模型修改基本结束，到达模型开始执行风险传递处

.idea/csv-editor.xml

@@ -3,6 +3,13 @@
   <component name="CsvFileAttributes">
     <option name="attributeMap">
       <map>
+        <entry key="\input_data\BomNodes.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
         <entry key="\input_data\Firm_amended.csv">
           <value>
             <Attribute>
@@ -10,6 +17,20 @@
             </Attribute>
           </value>
         </entry>
+        <entry key="\input_data\oa_with_exp.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+        <entry key="\input_data\oa_without_exp.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
         <entry key="\input_data\xv_with_exp.csv">
           <value>
             <Attribute>

computation.py

@@ -5,7 +5,7 @@ from mesa import Model
 
 from typing import TYPE_CHECKING
 
-from model import MyModel
+from my_model import MyModel
 
 if TYPE_CHECKING:
     from controller_db import ControllerDB

firm.py

@@ -1,5 +1,5 @@
 from mesa import Agent
-from model import MyModel
+import numpy as np
 
 
 class FirmAgent(Agent):
@@ -47,17 +47,18 @@ class FirmAgent(Agent):
                 "cap_limit_prob_type must be either 'uniform' or 'normal'"
             extra_cap_mean = self.size_stat[0][0] / self.flt_cap_limit_level
             if self.str_cap_limit_prob_type == 'uniform':
-                extra_cap = self.model.random.randint(extra_cap_mean - 2, extra_cap_mean + 2)
+                extra_cap = self.model.random.uniform(extra_cap_mean - 2, extra_cap_mean + 2)
+                extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
             elif self.str_cap_limit_prob_type == 'normal':
                 extra_cap = self.model.random.normalvariate(extra_cap_mean, 1)
-            extra_cap = max(0, round(extra_cap))
+                extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
             self.dct_prod_capacity[product] = extra_cap
 
     def remove_edge_to_cus(self, disrupted_prod):
         lst_out_edge = list(
-            self.firm_network.get_neighbors(self.unique_id))
+            self.firm_network.neighbors(self.unique_id))
         for n2 in lst_out_edge:
-            edge_data = self.firm_network.G.edges[self.unique_id, n2]
+            edge_data = self.firm_network.edges[self.unique_id, n2]
             if edge_data.get('Product') == disrupted_prod.code:
                 customer = self.model.schedule.get_agent(n2)
                 for prod in customer.dct_prod_up_prod_stat.keys():
@@ -69,7 +70,7 @@ class FirmAgent(Agent):
     def disrupt_cus_prod(self, prod, disrupted_up_prod):
         num_lost = len(self.dct_prod_up_prod_stat[prod]['s_stat'][disrupted_up_prod]['set_disrupt_firm'])
         num_remain = len([
-            u for u in self.firm_network.get_neighbors(self.unique_id)
+            u for u in self.firm_network.neighbors(self.unique_id)
             if self.firm_network.G.edges[u, self.unique_id].get('Product') == disrupted_up_prod.code])
         lost_percent = num_lost / (num_lost + num_remain)
         lst_size = [firm.size_stat[-1][0] for firm in self.model.schedule.agents]

input_data/oa_without_exp.csv

@@ -1,3 +1,2 @@
 X1,X2,X3,X4,X5,X6,X7,X8
 0,0,0,0,0,0,0,0
-,,,,,,,

input_data/xv_without_exp.csv

@@ -1,3 +1,2 @@
 n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,remove_t,netw_prf_n
 5,TRUE,TRUE,uniform,10,0.5,5,2
-,,,,,,,

my_model.py

@@ -16,17 +16,20 @@ class MyModel(Model):
     def __init__(self, params):
 
         # self.num_agents = N
-        self.is_prf_size = params['is_prf_size']
+        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()
-
-        # NetworkGrid 用于管理网格
         self.grid = NetworkGrid(self.network_graph)
 
         self.data_collector = DataCollector(
@@ -48,20 +51,21 @@ class MyModel(Model):
         self.remove_t = int(params['remove_t'])
         self.int_netw_prf_n = int(params['netw_prf_n'])
 
-        self.product_network = None
-        self.firm_network = None
-        self.firm_prod_network = None
-
         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):
-        """ Initialize the product network and add it to the model. """
+        try:
             self.product_network = nx.adjacency_graph(json.loads(params['g_bom']))
-        self.network_graph.add_edges_from(self.product_network.edges)
+            print(
+                f"Product network initialized with {self.product_network.number_of_nodes()} nodes and {self.product_network.number_of_edges()} edges.")
+        except Exception as e:
+            print(f"Failed to initialize product network: {e}")
 
     def initialize_firm_network(self):
         """ Initialize the firm network and add it to the model. """
@@ -98,6 +102,11 @@ class MyModel(Model):
         self.add_edges_to_firm_network()
         self.connect_unconnected_nodes()
 
+        print(
+            f"Firm network has {self.firm_network.number_of_nodes()} nodes and {self.firm_network.number_of_edges()} edges.")
+        print(
+            f"Product network has {self.product_network.number_of_nodes()} nodes and {self.product_network.number_of_edges()} edges.")
+
     def add_edges_to_firm_network(self):
         """ Add edges to the firm network based on product BOM. """
         Firm = pd.read_csv("input_data/Firm_amended.csv")
@@ -176,8 +185,11 @@ class MyModel(Model):
             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']]
+            print(a_lst_product)
             firm_agent = FirmAgent(
                 ag_node, self,
                 type_region=attr['Type_Region'],
@@ -185,16 +197,37 @@ class MyModel(Model):
                 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):
-        """ Initialize disruptions in the network. """
-        for firm_code, lst_product in self.dct_lst_init_disrupt_firm_prod.items():
-            for product_code in lst_product:
-                self.firm_network.nodes[firm_code]['Product_Code'].remove(product_code)
+        # 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 = {}
 
-                # Log disruptions for visualization
-                self.dct_lst_init_disrupt_firm_prod[firm_code].append(product_code)
+        # 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
+            firm = next(firm for firm in self.company_agents if firm.unique_id == firm_code)
+
+            # 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):

product.py

@@ -10,14 +10,19 @@ class ProductAgent(Agent):
         self.product_network = self.model.product_network
 
     def a_successors(self):
-        # Find successors of the current product and return them as a list of ProductAgent
-        successors = list(self.product_network.successors(self))
-        return [self.model.schedule.agents[successor] for successor in successors]
+        # 从 product_network 中找到当前代理的后继节点
+        successors = list(self.model.product_network.successors(self.unique_id))
+
+        # 通过 unique_id 查找后继节点对应的代理对象，从 self.product_agents 中获取
+        return [agent for agent in self.model.product_agents if agent.unique_id in successors]
 
     def a_predecessors(self):
-        # Find predecessors of the current product and return them as a list of ProductAgent
-        predecessors = list(self.product_network.predecessors(self))
-        return [self.model.schedule.agents[predecessor] for predecessor in predecessors]
+        # 找到当前代理的前驱节点
+        predecessors = list(self.model.product_network.predecessors(self.unique_id))
+
+        # 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
+        return [agent for agent in self.model.product_agents if agent.unique_id in predecessors]
+
     def step(self):
         # 在每个时间步 进行的操作
         pass