481 lines
23 KiB
Python
481 lines
23 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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import numpy as np
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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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# 属性
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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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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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# initialize graph bom
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self.G_bom = nx.adjacency_graph(json.loads(params['g_bom']))
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# Create the firm-product network graph
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self.G_FirmProd = nx.MultiDiGraph()
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# Create the firm network graph
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self.G_Firm = nx.MultiDiGraph()
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self.company_agents = []
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self.product_agents = []
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self.nprandom = np.random.default_rng(params['seed'])
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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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# 方法执行
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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.resource_integration()
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self.j_comp_consumed_produced()
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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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try:
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self.product_network = nx.adjacency_graph(json.loads(params['g_bom']))
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except Exception as e:
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print(f"Failed to initialize product network: {e}")
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# 赋予 产业的量
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# 产业种类
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data = pd.read_csv('input_data/测试 BomNodes.csv')
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data['Code'] = data['Code'].astype('string')
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self.type2 = data
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# 设备c折旧比值
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###
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def initialize_firm_network(self):
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# Read the firm data
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firm = pd.read_csv("input_data/测试 Firm_amended 170.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", "原材料", "设备数量", "库存商品"]]
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firm_industry_relation = pd.read_csv("input_data/firm_industry_relation.csv")
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firm_industry_relation['Firm_Code'] = firm_industry_relation['Firm_Code'].astype('string')
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firm_product = []
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grouped = firm_industry_relation.groupby('Firm_Code')['Product_Code'].apply(list)
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firm_product.append(grouped)
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firm_attr['Product_Code'] = firm_attr['Code'].map(grouped)
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firm_attr.set_index('Code', inplace=True)
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self.G_Firm.add_nodes_from(firm["Code"])
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# Assign attributes to the firm nodes
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firm_labels_dict = {code: firm_attr.loc[code].to_dict() for code in self.G_Firm.nodes}
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nx.set_node_attributes(self.G_Firm, firm_labels_dict)
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self.Firm = firm
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def initialize_firm_product_network(self):
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firm_industry_relation = pd.read_csv("input_data/firm_industry_relation.csv")
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firm_industry_relation['Firm_Code'] = firm_industry_relation['Firm_Code'].astype('string')
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# 将 'firm_prod' 表中的每一行作为图中的节点
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self.G_FirmProd.add_nodes_from(firm_industry_relation.index)
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# 为每个节点分配属性
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grouped = firm_industry_relation.groupby('Firm_Code')
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self.firm_prod_labels_dict = {code: group['Product_Code'].tolist() for code, group in grouped}
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firm_prod_labels_dict = {code: firm_industry_relation.loc[code].to_dict() for code in
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firm_industry_relation.index}
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nx.set_node_attributes(self.G_FirmProd, firm_prod_labels_dict)
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def add_edges_to_firm_network(self):
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""" Add edges between firms based on the product BOM relationships """
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# Add edges to G_Firm according to G_bom
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for node in nx.nodes(self.G_Firm):
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lst_pred_product_code = []
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for product_code in self.G_Firm.nodes[node]['Product_Code']:
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lst_pred_product_code += list(self.G_bom.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)) # Ensure consistency
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for pred_product_code in lst_pred_product_code:
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# Get a list of firms producing the component (pred_product_code)
