4.6
      - comm==0.2.3
      - contourpy==1.3.3
      - cycler==0.12.1
      - debugpy==1.8.17
      - decorator==5.2.1
      - et_xmlfile==2.0.0
      - executing==2.2.1
      - fastjsonschema==2.21.2
      - filelock==3.20.0
      - fonttools==4.60.1
      - gurobipy==13.0.0
      - h11==0.16.0
      - humanize==4.14.0
      - idna==3.11
      - ipykernel==7.1.0
      - ipython==9.7.0
      - ipython_pygments_lexers==1.1.1
      - ipyvue==1.11.3
      - ipyvuetify==1.11.3
      - ipywidgets==8.1.8
      - jedi==0.19.2
      - Jinja2==3.1.6
      - jsonschema==4.25.1
      - jsonschema-specifications==2025.9.1
      - jupyter_client==8.6.3
      - jupyter_core==5.9.1
      - jupyterlab_widgets==3.0.16
      - kiwisolver==1.4.9
      - Markdown==3.10
      - markdown-it-py==4.0.0
      - MarkupSafe==3.0.3
      - matplotlib==3.10.7
      - matplotlib-inline==0.2.1
      - mdurl==0.1.2
      - Mesa==3.3.1
      - narwhals==2.12.0
      - nbformat==5.10.4
      - nest-asyncio==1.6.0
      - networkx==3.5
      - numpy==2.3.5
      - openpyxl==3.1.5
      - packaging==25.0
      - pandas==2.3.3
      - parso==0.8.5
      - pillow==12.0.0
      - platformdirs==4.5.0
      - prompt_toolkit==3.0.52
      - psutil==7.1.3
      - PuLP==3.3.0
      - pure_eval==0.2.3
      - Pygments==2.19.2
      - pymdown-extensions==10.17.1
      - pyparsing==3.2.5
      - python-dateutil==2.9.0.post0
      - pytz==2025.2
      - PyYAML==6.0.3
      - pyzmq==27.1.0
      - reacton==1.9.1
      - referencing==0.37.0
      - requests==2.32.5
      - rich==14.2.0
      - rich-click==1.9.4
      - rpds-py==0.29.0
      - scipy==1.16.3
      - setuptools==80.9.0
      - six==1.17.0
      - sniffio==1.3.1
      - solara==1.54.0
      - solara-server==1.54.0
      - solara-ui==1.54.0
      - stack-data==0.6.3
      - starlette==0.50.0
      - tornado==6.5.2
      - tqdm==4.67.1
      - traitlets==5.14.3
      - typing_extensions==4.15.0
      - tzdata==2025.2
      - urllib3==2.5.0
      - uvicorn==0.38.0
      - watchdog==6.0.0
      - watchfiles==1.1.1
      - wcwidth==0.2.14
      - websockets==15.0.1
      - wheel==0.45.1
      - widgetsnbextension==4.0.15
--- a/ga_two_factor_calibration.py
+++ b/ga_two_factor_calibration.py
@ -0,0 +1,117 @@
 import json
 import random
 from pathlib import Path
 from typing import List, Tuple
 from simulation_model import SimulationModel
 # Decision variables: two factory factors only
 TARGET_FACTORIES = ["ZhongcaiBaicheng", "ZhongcaiHami"]
 # Bounds
 FACTOR_BOUNDS = (0.8, 3.0)
 POP_SIZE = 20
 GENERATIONS = 50
 MUTATION_RATE = 0.2
 MUTATION_STD = 0.05  # for factors
 def clip(val: float, bounds: Tuple[float, float]) -> float:
    lo, hi = bounds
    return max(lo, min(hi, val))
 def evaluate(genes: List[float]) -> float:
    factory_factors = {fid: val for fid, val in zip(TARGET_FACTORIES, genes)}
    model = SimulationModel(factory_factors=factory_factors, output_enabled=False)
    while model.running:
        model.step()
    return model.mean_abs_error
 def mutate(genes: List[float]) -> List[float]:
    new = genes.copy()
    for i in range(len(new)):
        if random.random() < MUTATION_RATE:
            jitter = random.gauss(0, MUTATION_STD)
            new[i] = clip(new[i] + jitter, FACTOR_BOUNDS)
    return new
 def crossover(p1: List[float], p2: List[float]) -> Tuple[List[float], List[float]]:
    point = random.randint(1, len(p1) - 1)
    c1 = p1[:point] + p2[point:]
    c2 = p2[:point] + p1[point:]
    return c1, c2
 def init_population() -> List[List[float]]:
    pop = []
    best_path = Path("data") / "ga_two_best_params.json"
    seed_indiv = None
    if best_path.exists():
