253 lines
12 KiB
Python
253 lines
12 KiB
Python
import json
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from random import shuffle
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import networkx as nx
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import pandas as pd
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from mesa import Model
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from mesa.space import MultiGrid, NetworkGrid
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from mesa.datacollection import DataCollector
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from mesa.time import RandomActivation
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from firm import FirmAgent
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from product import ProductAgent
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class MyModel(Model):
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def __init__(self, params):
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# self.num_agents = N
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self.is_prf_size = params['is_prf_size']
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self.prf_conn = params['prf_conn']
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self.cap_limit_prob_type = params['cap_limit_prob_type']
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self.cap_limit_level = params['cap_limit_level']
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self.diff_new_conn = params['diff_new_conn']
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# NetworkX 图对象
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self.t = 0
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self.network_graph = nx.MultiDiGraph()
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# NetworkGrid 用于管理网格
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self.grid = NetworkGrid(self.network_graph)
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self.data_collector = DataCollector(
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agent_reporters={"Product": "name"}
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)
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self.company_agents = []
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self.product_agents = []
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# Initialize parameters from `params`
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self.sample = params['sample']
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self.int_stop_ts = 0
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self.int_n_iter = int(params['n_iter'])
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self.dct_lst_init_disrupt_firm_prod = params['dct_lst_init_disrupt_firm_prod']
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# external variable
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self.int_n_max_trial = int(params['n_max_trial'])
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self.is_prf_size = bool(params['prf_size'])
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self.remove_t = int(params['remove_t'])
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self.int_netw_prf_n = int(params['netw_prf_n'])
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self.product_network = None
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self.firm_network = None
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self.firm_prod_network = None
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self.initialize_product_network(params)
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self.initialize_firm_network()
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self.initialize_firm_product_network()
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self.initialize_agents()
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self.initialize_disruptions()
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def initialize_product_network(self, params):
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""" Initialize the product network and add it to the model. """
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self.product_network = nx.adjacency_graph(json.loads(params['g_bom']))
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self.network_graph.add_edges_from(self.product_network.edges)
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def initialize_firm_network(self):
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""" Initialize the firm network and add it to the model. """
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Firm = pd.read_csv("input_data/Firm_amended.csv")
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Firm['Code'] = Firm['Code'].astype('string')
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Firm.fillna(0, inplace=True)
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Firm_attr = Firm.loc[:, ["Code", "Type_Region", "Revenue_Log"]]
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firm_product = [row[row == 1].index.to_list() for _, row in Firm.loc[:, '1':].iterrows()]
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Firm_attr.loc[:, 'Product_Code'] = firm_product
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Firm_attr.set_index('Code', inplace=True)
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self.firm_network = nx.MultiDiGraph()
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self.firm_network.add_nodes_from(Firm["Code"])
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firm_labels_dict = {code: Firm_attr.loc[code].to_dict() for code in self.firm_network.nodes}
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nx.set_node_attributes(self.firm_network, firm_labels_dict)
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def initialize_firm_product_network(self):
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""" Initialize the firm-product network and add it to the model. """
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Firm_Prod = pd.read_csv("input_data/Firm_amended.csv")
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Firm_Prod.fillna(0, inplace=True)
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firm_prod = pd.DataFrame({'bool': Firm_Prod.loc[:, '1':].stack()})
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firm_prod = firm_prod[firm_prod['bool'] == 1].reset_index()
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firm_prod.drop('bool', axis=1, inplace=True)
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firm_prod.rename({'level_0': 'Firm_Code', 'level_1': 'Product_Code'}, axis=1, inplace=True)
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firm_prod['Firm_Code'] = firm_prod['Firm_Code'].astype('string')
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self.firm_prod_network = nx.MultiDiGraph()