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lst_pred_firm = [firm_code for firm_code, product in self.firm_prod_labels_dict.items() if
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pred_product_code in product]
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# Select multiple suppliers (multi-sourcing)
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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.G_Firm.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.nprandom.choice(lst_pred_firm, n_pred_firm, replace=False, p=lst_prob)
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else:
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lst_choose_firm = self.nprandom.choice(lst_pred_firm, n_pred_firm, replace=False)
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# Add edges from predecessor firms to current node (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.G_Firm.add_edges_from(lst_add_edge)
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# Add edges to firm-product network
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self.add_edges_to_firm_product_network(node, pred_product_code, lst_choose_firm)
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def add_edges_to_firm_product_network(self, node, pred_product_code, lst_choose_firm):
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""" Helper function to add edges to the firm-product network """
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set_node_prod_code = set(self.G_Firm.nodes[node]['Product_Code'])
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set_pred_succ_code = set(self.G_bom.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.G_FirmProd.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.G_FirmProd.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.G_FirmProd.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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for node in nx.nodes(self.G_Firm):
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if self.G_Firm.degree(node) == 0:
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for product_code in self.G_Firm.nodes[node]['Product_Code']:
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current_node = [n for n, v in self.G_FirmProd.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.G_bom.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 for firm_code, product in self.firm_prod_labels_dict.items() if
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succ_product_code in product]
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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.G_Firm.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.nprandom.choice(lst_succ_firm, n_succ_firm, replace=False,
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p=lst_prob)
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else:
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lst_choose_firm = self.nprandom.choice(lst_succ_firm, n_succ_firm, replace=False)
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lst_add_edge = [(node, succ_firm, {'Product': product_code}) for succ_firm in
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lst_choose_firm]
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self.G_Firm.add_edges_from(lst_add_edge)
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# Add edges to firm-product network
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for succ_firm in lst_choose_firm:
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succ_node = [n for n, v in self.G_FirmProd.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.G_FirmProd.add_edge(current_node, succ_node)
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self.sample.g_firm = json.dumps(nx.adjacency_data(self.G_Firm))
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self.firm_network = self.G_Firm # 直接使用 networkx 图对象
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self.firm_prod_network = self.G_FirmProd # 直接使用 networkx 图对象
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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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# 产业种类
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# 利用 测试 BomNodes.csv 转换产业 和 id 前提是 一个产业一个产品id 且一一对应
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product_id = self.type2.loc[self.type2['Code'] == ag_node, 'Index']
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type2 = self.type2.loc[product_id, '产业种类'].values[0]
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# depreciation ratio 折旧比值
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product_id = product_id.iloc[0]
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j_comp_data_consumed = self.data_consumed[product_id]
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j_comp_data_produced = self.data_produced[product_id]
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product = ProductAgent(ag_node, self, name=attr['Name'], type2=type2,
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j_comp_data_consumed=j_comp_data_consumed,
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j_comp_data_produced=j_comp_data_produced,
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)
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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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firm_id = self.Firm['Code'] == ag_node
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n_equip_c = self.Firm.loc[firm_id, '设备数量'].values[0]
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demand_quantity = self.Firm.loc[firm_id, 'production_output'].values[0]
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production_output = self.Firm.loc[firm_id, 'demand_quantity'].values[0]
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# c购买价格？ 数据预处理
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# c_price = self.Firm.loc[self.Firm['Code'] == ag_node, 'c_price'].values[0]
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# 资源 资源库存信息 利用 firm_resource
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R = self.firm_resource_R.loc[firm_id].to_list()[0]
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P = self.firm_resource_P.loc[firm_id].to_list()[0]
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C = self.firm_resource_C.loc[firm_id].to_list()[0]