        try:
            best = json.loads(best_path.read_text(encoding="utf-8"))
            seed_indiv = [float(best.get(f"factor_{fid}", random.uniform(*FACTOR_BOUNDS))) for fid in TARGET_FACTORIES]
        except Exception:
            seed_indiv = None
    for idx in range(POP_SIZE):
        if seed_indiv is not None and idx == 0:
            pop.append(seed_indiv)
            continue
        indiv = [random.uniform(*FACTOR_BOUNDS) for _ in TARGET_FACTORIES]
        pop.append(indiv)
    return pop
 def main():
    best_genes = None
    best_score = float("inf")
    population = init_population()
    for gen in range(GENERATIONS):
        scored = []
        for indiv in population:
            score = evaluate(indiv)
            scored.append((score, indiv))
            if score < best_score:
                best_score = score
                best_genes = indiv
        scored.sort(key=lambda x: x[0])
        next_pop = [scored[0][1], scored[1][1]]
        while len(next_pop) < POP_SIZE:
            parents = random.sample(scored[:max(3, len(scored))], 2)
            c1, c2 = crossover(parents[0][1], parents[1][1])
            next_pop.append(mutate(c1))
            if len(next_pop) < POP_SIZE:
                next_pop.append(mutate(c2))
        population = next_pop
        print(f"Generation {gen+1}: best={best_score:.4f}")
    result = {}
    for fid, val in zip(TARGET_FACTORIES, best_genes):
        result[f"factor_{fid}"] = val
    data_path = Path("data") / "ga_two_best_params.json"
    prev_score = float("inf")
    if data_path.exists():
        try:
            prev = json.loads(data_path.read_text(encoding="utf-8"))
            prev_score = float(prev.get("best_score", float("inf")))
        except Exception:
            prev_score = float("inf")
    if best_score < prev_score:
        data_path.write_text(
            json.dumps({"best_score": best_score, **result}, ensure_ascii=False, indent=2),
            encoding="utf-8",
        )
        print(f"New best mean abs error: {best_score:.4f}, saved to {data_path}")
    else:
        print(f"Best mean abs error: {best_score:.4f} (not better than {prev_score:.4f}, not saved)")
 if __name__ == "__main__":
    main()
--- a/genetic_calibration.py
+++ b/genetic_calibration.py
@ -0,0 +1,167 @@
 import json
 import random
 from pathlib import Path
 from typing import List, Tuple
 from simulation_model import SimulationModel
 # Decision variables:
 #  - month1, month2, month3, month4 efficiencies (hour/blade)
 #  - factory-specific new-factory factors (from data/factory_mapping.json)
 FACTORY_MAPPING = json.loads(Path("data/factory_mapping.json").read_text(encoding="utf-8"))
 FACTORY_IDS = list(FACTORY_MAPPING.values())
 # Bounds for genes: (min, max)
 MONTH_BOUNDS = (30.0, 250.0)
 FACTOR_BOUNDS = (0.8, 3.0)
 POP_SIZE = 20
 GENERATIONS = 200
 MUTATION_RATE = 0.2
 MUTATION_STD = 5.0  # hours for months; factors mutate separately
 def clip(val: float, bounds: Tuple[float, float]) -> float:
    lo, hi = bounds
    return max(lo, min(hi, val))
 def evaluate(genes: List[float]) -> float:
    month1, month2, month3, month4 = genes[:4]
    factor_genes = genes[4:]
    factory_factors = {fid: val for fid, val in zip(FACTORY_IDS, factor_genes)}
    try:
        model = SimulationModel(
            month1=month1,
            month2=month2,
            month3=month3,
            month4=month4,
            factory_factors=factory_factors,
            output_enabled=False,
        )
        while model.running:
            model.step()
        return model.mean_abs_error
    except PermissionError as e:
        print(f"文件访问冲突: {e}. 正在重试...")
