mesa/my_model.py

import json
from random import shuffle

import networkx as nx
import pandas as pd
from mesa import Model
from mesa.space import MultiGrid, NetworkGrid
from mesa.datacollection import DataCollector

import numpy as np
from firm import FirmAgent
from product import ProductAgent


class MyModel(Model):
    def __init__(self, params):

        # 属性
        self.is_prf_size = params['prf_size']
        self.prf_conn = params['prf_conn']
        self.cap_limit_prob_type = params['cap_limit_prob_type']
        self.cap_limit_level = params['cap_limit_level']
        self.diff_new_conn = params['diff_new_conn']

        self.firm_network = nx.MultiDiGraph()  # 有向多重图
        self.firm_prod_network = nx.MultiDiGraph()
        self.product_network = nx.MultiDiGraph()  # 有向多重图

        # NetworkGrid 用于管理网格
        # NetworkX 图对象
        self.t = 0
        self.network_graph = nx.MultiDiGraph()
        self.grid = NetworkGrid(self.network_graph)

        self.data_collector = DataCollector(
            agent_reporters={"Product": "name"}
        )

        # initialize graph bom
        self.G_bom = nx.adjacency_graph(json.loads(params['g_bom']))

        # Create the firm-product network graph
        self.G_FirmProd = nx.MultiDiGraph()
        # Create the firm network graph
        self.G_Firm = nx.MultiDiGraph()

        self.company_agents = []
        self.product_agents = []

        self.nprandom = np.random.default_rng(params['seed'])

        # Initialize parameters from `params`
        self.sample = params['sample']
        self.int_stop_ts = 0
        self.int_n_iter = int(params['n_iter'])
        self.dct_lst_init_disrupt_firm_prod = params['dct_lst_init_disrupt_firm_prod']

        # external variable
        self.int_n_max_trial = int(params['n_max_trial'])
        self.is_prf_size = bool(params['prf_size'])
        self.remove_t = int(params['remove_t'])
        self.int_netw_prf_n = int(params['netw_prf_n'])
        # 方法执行
        self.initialize_product_network(params)
        self.initialize_firm_network()
        self.initialize_firm_product_network()
        self.add_edges_to_firm_network()
        self.connect_unconnected_nodes()
        self.initialize_agents()
        self.initialize_disruptions()

    def initialize_product_network(self, params):
        try:
            self.product_network = nx.adjacency_graph(json.loads(params['g_bom']))
        except Exception as e:
            print(f"Failed to initialize product network: {e}")

    def initialize_firm_network(self):
        # Read the firm data
        firm = pd.read_csv("input_data/Firm_amended.csv")
        firm['Code'] = firm['Code'].astype('string')
        firm.fillna(0, inplace=True)
        firm_attr = firm.loc[:, ["Code", "Type_Region", "Revenue_Log"]]

        firm_product = []
        for _, row in firm.loc[:, '1':].iterrows():
            firm_product.append(row[row == 1].index.to_list())
        firm_attr.loc[:, 'Product_Code'] = firm_product
        firm_attr.set_index('Code', inplace=True)

        self.G_Firm.add_nodes_from(firm["Code"])

        # Assign attributes to the firm nodes
        firm_labels_dict = {code: firm_attr.loc[code].to_dict() for code in self.G_Firm.nodes}
        nx.set_node_attributes(self.G_Firm, firm_labels_dict)

        self.Firm = firm

    def initialize_firm_product_network(self):
        """ Initialize the firm-product network """
        # Read the firm-product data
        Firm_Prod = pd.read_csv("input_data/Firm_amended.csv")
        Firm_Prod.fillna(0, inplace=True)

        # Stack the firm-product relationships into a DataFrame
        firm_prod = pd.DataFrame({'bool': Firm_Prod.loc[:, '1':].stack()})
        firm_prod = firm_prod[firm_prod['bool'] == 1].reset_index()
        firm_prod.drop('bool', axis=1, inplace=True)
        firm_prod.rename({'level_0': 'Firm_Code', 'level_1': 'Product_Code'}, axis=1, inplace=True)
        firm_prod['Firm_Code'] = firm_prod['Firm_Code'].astype('string')

        self.G_FirmProd.add_nodes_from(firm_prod.index)
        # Assign attributes to the firm-product nodes
        firm_prod_labels_dict = {code: firm_prod.loc[code].to_dict() for code in firm_prod.index}
        nx.set_node_attributes(self.G_FirmProd, firm_prod_labels_dict)

    def add_edges_to_firm_network(self):
        """ Add edges between firms based on the product BOM relationships """
        # Add edges to G_Firm according to G_bom
        for node in nx.nodes(self.G_Firm):
            lst_pred_product_code = []
            for product_code in self.G_Firm.nodes[node]['Product_Code']:
                lst_pred_product_code += list(self.G_bom.predecessors(product_code))
            lst_pred_product_code = list(set(lst_pred_product_code))
            lst_pred_product_code = list(sorted(lst_pred_product_code))  # Ensure consistency

            for pred_product_code in lst_pred_product_code:
                # Get a list of firms producing the component (pred_product_code)
                lst_pred_firm = self.Firm['Code'][self.Firm[pred_product_code] == 1].to_list()

