commit e4c411bbe2be6558b6163cc38fb7027318c6b83f Author: Zyy554 Date: Sun Nov 30 00:44:13 2025 +0800 first diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..dfe0770 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +# Auto detect text files and perform LF normalization +* text=auto diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..b0bf4c7 --- /dev/null +++ b/.gitignore @@ -0,0 +1,184 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +output/ + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000..f625fe0 --- /dev/null +++ b/README.md @@ -0,0 +1,2 @@ +# gw + diff --git a/app.py b/app.py new file mode 100644 index 0000000..0ed1711 --- /dev/null +++ b/app.py @@ -0,0 +1,259 @@ +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() diff --git a/build_distance_matrix.py b/build_distance_matrix.py new file mode 100644 index 0000000..6b629ee --- /dev/null +++ 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() diff --git a/data/DemandLocation.csv b/data/DemandLocation.csv new file mode 100644 index 0000000..c2f48c4 --- /dev/null +++ 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 \ No newline at end of file diff --git a/data/FactoryLocation.csv b/data/FactoryLocation.csv new file mode 100644 index 0000000..2946f2d --- /dev/null +++ b/data/FactoryLocation.csv @@ -0,0 +1,28 @@ +,,γ +,97.142116,40.792238 +ű,114.366182,41.115161 +ʱ,93.2830057,43.96107218 +ʱɫ,105.9323029,24.4875734 +ʱ,119.5548,33.3112 +ʱԭ,123.5325177,44.3099525 +ʱ,113.151737,27.835806 +˫,120.43,33.11 +˫Ӫ,118.07,36.55 +˳,115.0742,35.7773 +˳ɳ,112.590853,31.324363 +вİ׳,123.906296,45.50445 +вĸ,119.8,33.8 +вĹ,93.2830057,43.96107218 +вĺ,113.27,36.02 +вľȪ,97.142116,40.792238 +вƸ,119.13,34.36 +вƼ,113.89,27.66 +в,114.8256,44.5873 +в˰,122.089308,45.921339 +в,112.0157107,20.9886087 +в,93.2830057,43.96107218 +вϪ,101.16,23.19 +ͨ,90.524934,44.753213 +ͨ,123.906296,45.50445 +ͨ綫,120.42,32.12 +ͨ,103.9680036,39.09623555 diff --git a/data/ProductionLine.csv b/data/ProductionLine.csv new file mode 100644 index 0000000..7cfb596 --- /dev/null +++ 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 diff --git a/data/ProductionLine未修改.csv b/data/ProductionLine未修改.csv new file mode 100644 index 0000000..e8071c1 --- /dev/null +++ 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 diff --git a/data/benchmark.csv b/data/benchmark.csv new file mode 100644 index 0000000..d63a91c --- /dev/null +++ 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 diff --git a/data/benchmark未修改.csv b/data/benchmark未修改.csv new file mode 100644 index 0000000..fd87df1 --- /dev/null +++ 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 diff --git a/data/demand.csv b/data/demand.csv new file mode 100644 index 0000000..a632441 --- /dev/null +++ b/data/demand.csv @@ -0,0 +1,350 @@ +ͺ,ʡ,,,1,2,3,4,5,6,7,8,9,10,11,12, +GW83.4,,«,,,,,,,,,,,1,,,1 +GW83.4,,,,3,,3,,,2,,,,,,,8 +GW93,,,,,2,2,,,,,,,,,,4 +GW93,,,,,,3,,,,,,,,,,3 +GW93,,,,1,3,3,,,,,,,,,,7 +GW99A,,,,1,,,,,,,,,,,,1 +GW130,,γ,ϱ,,,1,,,,,,,,,,1 +GW110.5,,γ,ϱ,,,1,2,13,12,9,20,20,17,6,,100 +GW93,,γ,ϱ,,,,,4,4,5,,,,,,13 +GW93,,,,1,3,7,8,,,,,,,,,19 +GW93,½,,,,1,12,8,10,,,,,,,,31 +SI122,㶫,,,,,,,1,5,15,,,,,,21 +SI122,㶫,,,,,,,,,1,,,,,,1 +SI122,㶫,,,,,,,,5,13,4,,,,,22 +GW110.5,Ϻ,Ϻ,,,,,4,16,8,8,,,,,,36 +GW110.5,,γ,ϱ,,,,,,,,,10,8,6,,24 +GW110.5,,γ,ϱ,,,,,8,10,2,4,,,,,24 +GW110.5,,γ,ϱ,,,,,,1,1,4,8,9,,,23 +GW93,,,,2,,1,,,,,,,,,,3 +GW93,,,,,1,1,1,,,,,,,,,3 +GW93,,,,2,,3,1,,,,,,,,,6 +GW93,,,,2,2,7,2,,,,,,,,,13 +GW99A,,μ,,,,3,3,3,,,,,,,,9 +GW93,ɽ,̫ԭ,,1,3,5,,,,,,,,,,9 +GW93,,,ϱ,1,,,,,,,,,,,,1 +GW93,,,ϱ,,,1,,,,,,,,,,1 +GW81,,,,1,,,,,,,,,,,,1 +GW76,,,,,,,,,,,,,,7,8,15 +SI90.2,,,,,,,,,,,4,5,4,4,,17 +SI90.2,½,³ľ,,,,,,1,2,,,,,,,3 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+GW99A,,,,,,2,2,1,,,,,1,4,3,13 +GW99,,̨,,4,4,1,,,,,,,,,,9 +GWBD-A3,,Ƹ,ϱ,,,,,,,,,,,,2,2 +GW99A,,绯,,,,,2,2,5,2,,,,,,11 +GW99,,,,,,,,,,,,,,,1,1 +GWBD-A3,,,,,,,,,,,,,,,1,1 +GWBD-A3,,,,,,,,,,,,,,,1,1 +GW81,,,,,,,,,,8,8,4,,4,8,32 +GW99A,,,ϱ,,,2,1,,,,,,,,,3 +GW99,,,ϱ,2,,1,2,1,,,,,,,,6 +GW99A,,,ϱ,,,,,,,,,2,2,2,2,8 +GW93,,Ͻ,,,,,,2,,,,,,,,2 +GW99,,Ͻ,,,,2,3,1,2,,,,,,,8 +GW83.4,,,,2,,2,5,1,,,,,,,,10 +GW99A,,,ϱ,2,,,,,,,,,,,,2 +GW99A,,,,,,,,,,,,1,,,,1 +GW99,,,,,,,,,,,,,1,2,1,4 +GW99A,,,,,,1,1,6,3,1,5,1,,,,18 +GW99,,,ϱ,,,,,,,,2,3,4,5,1,15 +GW99A,,Ͽ,ϱ,6,,,,,,,,,,,,6 +GWBD-A2,½,,,,3,7,6,,6,6,5,3,3,9,,48 +GWBD-A2,½,ǵ,,,,,,,,4,12,10,19,,,45 +GW93,½,ǵ,,,,,,,4,14,2,1,,,,21 +GWBD-A2,½,ǵ,,,,,,,4,8,8,4,,,,24 +GWBD-A2,½,,,,,,,,,,,,,,1,1 +GWBD-A2,½,,,,,,,,,,,,,,1,1 +GWBD-A2,½,,,,,,,,,,,,,,5,5 +GW83.4,,ɽ,,,,,1,,,,,,,,,1 +GWBD-A2,½,,,,,5,8,3,,,,,,,,16 +GWBD-A2,½,,,,,,2,3,,,,,,,,5 +GW93,ɹ,ͺ,,,,,,,4,,,1,,,,5 +GW99A,,,,7,6,1,2,2,,,,,,,,18 +GW99A,,̨,,,,,,1,4,8,10,9,,,,32 +GWBD-A2,ӱ,żҿ,,,,,,,,,,,1,4,,5 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+GWBD-A2,,ͨ,,,,,,3,1,,,,,,,4 +GW99,,ͨ,,,,,,1,,,,,,,,1 +GWBD-A2,ӱ,ػʵ,,,,,,,,1,,,,3,4,8 +GW99A,,,,4,,2,7,,,,,,,,,13 +GW99A,,,,,,,12,4,,,,,,,,16 +GW81,,,,,,4,,,,,,,,,,4 +GWBD-A3,,,,,,,,,,,,5,1,,,6 +GW93,,,,,,,,,,1,1,,,,,2 +GW99,ӱ,е,,1,1,,,1,,,,,,,,3 +GW99,ӱ,е,,2,,2,2,7,,,,,,,,13 +SI122,,,ϲ,,,,,,3,7,2,,,,,12 +GW130,㶫,,,,,,,,,,,,3,3,1,7 +GW93,ӱ,е,,,,,,,2,1,,,,,,3 +SI90.2,ӱ,е,,1,,4,2,,10,3,,1,,,,21 +GW93,ӱ,е,,,,2,1,3,,,,,,,,6 +SI90.2,ӱ,е,,3,,3,,8,,,,,,,,14 +GW81,,,,,,,,,,2,,,,,,2 +GWBD-A3,,,ϱ,,1,2,,,,,,,,,,3 +GW99A,ɽ,,,,,,,,,,,,,2,1,3 +GW99,ɽ,,,,,,,,,,,,,2,3,5 +GWBD-A2,,ľ˹,,6,3,3,,,6,5,5,,1,3,4,36 +GWBD-A2,,,,1,,,,,,,,,,,,1 +SI90.2,,,,,,,,,,,,8,10,8,,26 +SI90.2,,,,10,,,,,,,,,,,,10 +GW81,,,,,,,,,,7,,,,,,7 +GW83.3,,,,,,,,,,,,5,,,,5 +GW93,ɽ,˳,,,,,,,,,,,,,4,4 +GWBD-A2,ɽ,˷,,,,,,,,,,,2,8,3,13 +GWBD-A2,½,,,,,,,,1,4,8,3,8,,,24 +GW99,½,,,,,12,5,8,6,6,,11,7,10,,65 +GWBD-A2,,,,,,,,,,,,,,2,2,4 +GW93,,μ,,1,2,2,,,,,,,,,,5 +GWBD-A2,ɽ,,,,,,,,,,,1,3,6,3,13 +GWBD-A2,ɽ,ͬ,,,,,1,,2,,2,1,3,3,,12 +GW76,,,,2,,,,,,,,,,,,2 +GW99,ɹ,˹,,,,,,,3,5,2,1,4,10,5,30 +GW76,,,,1,,,,,,,,,,,,1 +GWBD-A2,½,³ľ,,,,,6,7,,,,,,,,13 +GW99,ຣ,ϲ,,,,,,,,,2,3,,,,5 +GW76,,,,7,,,,,,,,,,,,7 +GWBD-A3,,Ͻ,,,,,2,1,2,,1,,,,,6 +GWBD-A2,,,,,,,,2,,,,,,,,2 +SI90.2,½,³,,5,,,,,,,,,,,,5 +GW93,,,,,,1,,,,,,,,,,1 +GWBD-A2,ɹ,˰,,,,,,1,3,2,2,3,4,5,,20 +GWBD-A2,ɹ,˰,,,,,,,,,,3,,1,,4 +GWBD-A2,ɹ,˰,,,,,1,8,6,1,3,,,,,19 +GWBD-A2,ɹ,˰,,,,,,,4,,,,,,,4 +GWBD-A2,,,,,,,,,1,,,,,,,1 +GW76,,,,,,,4,,,,,,,,,4 +GW86,,,,,8,,,,,,,,,,,8 +GW76,,,,3,,,,,,,,,,,,3 +GW81,,,,,,,5,,,,,,,,,5 +GW76,,,,7,,,,,,,,,,,,7 +GW76,,,,,,,7,,,,,,,,,7 +SI90.2,,,,,,,7,10,,10,5,,,,,32 +SI90.2,,,,,7,16,10,,,,15,12,,,,60 +GW83.4,,,,14,7,,,,7,6,,,,,,34 +GW76,,,,,,,,,4,,,,,,,4 +GW81,,,,,,,,,,1,,,,,,1 +GW130,㶫,,,,,,,,,,,,5,10,10,25 +GW81,,,,,,,,,10,,5,,10,,,25 +GWBD-A2,½,,,,,,,,,8,7,2,6,5,15,43 +SI90.2,½,³,,,,,,1,4,7,12,3,15,14,,56 +GW99A,,Ȫ,,,,,,,,,,,3,9,3,15 +GWBD-A2,ɹ,ͨ,,2,2,5,4,4,,,,,,,,17 +GW99A,ӱ,̨,,,,,1,3,2,3,5,1,1,,,16 +GW99,,,,,,,,,,,,,3,,2,5 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+黔东南苗族侗族自治州,中材哈密,2436.1403766292683 +黔东南苗族侗族自治州,中材邯郸,1337.1946642735968 +黔东南苗族侗族自治州,中材酒泉,1967.521346191245 +黔东南苗族侗族自治州,中材连云港,1537.6588706876562 +黔东南苗族侗族自治州,中材萍乡,722.9016459217405 +黔东南苗族侗族自治州,中材锡林,2264.2273584225327 +黔东南苗族侗族自治州,中材兴安盟,2660.78936359328 +黔东南苗族侗族自治州,中材阳江,680.9807889077367 +黔东南苗族侗族自治州,中材伊吾,2436.1403766292683 +黔东南苗族侗族自治州,中材玉溪,649.0707233593354 +黔东南苗族侗族自治州,重通昌吉,2640.2489232525113 +黔东南苗族侗族自治州,重通大安,2708.958571245718 +黔东南苗族侗族自治州,重通如东,1502.9565853143051 +黔东南苗族侗族自治州,重通武威,1574.9209976445593 diff --git a/data/factory_mapping.json b/data/factory_mapping.json new file mode 100644 index 0000000..dfbc33b --- /dev/null +++ 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" +} diff --git a/data/ga_best_params.json b/data/ga_best_params.json new file mode 100644 index 0000000..621b9f5 --- /dev/null +++ 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 +} \ No newline at end of file diff --git a/data/ga_two_best_params.json b/data/ga_two_best_params.json new file mode 100644 index 0000000..03a97ca --- /dev/null +++ b/data/ga_two_best_params.json @@ -0,0 +1,5 @@ +{ + "best_score": 0.013780913115116374, + "factor_ZhongcaiBaicheng": 1.8461095046937892, + "factor_ZhongcaiHami": 1.4843503931169255 +} \ No newline at end of file diff --git a/data/model_params.json b/data/model_params.json new file mode 100644 index 0000000..6f5a806 --- /dev/null +++ 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" + ] +} diff --git a/data/month_hours.json b/data/month_hours.json new file mode 100644 index 0000000..79a9196 --- /dev/null +++ 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 +} diff --git a/data/product.csv b/data/product.csv new file mode 100644 index 0000000..b9146ef --- /dev/null +++ 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 diff --git a/data/transportation_price.csv b/data/transportation_price.csv new file mode 100644 index 0000000..5ee6b35 --- /dev/null +++ b/data/transportation_price.csv @@ -0,0 +1,24 @@ +Ʒͺ,[], ƽ[Ԫ/(*Ƭ)] , ƽ[Ԫ/Ƭ] +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 , +,,, diff --git a/demand_agent.py b/demand_agent.py new file mode 100644 index 0000000..25f53e3 --- /dev/null +++ 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) diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..0988be4 --- /dev/null +++ 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 diff --git a/ga_two_factor_calibration.py b/ga_two_factor_calibration.py new file mode 100644 index 0000000..244fbd1 --- /dev/null +++ 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() diff --git a/genetic_calibration.py b/genetic_calibration.py new file mode 100644 index 0000000..8b397a7 --- /dev/null +++ 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() diff --git a/production_line.py b/production_line.py new file mode 100644 index 0000000..ac7aeec --- /dev/null +++ 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, + ) diff --git a/schedule_pulp.py b/schedule_pulp.py new file mode 100644 index 0000000..d9f2b55 --- /dev/null +++ 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'}") diff --git a/simulation_model.py b/simulation_model.py new file mode 100644 index 0000000..b044f83 --- /dev/null +++ b/simulation_model.py @@ -0,0 +1,1054 @@ +import json +import pandas as pd +import os +from datetime import datetime +import matplotlib +matplotlib.use('TkAgg') +import matplotlib.pyplot as plt +from mesa.datacollection import DataCollector +from mesa.model import Model +import pulp + +from production_line import ProductionLineAgent +from demand_agent import DemandAgent + +# Configure matplotlib fonts for Chinese output in plots. +matplotlib.rcParams["font.family"] = ["Microsoft YaHei", "SimHei", "sans-serif"] +matplotlib.rcParams["axes.unicode_minus"] = False + + +class SimulationModel(Model): + """ + Minimal model that instantiates one ProductionLineAgent per production line + entry and tracks the number of lines per region over time. + """ + + def __init__( + self, + month1: float | None = None, + month2: float | None = None, + month3: float | None = None, + month4: float | None = None, + factory_factors: dict | None = None, + output_enabled: bool = False, + is_within_region_allocation_only: bool | None = None, + product_set: tuple | list | set | str | None = None, + **kwargs, + ): + super().