        return float('inf')  # 返回高值以惩罚该个体
    except Exception as e:
        print(f"发生错误: {e}")
        return float('inf')
 def mutate(genes: List[float]) -> List[float]:
    new = genes.copy()
    for i in range(len(new)):
        if random.random() < MUTATION_RATE:
            if i < 4:
                new[i] = clip(new[i] + random.gauss(0, MUTATION_STD), MONTH_BOUNDS)
            else:
                jitter = random.gauss(0, 0.05)
                new[i] = clip(new[i] + jitter, FACTOR_BOUNDS)
    return new
 def crossover(p1: List[float], p2: List[float]) -> Tuple[List[float], List[float]]:
    point = random.randint(1, len(p1) - 1)
    c1 = p1[:point] + p2[point:]
    c2 = p2[:point] + p1[point:]
    return c1, c2
 def init_population() -> List[List[float]]:
    pop = []
    # Warm start from best params if available
    best_path = Path("data") / "ga_best_params.json"
    seed_indiv = None
    if best_path.exists():
        try:
            best = json.loads(best_path.read_text(encoding="utf-8"))
            seed_indiv = [
                float(best.get("month1", random.uniform(*MONTH_BOUNDS))),
                float(best.get("month2", random.uniform(*MONTH_BOUNDS))),
                float(best.get("month3", random.uniform(*MONTH_BOUNDS))),
                float(best.get("month4", random.uniform(*MONTH_BOUNDS))),
            ]
            for fid in FACTORY_IDS:
                seed_indiv.append(float(best.get(f"factor_{fid}", random.uniform(*FACTOR_BOUNDS))))
        except Exception:
            seed_indiv = None
    for _ in range(POP_SIZE):
        if seed_indiv is not None and _ == 0:
            indiv = seed_indiv
        else:
            indiv = [
                random.uniform(*MONTH_BOUNDS),
                random.uniform(*MONTH_BOUNDS),
                random.uniform(*MONTH_BOUNDS),
                random.uniform(*MONTH_BOUNDS),
            ]
            indiv += [random.uniform(*FACTOR_BOUNDS) for _ in FACTORY_IDS]
        pop.append(indiv)
    return pop
 def main():
    best_genes = None
    best_score = float("inf")
    population = init_population()
    for gen in range(GENERATIONS):
        scored = []
        for indiv in population:
            score = evaluate(indiv)
            scored.append((score, indiv))
            if score < best_score:
                best_score = score
                best_genes = indiv
        scored.sort(key=lambda x: x[0])
        # Elitism: keep top 2
        next_pop = [scored[0][1], scored[1][1]]
        # Fill rest via crossover + mutation
        while len(next_pop) < POP_SIZE:
            parents = random.sample(scored[:10], 2)
            c1, c2 = crossover(parents[0][1], parents[1][1])
            next_pop.append(mutate(c1))
            if len(next_pop) < POP_SIZE:
                next_pop.append(mutate(c2))
        population = next_pop
        print(f"Generation {gen+1}: best={best_score:.4f}")
    # Save best parameters
    result = {
        "month1": best_genes[0],
        "month2": best_genes[1],
        "month3": best_genes[2],
        "month4": best_genes[3],
    }
    for fid, val in zip(FACTORY_IDS, best_genes[4:]):
        result[f"factor_{fid}"] = val
    out_path = Path("output") / "ga_best_params.json"
    data_path = Path("data") / "ga_best_params.json"
    data_path.parent.mkdir(exist_ok=True)
    # Only overwrite if better
    if data_path.exists():
        try:
            prev = json.loads(data_path.read_text(encoding="utf-8"))
            prev_score = float(prev.get("best_score", float("inf")))
        except Exception:
            prev_score = float("inf")
    else:
        prev_score = float("inf")
    if best_score < prev_score:
        data_path.write_text(
            json.dumps({"best_score": best_score, **result}, ensure_ascii=False, indent=2),
            encoding="utf-8",
        )
        print(f"New best mean abs error: {best_score:.4f}, saved to {data_path}")
    else:
        print(f"Best mean abs error: {best_score:.4f} (not better than {prev_score:.4f}, not saved)")
 if __name__ == "__main__":
    main()
--- a/production_line.py
+++ b/production_line.py
@ -0,0 +1,64 @@
 from mesa.agent import Agent
 class ProductionLineAgent(Agent):
    """A production line agent with basic production scheduling."""