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self.firm_prod_network.add_nodes_from(firm_prod.index)
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firm_prod_labels_dict = {code: firm_prod.loc[code].to_dict() for code in firm_prod.index}
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nx.set_node_attributes(self.firm_prod_network, firm_prod_labels_dict)
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self.add_edges_to_firm_network()
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self.connect_unconnected_nodes()
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def add_edges_to_firm_network(self):
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""" Add edges to the firm network based on product BOM. """
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Firm = pd.read_csv("input_data/Firm_amended.csv")
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Firm['Code'] = Firm['Code'].astype('string')
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Firm.fillna(0, inplace=True)
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for node in nx.nodes(self.firm_network):
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lst_pred_product_code = []
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for product_code in self.firm_network.nodes[node]['Product_Code']:
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lst_pred_product_code += list(self.product_network.predecessors(product_code))
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lst_pred_product_code = list(set(lst_pred_product_code))
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lst_pred_product_code = list(sorted(lst_pred_product_code))
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for pred_product_code in lst_pred_product_code:
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lst_pred_firm = Firm['Code'][Firm[pred_product_code] == 1].to_list()
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n_pred_firm = self.int_netw_prf_n
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if n_pred_firm > len(lst_pred_firm):
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n_pred_firm = len(lst_pred_firm)
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if self.is_prf_size:
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lst_pred_firm_size = [self.firm_network.nodes[pred_firm]['Revenue_Log'] for pred_firm in
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lst_pred_firm]
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lst_prob = [size / sum(lst_pred_firm_size) for size in lst_pred_firm_size]
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lst_choose_firm = self.random.choices(lst_pred_firm, k=n_pred_firm, weights=lst_prob)
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else:
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lst_choose_firm = self.random.choices(lst_pred_firm, k=n_pred_firm)
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lst_add_edge = [(pred_firm, node, {'Product': pred_product_code}) for pred_firm in lst_choose_firm]
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self.firm_network.add_edges_from(lst_add_edge)
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# Add edges to firm-prod network
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set_node_prod_code = set(self.firm_network.nodes[node]['Product_Code'])
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set_pred_succ_code = set(self.product_network.successors(pred_product_code))
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lst_use_pred_prod_code = list(set_node_prod_code & set_pred_succ_code)
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for pred_firm in lst_choose_firm:
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pred_node = [n for n, v in self.firm_prod_network.nodes(data=True) if
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v['Firm_Code'] == pred_firm and v['Product_Code'] == pred_product_code][0]
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for use_pred_prod_code in lst_use_pred_prod_code:
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current_node = [n for n, v in self.firm_prod_network.nodes(data=True) if
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v['Firm_Code'] == node and v['Product_Code'] == use_pred_prod_code][0]
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self.firm_prod_network.add_edge(pred_node, current_node)
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def connect_unconnected_nodes(self):
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""" Connect unconnected nodes in the firm network. """
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Firm = pd.read_csv("input_data/Firm_amended.csv")
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Firm['Code'] = Firm['Code'].astype('string')
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Firm.fillna(0, inplace=True)
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for node in nx.nodes(self.firm_network):
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if self.firm_network.degree(node) == 0:
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for product_code in self.firm_network.nodes[node]['Product_Code']:
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current_node = [n for n, v in self.firm_prod_network.nodes(data=True) if
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v['Firm_Code'] == node and v['Product_Code'] == product_code][0]
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lst_succ_product_code = list(self.product_network.successors(product_code))
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for succ_product_code in lst_succ_product_code:
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lst_succ_firm = Firm['Code'][Firm[succ_product_code] == 1].to_list()
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n_succ_firm = self.int_netw_prf_n
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if n_succ_firm > len(lst_succ_firm):
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n_succ_firm = len(lst_succ_firm)
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if self.is_prf_size:
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lst_succ_firm_size = [self.firm_network.nodes[succ_firm]['Revenue_Log'] for succ_firm in
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lst_succ_firm]
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lst_prob = [size / sum(lst_succ_firm_size) for size in lst_succ_firm_size]
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lst_choose_firm = self.random.choices(lst_succ_firm, k=n_succ_firm, weights=lst_prob)
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else:
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lst_choose_firm = self.random.choices(lst_succ_firm, k=n_succ_firm)
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lst_add_edge = [(node, succ_firm, {'Product': product_code}) for succ_firm in lst_choose_firm]