__init__() + cfg = self._load_model_params() + # Apply overrides if provided + self.month_holiday_days = self._load_month_holiday_days(cfg) + self.ramp_ranges = { + 1: float(month1 if month1 is not None else cfg["month1"]), + 2: float(month2 if month2 is not None else cfg["month2"]), + 3: float(month3 if month3 is not None else cfg["month3"]), + 4: float(month4 if month4 is not None else cfg["month4"]), + } + self.factory_mapping = self._load_factory_mapping() + self.factory_factors = self._load_factory_factors(cfg) + merged_factors = {} + merged_factors.update({k: v for k, v in kwargs.items() if k.startswith("factor_")}) + if factory_factors: + merged_factors.update(factory_factors) + if merged_factors: + self._merge_factory_factors(merged_factors) + self.default_new_factory_factor = cfg.get("factor_default", 1.3) + self.within_region_only = self._to_bool( + is_within_region_allocation_only if is_within_region_allocation_only is not None else cfg.get("is_within_region_allocation_only", False) + ) + self.current_month = 1 + self.cumulative_production = 0 + self.monthly_totals = {} + self.production_log = [] + self.mean_abs_error = 0.0 + # Allow explicit product_set override; otherwise fall back to config. + self.product_set = self._parse_product_set(product_set if product_set is not None else cfg.get("product_set")) + self.output_enabled = output_enabled + self.region_totals = {} + self.factory_error_df = None + self.demand_agents = [] + self.region_demand_totals = {} + self.region_alloc_inventory = {} + self.region_demand_fulfilled = {} + self.backlog_city_prod = {} + self.region_fulfill_pct = {} + self.region_unmet_backlog = {} + self.region_transport_month = {} + self.inv_history = [] + self.fulfill_history_month = [] + self.fulfill_history_cum = [] + self.unmet_history = [] + self.assignment_log = [] + self.overall_fulfill_pct = 0.0 + self.monthly_transport_cost = 0.0 + self.fulfill_overall_history = [] + self.transport_cost_history = [] + self.transport_units_history = [] + self.product_names = set() + self.product_list = [] + self.monthly_allocation_summary = [] + + self._load_month_hours() + self._load_agents_from_csv() + self._load_demand_agents_from_csv() + self._load_transport_data() + + # Initialize fulfillment tracking before the first DataCollector run + demand_regions = getattr(self, "demand_regions", []) + self.region_fulfill_pct_month = {r: 0 for r in demand_regions} + self.region_fulfill_pct_cum = {r: 0 for r in demand_regions} + self.region_transport_month = {r: 0 for r in demand_regions} + + reporters = { + region: (lambda m, region=region: m.region_totals.get(region, 0)) + for region in self.region_names + } + reporters.update( + {f"demand_{region}": (lambda m, region=region: m.region_demand_totals.get(region, 0)) + for region in self.demand_regions} + ) + reporters.update( + { + f"inventory_{region}": (lambda m, region=region: m.region_alloc_inventory.get(region, 0)) + for region in self.region_names + } + ) + reporters.update( + { + f"demand_fulfilled_{region}": (lambda m, region=region: m.region_demand_fulfilled.get(region, 0)) + for region in self.demand_regions + } + ) + reporters.update( + { + "cumulative_production": lambda m: m.cumulative_production, + "monthly_total": lambda m: m.monthly_totals.get(m.current_month, 0), + "mean_abs_error": lambda m: m.mean_abs_error, + "fulfill_pct_overall": lambda m: m.overall_fulfill_pct, + "transport_cost": lambda m: m.monthly_transport_cost, + } + ) + reporters.update( + {f"demand_{region}": (lambda m, region=region: m.region_demand_totals.get(region, 0)) + for region in self.demand_regions} + ) + reporters.update( + { + f"inventory_{region}": (lambda m, region=region: m.region_alloc_inventory.get(region, 0)) + for region in self.region_names + } + ) + reporters.update( + { + f"fulfill_pct_{region}": (lambda m, region=region: m.region_fulfill_pct.get(region, 0)) + for region in self.demand_regions + } + ) + reporters.update( + { + f"unmet_{region}": (lambda m, region=region: m.region_unmet_backlog.get(region, 0)) + for region in self.demand_regions + } + ) + reporters.update( + { + f"transport_units_{region}": (lambda m, region=region: m.region_transport_month.get(region, 0)) + for region in self.demand_regions + } + ) + # Ensure fulfillment tracking exists before reporter access + if not hasattr(self, "region_fulfill_pct_month"): + self.region_fulfill_pct_month = {r: 0 for r in self.demand_regions} + if not hasattr(self, "region_fulfill_pct_cum"): + self.region_fulfill_pct_cum = {r: 0 for r in self.demand_regions} + reporters.update( + { + f"fulfill_month_{region}": (lambda m, region=region: m.region_fulfill_pct_month.get(region, 0)) + for region in self.demand_regions + } + ) + reporters.update( + { + f"fulfill_cum_{region}": (lambda m, region=region: m.region_fulfill_pct_cum.get(region, 0)) + for region in self.demand_regions + } + ) + self.datacollector = DataCollector(model_reporters=reporters) + self.running = True + self.datacollector.collect(self) + + def _get_output_timestamp(self) -> str: + if not hasattr(self, "_output_timestamp"): + self._output_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + return self._output_timestamp + + def _load_month_hours(self): + with open("data/month_hours.json", "r", encoding="utf-8") as f: + self.month_days = {int(k): v for k, v in json.load(f).items()} + + def _load_model_params(self): + with open("data/model_params.json", "r", encoding="utf-8") as f: + return json.load(f) + + def _load_month_holiday_days(self, cfg: dict) -> dict: + # Default 2 days off per month unless overridden + holidays = {m: 2 for m in range(1, 13)} + for m in range(1, 13): + key = f"holiday_days_{m}" + if key in cfg: + try: + holidays[m] = int(float(cfg[key])) + except Exception: + continue + return holidays + + def _sanitize_product(self, name: str) -> str: + return str(name).replace("-", "").replace(".", "") + + def _load_factory_mapping(self) -> dict: + with open("data/factory_mapping.json", "r", encoding="utf-8") as f: + return json.load(f) + + def _parse_product_set(self, val) -> tuple: + if val is None: + return tuple() + if isinstance(val, str): + stripped = val.strip() + if stripped.startswith("(") and stripped.endswith(")"): + stripped = stripped[1:-1] + parts = [p.strip() for p in stripped.split(",") if p.strip()] + return tuple(parts) + if isinstance(val, (list, tuple, set)): + return tuple(str(v).strip() for v in val if str(v).strip()) + return tuple() + + def _to_bool(self, val): + if isinstance(val, str): + return val.strip().lower() in {"true", "1", "yes", "y", "是", "对"} + return bool(val) + + def _load_factory_factors(self, cfg: dict) -> dict: + factors = {} + for key, val in cfg.items(): + if key.startswith("factor_"): + suffix = key[len("factor_") :] + factors[suffix] = float(val) + return factors + + def _merge_factory_factors(self, overrides: dict): + for key, val in overrides.items(): + suffix = key + if key.startswith("factor_"): + suffix = key[len("factor_") :] + self.factory_factors[self._sanitize_product(suffix)] = float(val) + + def get_factory_code(self, factory_name: str) -> str: + return self.factory_mapping.get(factory_name, self._sanitize_product(factory_name)) + + def get_factory_factor(self, factory_name: str) -> float: + code = self.get_factory_code(factory_name) + return self.factory_factors.get(code, self.default_new_factory_factor) + + def _load_agents_from_csv(self): + encodings = ("utf-8", "utf-8-sig", "gbk") + last_error = None + for enc in encodings: + try: + df = pd.read_csv("data/ProductionLine.csv", encoding=enc) + break + except UnicodeDecodeError as exc: + last_error = exc + continue + else: + raise last_error + + df["生产型号"] = df["生产型号"].astype(str).str.strip() + if self.product_set: + df = df[df["生产型号"].isin(self.product_set)] + self.region_names = sorted(df["区域名"].unique()) if not df.empty else [] + self.line_factory = {} + self.line_region = {} + for line_id, group in df.groupby("产线ID"): + first = group.iloc[0] + schedule = [] + for _, row in group.iterrows(): + product = row["生产型号"] + schedule.append( + { + "product": product, + "start_month": int(row["开始月份"]), + "end_month": int(row["结束月份"]), + } + ) + self.product_names.add(str(product).strip()) + is_new_factory = str(first["是否新工厂"]).strip() in {"是", "Yes", "True", "true", "1"} + self.line_factory[line_id] = first["工厂名"] + self.line_region[line_id] = first["区域名"] + ProductionLineAgent( + model=self, + line_id=line_id, + region=first["区域名"], + factory=first["工厂名"], + is_new_factory=is_new_factory, + schedule=schedule, + ramp_ranges=self.ramp_ranges, + ) + + def _load_demand_agents_from_csv(self): + encodings = ("utf-8", "utf-8-sig", "gbk") + last_error = None + for enc in encodings: + try: + df = pd.read_csv("data/demand.csv", encoding=enc) + break + except UnicodeDecodeError as exc: + last_error = exc + continue + else: + raise last_error + + product_col = df.columns[0] + df[product_col] = df[product_col].astype(str).str.strip() + if self.product_set: + df = df[df[product_col].isin(self.product_set)] + month_cols = [col for col in df.columns if col.endswith("月")] + df[month_cols] = df[month_cols].apply(pd.to_numeric, errors="coerce") + # total_demand = df[month_cols].stack().sum() + # region_totals = df.groupby("需求区域")[month_cols].sum().sum(axis=1) + # print(f"载入需求总量：{total_demand}") + # print(f"分区域需求：{region_totals.to_dict()}") + month_cols = [col for col in df.columns if col.endswith("月")] + grouped = ( + df.groupby([product_col, "市", "需求区域"])[month_cols] + .sum() + .reset_index() + ) + self.demand_regions = sorted(grouped["需求区域"].unique()) + self.demand_by_city_product = {} + self.city_to_region = {} + + # Aggregate per city-region into product -> monthly dict + city_region_groups = grouped.groupby(["市", "需求区域"]) + for (city, region), sub in city_region_groups: + prod_monthly = {} + for _, row in sub.iterrows(): + prod_name = str(row[product_col]).strip() + monthly = {int(col.rstrip("月")): row[col] for col in month_cols} + prod_monthly[prod_name] = monthly + self.demand_by_city_product[(city, prod_name)] = monthly + self.city_to_region[str(city).strip()] = region + self.product_names.add(prod_name) + DemandAgent( + model=self, + city=city, + region=region, + product_monthly_demand=prod_monthly, + ) + + def _load_transport_data(self): + # Distance matrix + dist_df = pd.read_csv("data/distance_matrix.csv", encoding="utf-8") + self.distance_lookup = { + (row["factory"], row["demand_city"]): float(row["distance_km"]) + for _, row in dist_df.iterrows() + } + # Transportation prices + price_df = pd.read_csv("data/transportation_price.csv", encoding="gbk") + price_df.columns = [c.strip() for c in price_df.columns] + price_df["产品型号"] = price_df["产品型号"].str.strip() + if self.product_set: + generic = {"others", "other", "其它", "其他"} + price_df = price_df[ + price_df["产品型号"].isin(self.product_set) + | price_df["产品型号"].str.lower().isin(generic) + ] + price_df["里程区间[公里]"] = price_df["里程区间[公里]"].str.strip() + for col in price_df.columns: + if "单价" in col or "整车价" in col: + price_df[col] = ( + price_df[col] + .astype(str) + .str.replace(",", "", regex=False) + .str.replace(" ", "", regex=False) + ) + price_df[col] = pd.to_numeric(price_df[col], errors="coerce") + self.transport_prices = [] + for _, row in price_df.iterrows(): + rng = str(row["里程区间[公里]"]) + if "-" in rng: + low, high = rng.split("-") + low = float(low) + high = float(high) + else: + low = 0.0 + high = float("inf") + self.transport_prices.append( + { + "category": row["产品型号"], + "low": low, + "high": high, + "unit_price": row.get("平均单价[元/(公里*片)]", None), + "truck_price": row.get("平均整车价[元/片]", None), + } + ) + self.special_products = {"GWBD-A2", "GWBD-A3", "GWBD-B"} + self.big_M = 1e6 + # Finalize product list once all sources are loaded + self.product_list = sorted(self.product_names) + + def _cost_per_unit(self, factory, city, product): + dist = self.distance_lookup.get((factory, city)) + if dist is None or pd.isna(dist): + return self.big_M + prod = str(product).strip() + best_cost = None + for row in self.transport_prices: + row_cat = str(row["category"]).strip() + # Match logic: exact product, specials grouped, or generic others buckets + is_special_prod = prod in self.special_products + is_special_row = row_cat in self.special_products + is_generic_row = row_cat.lower() in {"others", "other", "其它", "其他"} + match = False + if row_cat == prod: + match = True + elif is_special_prod and is_special_row: + match = True + elif is_generic_row: + match = True + if not match: + continue + if row["low"] <= dist <= row["high"]: + unit_price = row["unit_price"] + truck_price = row["truck_price"] + cost = None + if pd.notna(unit_price): + cost = unit_price * dist * 3 + elif pd.notna(truck_price): + cost = truck_price * 3 + if cost is not None: + best_cost = cost if best_cost is None else min(best_cost, cost) + return best_cost if best_cost is not None else self.big_M + + def get_available_hours(self, month: int) -> float: + days = self.month_days.get(month, 30) + holidays = self.month_holiday_days.get(month, 0) + days = max(days - holidays, 0) + return days * 24 + + def record_demand(self, region: str, demand_units: float): + self.region_demand_totals[region] = self.region_demand_totals.get(region, 0) + demand_units + + def count_region(self, region): + return sum(1 for agent in self.agents if agent.region == region) + + def record_production(self, line_id, factory, region, month, product, units): + if units <= 0: + return + self.production_log.append( + { + "line_id": line_id, + "factory": factory, + "region": region, + "month": month, + "product": product, + "units": units, + } + ) + self.monthly_totals[month] = self.monthly_totals.get(month, 0) + units + self.cumulative_production += units + self.region_totals[region] = self.region_totals.get(region, 0) + units + + def step(self): + for agent in list(self.agents): + agent.step() + # Allocation after production for current month + self._run_allocation() + self._update_region_inventory() + self._record_histories() + if self.current_month >= 12: + self.running = False + self._finalize_factory_errors(write_files=self.output_enabled) + if self.output_enabled: + self._write_report() + self._write_allocation_workbook() + self.datacollector.collect(self) + if not self.running and self.output_enabled: + self._write_visualization_outputs() + self.current_month += 1 + + def _write_report(self): + if not self.production_log: + return + df = pd.DataFrame(self.production_log) + # Group by product, factory, line and pivot months + pivot = ( + df.groupby(["product", "line_id", "factory", "month"])["units"] + .sum() + .reset_index() + .pivot_table( + index=["product", "line_id", "factory"], + columns="month", + values="units", + fill_value=0, + ) + ) + # Ensure columns 1-12 exist + for m in range(1, 13): + if m not in pivot.columns: + pivot[m] = 0 + pivot = pivot[sorted([c for c in pivot.columns if isinstance(c, int)])] + pivot["total"] = pivot.sum(axis=1) + pivot.reset_index(inplace=True) + pivot.columns = ( + ["生产型号", "产线名称", "工厂名"] + + [f"{m}月" for m in range(1, 13)] + + ["总计"] + ) + timestamp = self._get_output_timestamp() + output_dir = "output" + os.makedirs(output_dir, exist_ok=True) + out_path = os.path.join(output_dir, f"production_report_{timestamp}.csv") + pivot.to_csv(out_path, index=False, encoding="utf-8-sig") + + def _write_allocation_workbook(self): + if not self.monthly_allocation_summary: + return + timestamp = self._get_output_timestamp() + output_dir = "output" + os.makedirs(output_dir, exist_ok=True) + out_path = os.path.join(output_dir, f"allocation_summary_{timestamp}.xlsx") + with pd.ExcelWriter(out_path, engine="openpyxl") as writer: + for month in range(1, 13): + rows = [row for row in self.monthly_allocation_summary if row["month"] == month] + if not rows and self.product_list: + rows = [ + { + "month": month, + "product": product, + "inventory_before": 0, + "demand_before": 0, + "allocated": 0, + "inventory_after": 0, + "demand_after": 0, + } + for product in self.product_list + ] + if not rows: + continue + df = pd.DataFrame(rows) + df = df.sort_values(by="product") + df = df[ + ["product", "inventory_before", "demand_before", "allocated", "inventory_after", "demand_after"] + ] + df.columns = ["叶片型号", "调拨前总库存", "调拨前总需求", "当月调拨总量", "调拨后总库存", "调拨后总需求"] + sheet_name = f"第{month}月" + df.to_excel(writer, sheet_name=sheet_name, index=False) + + def _write_visualization_outputs(self): + df = self.datacollector.get_model_vars_dataframe() + if df.empty: + return + + timestamp = self._get_output_timestamp() + output_dir = "output" + os.makedirs(output_dir, exist_ok=True) + + def plot_series(columns, label_map, ylabel, title, filename, colors=None): + available = [col for col in columns if col in df.columns] + if not available: + return + plt.figure(figsize=(10, 6)) + for col in available: + color = colors.get(col) if isinstance(colors, dict) else None + plt.plot(df.index, df[col], label=label_map.get(col, col), color=color) + plt.xlabel("步数") + if ylabel: + plt.ylabel(ylabel) + if title: + plt.title(title) + plt.legend(loc="best") + plt.tight_layout() + plt.savefig(os.path.join(output_dir, f"{filename}_{timestamp}.png")) + plt.close() + + def make_color_map(keys): + return {key: f"C{idx}" for idx, key in enumerate(keys)} + + production_cols = [region for region in self.region_names if region in df.columns] + demand_cols = [f"demand_{region}" for region in self.demand_regions if f"demand_{region}" in df.columns] + inventory_cols = [f"inventory_{region}" for region in self.region_names if f"inventory_{region}" in df.columns] + fulfill_cols = [f"fulfill_pct_{region}" for region in self.demand_regions if f"fulfill_pct_{region}" in df.columns] + unmet_cols = [f"unmet_{region}" for region in self.demand_regions if f"unmet_{region}" in df.columns] + fulfill_month_cols = [f"fulfill_month_{region}" for region in self.demand_regions if f"fulfill_month_{region}" in df.columns] + fulfill_cum_cols = [f"fulfill_cum_{region}" for region in self.demand_regions if f"fulfill_cum_{region}" in df.columns] + transport_unit_cols = [f"transport_units_{region}" for region in self.demand_regions if f"transport_units_{region}" in df.columns] + + plot_series( + production_cols, + {col: f"{col}-产出[套]" for col in production_cols}, + "产出[套]", + "区域产出", + "production_by_region", + colors=make_color_map(production_cols), + ) + plot_series( + demand_cols, + {col: f"{col.replace('demand_', '')}-需求[套]" for col in demand_cols}, + "需求量[套]", + "区域需求", + "demand_by_region", + colors=make_color_map(demand_cols), + ) + plot_series( + inventory_cols, + {col: f"{col.replace('inventory_', '')}-库存[套]" for col in inventory_cols}, + "库存量[套]", + "区域库存", + "inventory_by_region", + colors=make_color_map(inventory_cols), + ) + plot_series( + fulfill_cols, + {col: f"{col.replace('fulfill_pct_', '')}-满足率[%]" for col in fulfill_cols}, + "满足率[%]", + "区域满足率（累计）", + "fulfill_pct_by_region", + colors=make_color_map(fulfill_cols), + ) + plot_series( + fulfill_month_cols, + {col: f"{col.replace('fulfill_month_', '')}-当月满足率[%]" for col in fulfill_month_cols}, + "当月满足率[%]", + "区域满足率（当月）", + "fulfill_pct_month_by_region", + colors=make_color_map(fulfill_month_cols), + ) + plot_series( + fulfill_cum_cols, + {col: f"{col.replace('fulfill_cum_', '')}-累计满足率[%]" for col in fulfill_cum_cols}, + "累计满足率[%]", + "区域满足率（累计）", + "fulfill_pct_cum_by_region", + colors=make_color_map(fulfill_cum_cols), + ) + plot_series( + unmet_cols, + {col: f"{col.replace('unmet_', '')}-未满足需求[套]" for col in unmet_cols}, + "未满足需求[套]", + "区域未满足需求", + "unmet_by_region", + colors=make_color_map(unmet_cols), + ) + plot_series( + transport_unit_cols, + {col: f"{col.replace('transport_units_', '')}-运输量[套]" for col in transport_unit_cols}, + "运输量[套]", + "区域运输量", + "transport_units_by_region", + colors=make_color_map(transport_unit_cols), + ) + if "fulfill_pct_overall" in df.columns: + plot_series( + ["fulfill_pct_overall"], + {"fulfill_pct_overall": "总体满足率[%]"}, + "总体满足率[%]", + "总体满足率", + "fulfill_pct_overall", + ) + if "transport_cost" in df.columns: + plot_series( + ["transport_cost"], + {"transport_cost": "运输成本[万元]"}, + "运输成本[万元]", + "运输成本", + "transport_cost", + ) + + def _finalize_factory_errors(self, write_files: bool): + if not self.production_log: + self.mean_abs_error = float("inf") + return + df = pd.DataFrame(self.production_log) + factory_pivot = ( + df.groupby(["factory", "month"])["units"] + .sum() + .reset_index() + .pivot_table(index="factory", columns="month", values="units", fill_value=0) + ) + for m in range(1, 13): + if m not in factory_pivot.columns: + factory_pivot[m] = 0 + factory_pivot = factory_pivot[sorted([c for c in factory_pivot.columns if isinstance(c, int)])] + factory_pivot["total"] = factory_pivot.sum(axis=1) + factory_pivot.reset_index(inplace=True) + factory_pivot = factory_pivot.sort_values(by="factory") + factory_pivot.columns = ["工厂名称"] + [f"{m}月" for m in range(1, 13)] + ["总计"] + + # Benchmark comparison + try: + benchmark = pd.read_csv("data/benchmark.csv", encoding="utf-8-sig") + except UnicodeDecodeError: + benchmark = pd.read_csv("data/benchmark.csv", encoding="gbk") + benchmark_sorted = benchmark.sort_values(by=benchmark.columns[0]) + total_col = "总计" + month_cols = [f"{m}月" for m in range(1, 13)] + if total_col not in benchmark_sorted.columns: + benchmark_sorted[total_col] = benchmark_sorted[month_cols].sum(axis=1) + bench_total = benchmark_sorted[total_col].astype(float).reset_index(drop=True) + prod_total = factory_pivot["总计"].astype(float).reset_index(drop=True) + min_len = min(len(bench_total), len(prod_total)) + if min_len == 0: + self.mean_abs_error = float("inf") + return + bench_total = bench_total.iloc[:min_len] + prod_total = prod_total.iloc[:min_len] + bench_total_safe = bench_total.replace(0, pd.NA) + rel_errors = (prod_total - bench_total_safe) / bench_total_safe + rel_errors = rel_errors.fillna(0).astype(float) + abs_rel_errors = rel_errors.abs() + self.mean_abs_error = abs_rel_errors.mean() + + if not write_files: + return + + timestamp = self._get_output_timestamp() + output_dir = "output" + os.makedirs(output_dir, exist_ok=True) + factory_report_path = os.path.join(output_dir, f"factory_report_{timestamp}.csv") + factory_pivot.to_csv(factory_report_path, index=False, encoding="utf-8-sig") + + error_df = pd.DataFrame( + { + "工厂名称": factory_pivot["工厂名称"].iloc[:min_len], + "仿真总计": prod_total, + "基准总计": bench_total, + "误差（比例）": rel_errors.round(6), + "误差绝对值（比例）": abs_rel_errors.round(6), + } + ) + error_path = os.path.join(output_dir, f"factory_error_{timestamp}.csv") + error_df.to_csv(error_path, index=False, encoding="utf-8-sig") + + # Error bar charts by month and by factory (mean error) + # Align monthly errors + prod_months = factory_pivot[[f"{m}月" for m in range(1, 13)]].iloc[:min_len].astype(float) + bench_months = benchmark_sorted[[f"{m}月" for m in range(1, 13)]].iloc[:min_len].astype(float) + bench_months_safe = bench_months.replace(0, pd.NA) + month_pct_errors = (prod_months - bench_months_safe) / bench_months_safe * 100 + month_pct_errors = month_pct_errors.fillna(0).convert_dtypes().astype(float) + month_error_means = pd.Series(month_pct_errors.mean(axis=0), index=[f"{m}月" for m in range(1, 13)]) + + plt.figure(figsize=(10, 5)) + month_error_means.plot(kind="bar") + plt.ylabel("平均误差（%）") + plt.title("按月份的平均误差（百分比）") + plt.tight_layout() + month_plot_path = os.path.join(output_dir, f"error_by_month_{timestamp}.png") + plt.savefig(month_plot_path) + plt.close() + + bench_totals_safe = bench_total.replace(0, pd.NA) + factory_pct_errors = (prod_total - bench_totals_safe) / bench_totals_safe * 100 + factory_pct_errors = factory_pct_errors.fillna(0).convert_dtypes().astype(float) + factory_names = factory_pivot["工厂名称"].iloc[:min_len].reset_index(drop=True) + factory_df = pd.DataFrame({"name": factory_names, "error_pct": factory_pct_errors}).sort_values( + by="error_pct" + ) + self.factory_error_df = factory_df.reset_index(drop=True) + plt.figure(figsize=(12, 6)) + ax = factory_df["error_pct"].reset_index(drop=True).plot(kind="bar") + plt.ylabel("误差（%）") + plt.title("按工厂的误差（总计，百分比）") + plt.tight_layout() + # Annotate large absolute errors + for idx, row in factory_df.reset_index(drop=True).iterrows(): + val = row["error_pct"] + if abs(val) >= 50: # threshold + ax.text( + idx, + val, + row["name"], + rotation=90, + va="bottom" if val >= 0 else "top", + ha="center", + fontsize=8, + ) + factory_plot_path = os.path.join(output_dir, f"error_by_factory_{timestamp}.png") + plt.savefig(factory_plot_path) + plt.close() + + # Save histories for inventory, fulfillment pct, unmet backlog + inv_path = os.path.join(output_dir, f"inventory_history_{timestamp}.csv") + fulfill_path = os.path.join(output_dir, f"fulfill_history_{timestamp}.csv") + unmet_path = os.path.join(output_dir, f"unmet_history_{timestamp}.csv") + if self.inv_history: + pd.DataFrame(self.inv_history).to_csv(inv_path, index=False, encoding="utf-8-sig") + if self.fulfill_history_cum: + pd.DataFrame(self.fulfill_history_cum).to_csv(fulfill_path, index=False, encoding="utf-8-sig") + if self.unmet_history: + pd.DataFrame(self.unmet_history).to_csv(unmet_path, index=False, encoding="utf-8-sig") + + if self.assignment_log: + assign_path = os.path.join(output_dir, f"assignments_{timestamp}.csv") + pd.DataFrame(self.assignment_log).to_csv(assign_path, index=False, encoding="utf-8-sig") + + if self.fulfill_overall_history: + overall_path = os.path.join(output_dir, f"fulfill_overall_history_{timestamp}.csv") + pd.DataFrame(self.fulfill_overall_history).to_csv(overall_path, index=False, encoding="utf-8-sig") + if self.transport_cost_history: + cost_path = os.path.join(output_dir, f"transport_cost_history_{timestamp}.csv") + pd.DataFrame(self.transport_cost_history).to_csv(cost_path, index=False, encoding="utf-8-sig") + if self.fulfill_history_month: + pd.DataFrame(self.fulfill_history_month).to_csv( + os.path.join(output_dir, f"fulfill_month_history_{timestamp}.csv"), index=False, encoding="utf-8-sig" + ) + if self.transport_units_history: + pd.DataFrame(self.transport_units_history).to_csv( + os.path.join(output_dir, f"transport_units_history_{timestamp}.csv"), index=False, encoding="utf-8-sig" + ) + + def _update_monthly_fulfill(self): + # Per-region fulfillment percentage for current month + region_pct = {} + for region in self.demand_regions: + # pct computed during allocation + pct = self.region_fulfill_pct.get(region, 0) + region_pct[region] = pct + self.region_fulfill_pct = region_pct + self.region_unmet_backlog = { + self.city_to_region.get(str(city).strip()): self.region_unmet_backlog.get(self.city_to_region.get(str(city).strip()), 0) + for city in [] + } + + def _record_allocation_report(self, month, inventory_before, demand_before, shipped): + products = self.product_list or sorted(set(inventory_before) | set(demand_before) | set(shipped)) + for product in products: + before_inv = float(inventory_before.get(product, 0) or 0) + before_dem = float(demand_before.get(product, 0) or 0) + shipped_qty = float(shipped.get(product, 0) or 0) + after_inv = max(before_inv - shipped_qty, 0) + after_dem = max(before_dem - shipped_qty, 0) + self.monthly_allocation_summary.append( + { + "month": month, + "product": product, + "inventory_before": before_inv, + "demand_before": before_dem, + "allocated": shipped_qty, + "inventory_after": after_inv, + "demand_after": after_dem, + } + ) + + def _run_allocation(self): + # reset transport count for current month + self.region_transport_month = {r: 0 for r in self.demand_regions} + # Supply from line unit_stock (assembled units) + supplies = {} + inventory_before = {} + for agent in self.agents: + if not isinstance(agent, ProductionLineAgent): + continue + for prod, units in agent.unit_stock.items(): + qty = int(units) + if qty > 0: + supplies[(agent.line_id, str(prod).strip())] = qty + inventory_before[str(prod).strip()] = inventory_before.get(str(prod).strip(), 0) + qty + # Demand per city-product for current month including backlog + demands = {} + region_month_demand = {} + demand_before = {} + for (city, prod), monthly in self.demand_by_city_product.items(): + current = monthly.get(self.current_month, 0) + backlog = self.backlog_city_prod.get((city, prod), 0) + qty = backlog + current + if qty > 0: + key = (city, str(prod).strip()) + demands[key] = qty + region = self.city_to_region.get(str(city).strip()) + if region: + region_month_demand[region] = region_month_demand.get(region, 0) + qty + demand_before[str(prod).strip()] = demand_before.get(str(prod).strip(), 0) + qty + if not supplies: + self._record_allocation_report(self.current_month, inventory_before, demand_before, {}) + self.monthly_transport_cost = 0.0 + return + if not demands: + self.region_fulfill_pct = {r: 0 for r in self.demand_regions} + self._record_allocation_report(self.current_month, inventory_before, demand_before, {}) + self.monthly_transport_cost = 0.0 + return + + prob = pulp.LpProblem("allocation", pulp.LpMinimize) + x = {} + for (line, prod), supply in supplies.items(): + factory = self.line_factory.get(line) + for (city, dprod), dem in demands.items(): + if dprod != prod: + continue + if self.within_region_only: + demand_region = self.city_to_region.get(str(city).strip()) + line_region = self.line_region.get(line) + if demand_region is not None and line_region is not None and demand_region != line_region: + continue + x[(line, city, prod)] = pulp.LpVariable(f"x_{line}_{city}_{prod}", lowBound=0, cat="Integer") + + shortage = { + (city, prod): pulp.LpVariable(f"s_{city}_{prod}", lowBound=0, cat="Continuous") + for (city, prod) in demands + } + + # Objective: transport cost + penalty on shortage + obj_terms = [] + shortage_penalty = self.big_M * 10 # Penalize shortage higher than any transport cost + for (line, city, prod), var in x.items(): + factory = self.line_factory.get(line) + factory_region = self.line_region.get(line.strip()).strip() + demand_region = self.city_to_region.get(str(city).strip()).strip() + if "国际" in factory_region or "国际" in demand_region: + cost = 0 + else: + cost = self._cost_per_unit(factory, city, prod) + obj_terms.append(cost * var) + obj_terms += [shortage_penalty * s for s in shortage.values()] + prob += pulp.lpSum(obj_terms) + + # Supply constraints + for (line, prod), supply in supplies.items(): + prob += pulp.lpSum(var for (l, c, p), var in x.items() if l == line and p == prod) <= supply + + # Demand soft constraints + for (city, prod), dem in demands.items(): + prob += pulp.lpSum(var for (l, c, p), var in x.items() if c == city and p == prod) + shortage[(city, prod)] >= dem + + status = prob.solve(pulp.PULP_CBC_CMD(msg=False)) + if pulp.LpStatus[status] != "Optimal": + self.monthly_transport_cost = 0.0 + self._record_allocation_report(self.current_month, inventory_before, demand_before, {}) + return + + # Update inventories and backlogs + shipped = {} + shipped_city_prod = {} + shipped_product = {} + for (line, city, prod), var in x.items(): + qty = int(pulp.value(var) or 0) + if qty > 0: + shipped[(line, prod)] = shipped.get((line, prod), 0) + qty + shipped_city_prod[(city, prod)] = shipped_city_prod.get((city, prod), 0) + qty + shipped_product[prod] = shipped_product.get(prod, 0) + qty + # update demand fulfillment by region of demand + region = self.city_to_region.get(str(city).strip()) + if region: + self.region_demand_fulfilled[region] = self.region_demand_fulfilled.get(region, 0) + qty + if self.output_enabled: + factory_region = self.line_region.get(line) + demand_region = self.city_to_region.get(str(city).strip()) + distance_km = float(self.distance_lookup.get((factory, city), 0) or 0) + if factory_region == "国际" or demand_region == "国际": + unit_cost = 0.0 + else: + unit_cost = float(self._cost_per_unit(factory, city, prod) or 0.0) + assign_cost = (unit_cost * qty) / 10000.0 + self.assignment_log.append( + { + "month": self.current_month, + "line_id": line, + "product": prod, + "demand_city": city, + "quantity": qty, + "transport_distance_km": distance_km, + "transport_cost_wan": assign_cost, + } + ) + if shipped: + for agent in self.agents: + if isinstance(agent, ProductionLineAgent): + for (l, p), qty in shipped.items(): + if agent.line_id == l and p in agent.unit_stock: + agent.unit_stock[p] = max(0, agent.unit_stock[p] - qty) + + # Update backlogs and region fulfillment + region_shipped = {} + total_shipped = 0 + for (city, prod), qty in shipped_city_prod.items(): + region = self.city_to_region.get(str(city).strip()) + if region: + region_shipped[region] = region_shipped.get(region, 0) + qty + total_shipped += qty + + region_unmet = {} + region_pct_month = {} + region_pct_cum = {} + region_transport_month = {r: 0 for r in self.demand_regions} + for (city, prod), demand in demands.items(): + shipped_qty = shipped_city_prod.get((city, prod), 0) + remaining = max(0, demand - shipped_qty) + self.backlog_city_prod[(city, prod)] = remaining + region = self.city_to_region.get(str(city).strip()) + if region: + region_unmet[region] = region_unmet.get(region, 0) + remaining + for region, qty in region_shipped.items(): + region_transport_month[region] = qty + + for region in self.demand_regions: + total = region_month_demand.get(region, 0) + total_current = sum( + monthly.get(self.current_month, 0) + for (city, prod), monthly in self.demand_by_city_product.items() + if self.city_to_region.get(str(city).strip()) == region + ) + fulfilled = region_shipped.get(region, 0) + pct_cum = (fulfilled / total) * 100 if total > 0 else 0.0 + pct_month = (fulfilled / total_current) * 100 if total_current > 0 else 0.0 + region_pct_cum[region] = pct_cum + region_pct_month[region] = pct_month + + self.region_fulfill_pct = region_pct_cum + self.region_fulfill_pct_month = region_pct_month + self.region_fulfill_pct_cum = region_pct_cum + self.region_unmet_backlog = region_unmet + self.region_transport_month = region_transport_month + # Overall fulfillment and cost + total_demand_all = sum(region_month_demand.values()) + self.overall_fulfill_pct = (total_shipped / total_demand_all) * 100 if total_demand_all > 0 else 0.0 + # Compute realized transport cost directly from allocation results (exclude shortage penalties) + total_transport_cost = 0.0 + for (line, city, prod), var in x.items(): + qty = int(pulp.value(var) or 0) + if qty <= 0: + continue + factory = self.line_factory.get(line) + factory_region = self.line_region.get(line) + demand_region = self.city_to_region.get(str(city).strip()) + if factory_region == "国际" or demand_region == "国际": + unit_cost = 0.0 + else: + unit_cost = float(self._cost_per_unit(factory, city, prod) or 0.0) + total_transport_cost += unit_cost * qty + self.monthly_transport_cost = total_transport_cost / 10000.0 + self._record_allocation_report(self.current_month, inventory_before, demand_before, shipped_product) + + def _update_region_inventory(self): + region_inv = {} + for agent in self.agents: + if isinstance(agent, ProductionLineAgent): + total_units = sum(agent.unit_stock.values()) + region_inv[agent.region] = region_inv.get(agent.region, 0) + total_units + self.region_alloc_inventory = region_inv + + def _record_histories(self): + self.inv_history.append({"month": self.current_month, **self.region_alloc_inventory}) + self.fulfill_history_month.append({"month": self.current_month, **getattr(self, "region_fulfill_pct_month", {})}) + self.fulfill_history_cum.append({"month": self.current_month, **getattr(self, "region_fulfill_pct_cum", {})}) + self.unmet_history.append({"month": self.current_month, **self.region_unmet_backlog}) + self.fulfill_overall_history.append({"month": self.current_month, "fulfill_pct_overall": self.overall_fulfill_pct}) + self.transport_cost_history.append({"month": self.current_month, "transport_cost": self.monthly_transport_cost}) + self.transport_units_history.append({"month": self.current_month, **self.region_transport_month}) + + +if __name__ == "__main__": + model = SimulationModel(output_enabled=True) + while model.running: + model.step()