    def __init__(self, model, line_id, region, factory, is_new_factory, schedule, ramp_ranges):
        super().__init__(model)
        self.line_id = line_id
        self.region = region
        self.factory = factory
        self.is_new_factory = is_new_factory
        # List of dicts: {"product": str, "start_month": int, "end_month": int}
        self.schedule = schedule
        # ramp_ranges: {1: value, 2: value, 3: value, 4: value}
        self.ramp_ranges = ramp_ranges
        self.blade_stock = {}  # 临时叶片库存 {product: blades not yet assembled}
        self.unit_stock = {}  # 叶片机组库存 {product: assembled units}
    def _sample_cycle_hours(self, product, month_index):
        idx = min(max(month_index, 1), 4)
        base = self.ramp_ranges[idx]
        factor = self.model.get_factory_factor(self.factory)
        return base * factor
    def step(self):
        month = self.model.current_month
        available_hours = self.model.get_available_hours(month)
        active = [
            entry for entry in self.schedule if entry["start_month"] <= month <= entry["end_month"]
        ]
        if not active:
            return
        # If multiple entries overlap, take the first one in the list.
        task = active[0]
        product = task["product"]
        if not self.is_new_factory:
            cycle_hours = self._sample_cycle_hours(product, 4)
        else:
            if task["start_month"] == 1:
                month_index = 4
            else:
                month_index = month - task["start_month"] + 1
            cycle_hours = self._sample_cycle_hours(product, month_index)
        produced_blades = available_hours / cycle_hours
        # Update blade and unit inventories
        current_blades = self.blade_stock.get(product, 0) + produced_blades
        units_to_add = int(current_blades // 3)
        remaining_blades = current_blades - units_to_add * 3
        if units_to_add > 0:
            self.unit_stock[product] = self.unit_stock.get(product, 0) + units_to_add
        self.blade_stock[product] = remaining_blades
        self.model.record_production(
            line_id=self.line_id,
            factory=self.factory,
            region=self.region,
            month=month,
            product=product,
            units=units_to_add,
        )
--- a/schedule_pulp.py
+++ b/schedule_pulp.py
@ -0,0 +1,173 @@
 # -*- coding: utf-8 -*-
 import pandas as pd
 import pulp
 from math import radians, sin, cos, sqrt, atan2
 #  1. 读取数据
 inventory_df = pd.read_excel('可调拨库存表.xlsx')
 factory_loc_df = pd.read_excel('工厂位置.xlsx')
 demand_df = pd.read_excel('需求表.xlsx')        # 型号、市、需求
 city_loc_df = pd.read_excel('地名经纬度.xlsx')    # 市、经度、纬度
 #  2. 数据合并
 inventory_df['可调拨库存'] = pd.to_numeric(inventory_df['可调拨库存'], errors='coerce').fillna(0)
 demand_df['需求'] = pd.to_numeric(demand_df['需求'], errors='coerce').fillna(0)
 # 工厂库存合并
 factory_capacity = {}
 for _, row in inventory_df.groupby(['工厂', '型号'])['可调拨库存'].sum().reset_index().iterrows():
    f, p, cap = row['工厂'], row['型号'], int(row['可调拨库存'])
    if cap > 0:
        factory_capacity.setdefault(f, {})[p] = cap
 # 城市需求合并
 city_demand = {}
 for _, row in demand_df.iterrows():
    city, p, dem = row['市'], row['型号'], int(row['需求'])
    if dem > 0:
        city_demand.setdefault(city, {})
        city_demand[city][p] = city_demand[city].get(p, 0) + dem
 factory_lonlat = dict(zip(factory_loc_df['工厂'], zip(factory_loc_df['经度'], factory_loc_df['纬度'])))
 city_lonlat    = dict(zip(city_loc_df['市'], zip(city_loc_df['经度'], city_loc_df['纬度'])))
 # missing = set(city_demand) - set(city_lonlat)
 # if missing:
 #     raise ValueError(f"缺少经纬度：{missing}")
 #  3. 距离
 dist = {}
 for f in factory_capacity:
    for c in city_demand:
        lon1, lat1 = factory_lonlat[f]
        lon2, lat2 = city_lonlat[c]
        dlon = radians(lon2-lon1)
        dlat = radians(lat2-lat1)
        a = sin(dlat/2)**2 + cos(radians(lat1))*cos(radians(lat2))*sin(dlon/2)**2
        dist[(f,c)] = 6371 * 2 * atan2(sqrt(a), sqrt(1-a))