                # Select multiple suppliers (multi-sourcing)
                n_pred_firm = self.int_netw_prf_n
                if n_pred_firm > len(lst_pred_firm):
                    n_pred_firm = len(lst_pred_firm)

                if self.is_prf_size:
                    lst_pred_firm_size = [self.G_Firm.nodes[pred_firm]['Revenue_Log'] for pred_firm in
                                          lst_pred_firm]
                    lst_prob = [size / sum(lst_pred_firm_size) for size in lst_pred_firm_size]
                    lst_choose_firm = self.nprandom.choice(lst_pred_firm, n_pred_firm, replace=False, p=lst_prob)
                else:
                    lst_choose_firm = self.nprandom.choice(lst_pred_firm, n_pred_firm, replace=False)

                # Add edges from predecessor firms to current node (firm)
                lst_add_edge = [(pred_firm, node, {'Product': pred_product_code}) for pred_firm in lst_choose_firm]
                self.G_Firm.add_edges_from(lst_add_edge)

                # Add edges to firm-product network
                self.add_edges_to_firm_product_network(node, pred_product_code, lst_choose_firm)

    def add_edges_to_firm_product_network(self, node, pred_product_code, lst_choose_firm):
        """ Helper function to add edges to the firm-product network """
        set_node_prod_code = set(self.G_Firm.nodes[node]['Product_Code'])
        set_pred_succ_code = set(self.G_bom.successors(pred_product_code))
        lst_use_pred_prod_code = list(set_node_prod_code & set_pred_succ_code)

        for pred_firm in lst_choose_firm:
            pred_node = [n for n, v in self.G_FirmProd.nodes(data=True) if
                         v['Firm_Code'] == pred_firm and v['Product_Code'] == pred_product_code][0]
            for use_pred_prod_code in lst_use_pred_prod_code:
                current_node = [n for n, v in self.G_FirmProd.nodes(data=True) if
                                v['Firm_Code'] == node and v['Product_Code'] == use_pred_prod_code][0]
                self.G_FirmProd.add_edge(pred_node, current_node)

    def connect_unconnected_nodes(self):
        """ Connect unconnected nodes in the firm network """
        for node in nx.nodes(self.G_Firm):
            if self.G_Firm.degree(node) == 0:
                for product_code in self.G_Firm.nodes[node]['Product_Code']:
                    current_node = [n for n, v in self.G_FirmProd.nodes(data=True) if
                                    v['Firm_Code'] == node and v['Product_Code'] == product_code][0]
                    lst_succ_product_code = list(self.G_bom.successors(product_code))

                    for succ_product_code in lst_succ_product_code:
                        lst_succ_firm = self.Firm['Code'][self.Firm[succ_product_code] == 1].to_list()

                        n_succ_firm = self.int_netw_prf_n
                        if n_succ_firm > len(lst_succ_firm):
                            n_succ_firm = len(lst_succ_firm)

                        if self.is_prf_size:
                            lst_succ_firm_size = [self.G_Firm.nodes[succ_firm]['Revenue_Log'] for succ_firm in
                                                  lst_succ_firm]
                            lst_prob = [size / sum(lst_succ_firm_size) for size in lst_succ_firm_size]
                            lst_choose_firm = self.nprandom.choice(lst_succ_firm, n_succ_firm, replace=False,
                                                                   p=lst_prob)
                        else:
                            lst_choose_firm = self.nprandom.choice(lst_succ_firm, n_succ_firm, replace=False)

                        lst_add_edge = [(node, succ_firm, {'Product': product_code}) for succ_firm in
                                        lst_choose_firm]
                        self.G_Firm.add_edges_from(lst_add_edge)

