模型修改基本结束,到达模型开始执行风险传递处
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@ -3,6 +3,13 @@
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<component name="CsvFileAttributes">
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<option name="attributeMap">
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<map>
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<entry key="\input_data\BomNodes.csv">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="\input_data\Firm_amended.csv">
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<value>
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<Attribute>
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@ -10,6 +17,20 @@
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</Attribute>
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</value>
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</entry>
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<entry key="\input_data\oa_with_exp.csv">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="\input_data\oa_without_exp.csv">
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<value>
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<Attribute>
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<option name="separator" value="," />
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</Attribute>
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</value>
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</entry>
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<entry key="\input_data\xv_with_exp.csv">
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<value>
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<Attribute>
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@ -5,7 +5,7 @@ from mesa import Model
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from typing import TYPE_CHECKING
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from model import MyModel
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from my_model import MyModel
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if TYPE_CHECKING:
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from controller_db import ControllerDB
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13
firm.py
13
firm.py
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@ -1,5 +1,5 @@
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from mesa import Agent
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from model import MyModel
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import numpy as np
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class FirmAgent(Agent):
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@ -47,17 +47,18 @@ class FirmAgent(Agent):
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"cap_limit_prob_type must be either 'uniform' or 'normal'"
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extra_cap_mean = self.size_stat[0][0] / self.flt_cap_limit_level
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if self.str_cap_limit_prob_type == 'uniform':
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extra_cap = self.model.random.randint(extra_cap_mean - 2, extra_cap_mean + 2)
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extra_cap = self.model.random.uniform(extra_cap_mean - 2, extra_cap_mean + 2)
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extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
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elif self.str_cap_limit_prob_type == 'normal':
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extra_cap = self.model.random.normalvariate(extra_cap_mean, 1)
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extra_cap = max(0, round(extra_cap))
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extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
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self.dct_prod_capacity[product] = extra_cap
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def remove_edge_to_cus(self, disrupted_prod):
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lst_out_edge = list(
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self.firm_network.get_neighbors(self.unique_id))
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self.firm_network.neighbors(self.unique_id))
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for n2 in lst_out_edge:
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edge_data = self.firm_network.G.edges[self.unique_id, n2]
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edge_data = self.firm_network.edges[self.unique_id, n2]
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if edge_data.get('Product') == disrupted_prod.code:
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customer = self.model.schedule.get_agent(n2)
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for prod in customer.dct_prod_up_prod_stat.keys():
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@ -69,7 +70,7 @@ class FirmAgent(Agent):
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def disrupt_cus_prod(self, prod, disrupted_up_prod):
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num_lost = len(self.dct_prod_up_prod_stat[prod]['s_stat'][disrupted_up_prod]['set_disrupt_firm'])
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num_remain = len([
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u for u in self.firm_network.get_neighbors(self.unique_id)
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u for u in self.firm_network.neighbors(self.unique_id)
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if self.firm_network.G.edges[u, self.unique_id].get('Product') == disrupted_up_prod.code])
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lost_percent = num_lost / (num_lost + num_remain)
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lst_size = [firm.size_stat[-1][0] for firm in self.model.schedule.agents]
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@ -1,3 +1,2 @@
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X1,X2,X3,X4,X5,X6,X7,X8
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0,0,0,0,0,0,0,0
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,,,,,,,
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@ -1,3 +1,2 @@
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n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,remove_t,netw_prf_n
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5,TRUE,TRUE,uniform,10,0.5,5,2
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,,,,,,,
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@ -16,17 +16,20 @@ 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.is_prf_size = params['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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self.firm_network = nx.MultiDiGraph() # 有向多重图
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self.firm_prod_network = nx.MultiDiGraph()
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self.product_network = nx.MultiDiGraph() # 有向多重图
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# NetworkGrid 用于管理网格
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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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@ -48,20 +51,21 @@ class MyModel(Model):
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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.add_edges_to_firm_network()
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self.connect_unconnected_nodes()
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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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try:
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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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print(
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f"Product network initialized with {self.product_network.number_of_nodes()} nodes and {self.product_network.number_of_edges()} edges.")
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except Exception as e:
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print(f"Failed to initialize product network: {e}")
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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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@ -98,6 +102,11 @@ class MyModel(Model):
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self.add_edges_to_firm_network()
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self.connect_unconnected_nodes()
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print(
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f"Firm network has {self.firm_network.number_of_nodes()} nodes and {self.firm_network.number_of_edges()} edges.")
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print(
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f"Product network has {self.product_network.number_of_nodes()} nodes and {self.product_network.number_of_edges()} edges.")
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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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@ -176,8 +185,11 @@ class MyModel(Model):
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self.add_agent(product)
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# self.grid.place_agent(product, ag_node)
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##print(f"Product agent created: {product.name}, ID: {product.unique_id}")
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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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print(a_lst_product)
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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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@ -185,16 +197,37 @@ class MyModel(Model):
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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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##print(f"Firm agent created: {firm_agent.unique_id}, Products: {[p.name for p in a_lst_product]}")
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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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# 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
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t_dct = {}
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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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# 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
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for firm_code, lst_product in self.dct_lst_init_disrupt_firm_prod.items():
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# 从 company_agents 列表中选择指定公司
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firm = next(firm for firm in self.company_agents if firm.unique_id == firm_code)
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# 从总产品列表中选择该公司受干扰的产品
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disrupted_products = [product for product in self.product_agents if product.unique_id in lst_product]
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# 将公司与其受干扰的产品映射到字典中
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t_dct[firm] = disrupted_products
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# 更新 self.dct_lst_init_disrupt_firm_prod 字典,存储公司及其受干扰的产品
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self.dct_lst_init_disrupt_firm_prod = t_dct
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# 设置初始受干扰的公司产品状态
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for firm, a_lst_product in self.dct_lst_init_disrupt_firm_prod.items():
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for product in a_lst_product:
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# 确保产品存在于公司的生产状态字典中
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assert product in firm.dct_prod_up_prod_stat.keys(), \
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f"Product {product.code} not in firm {firm.code}"
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# 将产品状态更新为干扰状态,并记录干扰时间
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firm.dct_prod_up_prod_stat[product]['p_stat'].append(('D', self.t))
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def add_agent(self, agent):
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if isinstance(agent, FirmAgent):
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19
product.py
19
product.py
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@ -10,14 +10,19 @@ class ProductAgent(Agent):
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self.product_network = self.model.product_network
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def a_successors(self):
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# Find successors of the current product and return them as a list of ProductAgent
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successors = list(self.product_network.successors(self))
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return [self.model.schedule.agents[successor] for successor in successors]
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# 从 product_network 中找到当前代理的后继节点
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successors = list(self.model.product_network.successors(self.unique_id))
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# 通过 unique_id 查找后继节点对应的代理对象,从 self.product_agents 中获取
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return [agent for agent in self.model.product_agents if agent.unique_id in successors]
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def a_predecessors(self):
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# Find predecessors of the current product and return them as a list of ProductAgent
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predecessors = list(self.product_network.predecessors(self))
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return [self.model.schedule.agents[predecessor] for predecessor in predecessors]
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# 找到当前代理的前驱节点
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predecessors = list(self.model.product_network.predecessors(self.unique_id))
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# 通过 unique_id 查找前驱节点对应的代理对象,直接从 self.product_agents 列表中获取
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return [agent for agent in self.model.product_agents if agent.unique_id in predecessors]
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def step(self):
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# 在每个时间步进行的操作
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# 在每个时间步 进行的操作
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pass
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