 #  4. 单位运费函数
 def get_real_unit_cost(distance_km, product):
    d = distance_km
    p = str(product).strip()
    if p in ['GWBD-A2', 'GWBD-A3', 'GWBD-B']:
        if distance_km <= 100:
            return 15802.31
        elif distance_km <= 200:
            return 18445.12
        elif distance_km <= 300:
            return 22391.35
        elif distance_km <= 600:
            return 62.29 * d
        elif distance_km <= 900:
            return 44.75 * d
        elif distance_km <= 1200:
            return 38.23 * d
        elif distance_km <= 1500:
            return 36.08 * d
        elif distance_km <= 2000:
            return 33.44 * d
        elif distance_km <= 2500:
            return 32.76 * d
        elif distance_km <= 3500:
            return 26.68 * d
        else:
            return 24.27
    # 其他型号用第二套
    else:
        if distance_km <= 100:
            return 12859.46
        elif distance_km <= 200:
            return 14946.73
        elif distance_km <= 300:
            return 18662.46
        elif distance_km <= 600:
            return 52.83 * d
        elif distance_km <= 900:
            return 38.36 * d
        elif distance_km <= 1200:
            return 32.89 * d
        elif distance_km <= 1500:
            return 30.69 * d
        elif distance_km <= 2000:
            return 28.49 * d
        elif distance_km <= 2500:
            return 27.76 * d
        elif distance_km <= 3500:
            return 22.87 * d
        else:
            return 21.02 * d
 #  5. 规划模型
 prob = pulp.LpProblem("最小化运费", pulp.LpMinimize)
 x = {}
 for f in factory_capacity:
    for p in factory_capacity[f]:
        for city in city_demand:
            if city_demand[city].get(p, 0) > 0:
                x[(f,city,p)] = pulp.LpVariable(f"ship_{f}_{city}_{p}", lowBound=0, cat="Integer")
 # 缺货惩罚
 shortage = {(city,p): pulp.LpVariable(f"short_{city}_{p}", lowBound=0, cat="Continuous")
            for city in city_demand for p in city_demand[city]}
 BIG_M = 1e10
 prob += pulp.lpSum(
    x[k] * get_real_unit_cost(dist[(k[0],k[1])], k[2])   # 真实单位运费
    for k in x
 ) + pulp.lpSum(BIG_M * s for s in shortage.values())
 # 库存硬约束
 for f in factory_capacity:
    for p in factory_capacity[f]:
        prob += pulp.lpSum(x.get((f,c,p), 0) for c in city_demand) <= factory_capacity[f][p]
 # 需求软约束
 for city in city_demand:
    for p, dem in city_demand[city].items():
        prob += pulp.lpSum(x.get((f,city,p), 0) for f in factory_capacity) + shortage[(city,p)] >= dem
 # 6. 求解
 status = prob.solve(pulp.PULP_CBC_CMD(msg=True, timeLimit=600))
 print(f"\n求解状态: {pulp.LpStatus[status]}")
 if status != 1:
    print("无解（请检查总库存）")
 else:
    print("有解")
    # real_cost = pulp.value(prob.objective)
    # print(f"实际运输总费用: {real_cost:,.2f} 元")
 # ====================== 7. 输出 ======================
 result = []
 for (f,city,p), var in x.items():
    q = int(pulp.value(var) or 0)
    if q >= 1:
        d = dist[(f,city)]
        # unit = get_real_unit_cost(d, p)
        result.append({
            "工厂": f,
            "型号": p,
            "需求方": city,
            "调拨数量": q,
            # "距离_km": round(d, 1),
            # "真实单位运费": round(unit, 3),
            # "总运费": round(q * unit, 2)
        })
 df = pd.DataFrame(result)
 if not df.empty:
    df = df.sort_values(["工厂", "型号"])  # 按单位运费排序
    df[["工厂","型号","需求方","调拨数量"]].to_excel("调拨方案.xlsx", index=False)
    # print("\n调拨方案已保存（真实单位运费最优，优先满足单价更低的城市）")
    # print(df[["工厂","型号","需求方","调拨数量"]].to_string(index=False))
 else:
    print("\n无调拨记录")
 # 校验报告
 # print("\n各城市需求满足情况：")
 # for city in city_demand:
 #     for p, dem in city_demand[city].items():
 #         sup = sum(r["调拨数量"] for r in result if r["需求方"]==city and r["型号"]==p)
 #         print(f"  {city} | {p}: 需求 {dem} → 调拨 {sup} → {'完美' if sup==dem else '异常'}")
--- a/simulation_model.py
+++ b/simulation_model.py
		`@ -0,0 +1,2 @@`
							`# Auto detect text files and perform LF normalization`
							`* text=auto`