                        # Add edges to firm-product network
                        for succ_firm in lst_choose_firm:
                            succ_node = [n for n, v in self.G_FirmProd.nodes(data=True) if
                                         v['Firm_Code'] == succ_firm and v['Product_Code'] == succ_product_code][0]
                            self.G_FirmProd.add_edge(current_node, succ_node)

        self.sample.g_firm = json.dumps(nx.adjacency_data(self.G_Firm))
        self.firm_network = self.G_Firm  # 直接使用 networkx 图对象
        self.firm_prod_network = self.G_FirmProd  # 直接使用 networkx 图对象

    def initialize_agents(self):
        """ Initialize agents and add them to the model. """
        for ag_node, attr in self.product_network.nodes(data=True):
            product = ProductAgent(ag_node, self, name=attr['Name'])
            self.add_agent(product)
            # self.grid.place_agent(product, ag_node)

            ##print(f"Product agent created: {product.name}, ID: {product.unique_id}")

        for ag_node, attr in self.firm_network.nodes(data=True):
            a_lst_product = [agent for agent in self.product_agents if agent.unique_id in attr['Product_Code']]
            firm_agent = FirmAgent(
                ag_node, self,
                type_region=attr['Type_Region'],
                revenue_log=attr['Revenue_Log'],
                a_lst_product=a_lst_product,
            )
            self.add_agent(firm_agent)

            ##print(f"Firm agent created: {firm_agent.unique_id}, Products: {[p.name for p in a_lst_product]}")
            # self.grid.place_agent(firm_agent, ag_node)

    def initialize_disruptions(self):
        # 初始化一部字典，用于存储每个公司及其对应的受干扰产品列表
        t_dct = {}

        # 遍历初始公司-产品干扰数据，将其转化为基于公司和产品的映射
        for firm_code, lst_product in self.dct_lst_init_disrupt_firm_prod.items():
            # 从 company_agents 列表中选择指定公司
            firms = [firm for firm in self.company_agents if firm.unique_id == firm_code]
            firm = firms[0] if firms else None

            # 从总产品列表中选择该公司受干扰的产品
            disrupted_products = [product for product in self.product_agents if product.unique_id in lst_product]

            # 将公司与其受干扰的产品映射到字典中
            t_dct[firm] = disrupted_products

        # 更新 self.dct_lst_init_disrupt_firm_prod 字典，存储公司及其受干扰的产品
        self.dct_lst_init_disrupt_firm_prod = t_dct

        # 设置初始受干扰的公司产品状态
        for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items():
            for product in a_lst_product:
                # 确保产品存在于公司的生产状态字典中
                assert product in firm.dct_prod_up_prod_stat.keys(), \
                    f"Product {product.code} not in firm {firm.code}"

                # 将产品状态更新为干扰状态，并记录干扰时间
                firm.dct_prod_up_prod_stat[product]['p_stat'].append(('D', self.t))

    def add_agent(self, agent):
        if isinstance(agent, FirmAgent):
            self.company_agents.append(agent)
        elif isinstance(agent, ProductAgent):
            self.product_agents.append(agent)

    def step(self):
        # 1. Remove edge to customer and disrupt customer up product
        for firm in self.company_agents:
            for prod in firm.dct_prod_up_prod_stat.keys():
                status, ts = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
                if status == 'D' and ts == self.t - 1:
                    firm.remove_edge_to_cus(prod)

        for firm in self.company_agents:
            for prod in firm.dct_prod_up_prod_stat.keys():
                for up_prod in firm.dct_prod_up_prod_stat[prod]['s_stat'].keys():
                    if firm.dct_prod_up_prod_stat[prod]['s_stat'][up_prod]['set_disrupt_firm']:
                        firm.disrupt_cus_prod(prod, up_prod)

        # 2. Trial Process
        for n_trial in range(self.int_n_max_trial):
            shuffle(self.company_agents)  # 手动打乱代理顺序

            is_stop_trial = True
            for firm in self.company_agents:
                lst_seek_prod = []
                for prod in firm.dct_prod_up_prod_stat.keys():
                    status = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1][0]
                    if status == 'D':
                        for supply in firm.dct_prod_up_prod_stat[prod]['s_stat'].keys():
                            if not firm.dct_prod_up_prod_stat[prod]['s_stat'][supply]['stat']:
                                lst_seek_prod.append(supply)
                lst_seek_prod = list(set(lst_seek_prod))
                if len(lst_seek_prod) > 0:
                    is_stop_trial = False
                for supply in lst_seek_prod:
                    firm.seek_alt_supply(supply)
            if is_stop_trial:
                break

            # Handle requests
            shuffle(self.company_agents)  # 手动打乱代理顺序
            for firm in self.company_agents:
                if len(firm.dct_request_prod_from_firm) > 0:
                    firm.handle_request()

            # Reset dct_request_prod_from_firm
            for firm in self.company_agents:
                firm.clean_before_trial()

        # Increment the time step
        self.t += 1