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self.firm_network.add_edges_from(lst_add_edge)
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for succ_firm in lst_choose_firm:
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succ_node = [n for n, v in self.firm_prod_network.nodes(data=True) if
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v['Firm_Code'] == succ_firm and v['Product_Code'] == succ_product_code][0]
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self.firm_prod_network.add_edge(current_node, succ_node)
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def initialize_agents(self):
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""" Initialize agents and add them to the model. """
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for ag_node, attr in self.product_network.nodes(data=True):
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product = ProductAgent(ag_node, self, name=attr['Name'])
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self.add_agent(product)
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# self.grid.place_agent(product, ag_node)
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for ag_node, attr in self.firm_network.nodes(data=True):
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a_lst_product = [agent for agent in self.product_agents if agent.unique_id in attr['Product_Code']]
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firm_agent = FirmAgent(
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ag_node, self,
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type_region=attr['Type_Region'],
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revenue_log=attr['Revenue_Log'],
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a_lst_product=a_lst_product,
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)
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self.add_agent(firm_agent)
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# self.grid.place_agent(firm_agent, ag_node)
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def initialize_disruptions(self):
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""" Initialize disruptions in the network. """
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for firm_code, lst_product in self.dct_lst_init_disrupt_firm_prod.items():
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for product_code in lst_product:
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self.firm_network.nodes[firm_code]['Product_Code'].remove(product_code)
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# Log disruptions for visualization
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self.dct_lst_init_disrupt_firm_prod[firm_code].append(product_code)
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def add_agent(self, agent):
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if isinstance(agent, FirmAgent):
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self.company_agents.append(agent)
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elif isinstance(agent, ProductAgent):
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self.product_agents.append(agent)
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def step(self):
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print(f"Running step {self.t}")
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# 1. Remove edge to customer and disrupt customer up product
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for firm in self.company_agents:
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for prod in firm.dct_prod_up_prod_stat.keys():
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status, ts = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
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if status == 'D' and ts == self.t - 1:
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firm.remove_edge_to_cus(prod)
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for firm in self.company_agents:
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for prod in firm.dct_prod_up_prod_stat.keys():
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for up_prod in firm.dct_prod_up_prod_stat[prod]['s_stat'].keys():
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if firm.dct_prod_up_prod_stat[prod]['s_stat'][up_prod]['set_disrupt_firm']:
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firm.disrupt_cus_prod(prod, up_prod)
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# 2. Trial Process
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for n_trial in range(self.int_n_max_trial):
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shuffle(self.company_agents) # 手动打乱代理顺序
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is_stop_trial = True
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for firm in self.company_agents:
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lst_seek_prod = []
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for prod in firm.dct_prod_up_prod_stat.keys():
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status = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1][0]
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if status == 'D':
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for supply in firm.dct_prod_up_prod_stat[prod]['s_stat'].keys():
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if not firm.dct_prod_up_prod_stat[prod]['s_stat'][supply]['stat']:
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lst_seek_prod.append(supply)
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lst_seek_prod = list(set(lst_seek_prod))
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if len(lst_seek_prod) > 0:
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is_stop_trial = False
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for supply in lst_seek_prod:
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firm.seek_alt_supply(supply)
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if is_stop_trial:
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break
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# Handle requests
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shuffle(self.company_agents) # 手动打乱代理顺序
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for firm in self.company_agents:
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if len(firm.dct_request_prod_from_firm) > 0:
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firm.handle_request()
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# Reset dct_request_prod_from_firm
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for firm in self.company_agents:
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firm.clean_before_trial()
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# Increment the time step
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self.t += 1
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