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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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n_equip_c=n_equip_c,
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a_lst_product=a_lst_product,
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demand_quantity=demand_quantity,
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production_output=production_output,
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# c_price=c_price,
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R=R,
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P=P,
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C=C
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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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# 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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# 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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firms = [firm for firm in self.company_agents if firm.unique_id == firm_code]
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firm = firms[0] if firms else None
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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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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 resource_integration(self):
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data_R = pd.read_csv("测试数据 companies_materials.csv")
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data_C = pd.read_csv("测试数据 companies_devices.csv")
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data_P = pd.read_csv("测试数据 companies_products.csv")
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device_salvage_values = pd.read_csv('测试数据 device_salvage_values.csv')
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self.device_salvage_values = device_salvage_values
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data_merged_C = pd.merge(data_C, device_salvage_values, on='设备id', how='left')
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firm_resource_R = (data_R.groupby('Firm_Code')[['材料id', '材料数量']]
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.apply(lambda x: x.values.tolist()))
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firm_resource_C = (data_merged_C.groupby('Firm_Code')[['设备id', '设备数量', '设备残值']]
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.apply(lambda x: x.values.tolist()))
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firm_resource_P = (data_P.groupby('Firm_Code')[['产品id', '产品数量']]
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.apply(lambda x: x.values.tolist()))
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self.firm_resource_R = firm_resource_R
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self.firm_resource_C = firm_resource_C
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self.firm_resource_P = firm_resource_P
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def j_comp_consumed_produced(self):
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data_consumed = pd.read_csv('测试数据 consumed_materials.csv')
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data_produced = pd.read_csv('测试数据 produced_products.csv')
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data_consumed = (data_consumed.groupby('产业id')[['消耗材料id', '消耗量']]
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.apply(lambda x: x.values.tolist()))
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data_produced = (data_produced.groupby('产业id')[['制造产品id', '制造量']]
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.apply(lambda x: x.values.tolist()))
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self.data_consumed = data_consumed
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self.data_produced = data_produced
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def step(self):
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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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# 3. 判断是否需要购买资源 判断是否需要购买机器
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purchase_material_firms = {}
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purchase_machinery_firms = {}
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||
material_list = []
|
||
machinery_list = []
|
||
list_seek_material_firm = [] # 每一个收到请求的企业
|
||
list_seek_machinery_firm = [] # 每一个收到请求的企业
|
||
|
||
for firm in self.company_agents:
|
||
# 资源
|
||
for sub_list in firm.R:
|
||
if sub_list[1] <= firm.s_r:
|
||
required_material_quantity = firm.S_r - sub_list[1]
|
||
(material_list.append([sub_list[0], required_material_quantity]))
|
||
purchase_material_firms[firm] = material_list
|
||
# 设备
|
||
for sub_list in firm.C:
|
||
# 对于设备的required_machinery_quantity 要有所改变 根据残值而言！ 每一个周期固定减少残值值 x firm 里面定义
|
||
sub_list[2] -= firm.x
|
||
if sub_list[2] <= 0: # 残值小于等于 0 时
|
||
sub_list[1] -= 1
|
||
required_machinery_quantity = 1 # 补回原来的量 也就是 1
|
||
(machinery_list
|
||
.append([sub_list[0], required_machinery_quantity]))
|
||
purchase_machinery_firms[firm] = machinery_list
|
||
|
||
# 寻源并发送请求 决定是否接受供应 并更新
|
||
for material_firm_key, sub_list_values in purchase_material_firms.items():
|
||
for mater_list in sub_list_values:
|
||
result = material_firm_key.seek_material_supply(mater_list[0])
|
||
# 如果 result 不等于 0，才将其添加到 list_seek_material_firm 列表中
|
||
if result != -1:
|
||
list_seek_material_firm.append(result)
|
||
|
||
if len(list_seek_material_firm) != 0:
|
||
for seek_material_firm in list_seek_material_firm:
|
||
seek_material_firm.handle_material_request(mater_list) # 更新产品
|
||
for R_list in firm.R:
|
||
R_list[1] = firm.S_r
|
||
|
||
for machinery_firm, sub_list in purchase_machinery_firms.items():
|
||
for machi_list in sub_list:
|
||
# 执行一次调用 machinery_firm.seek_machinery_supply(machinery_list[0])
|
||
result = machinery_firm.seek_machinery_supply(machi_list[0])
|
||
# 如果 result 不等于 0，才将其添加到 list_seek_machinery_firm 列表中
|
||
if result != -1:
|
||
list_seek_machinery_firm.append(result)
|
||
|
||
if len(list_seek_machinery_firm) != 0:
|
||
for seek_machinery_firm in list_seek_machinery_firm:
|
||
seek_machinery_firm.handle_machinery_request(machi_list)
|
||
for C_list, C0_list in zip(firm.C, firm.C0):
|
||
C_list[1] = C0_list[1] # 赋值回去
|
||
C_list[2] = C0_list[2]
|
||
|
||
# 消耗资源过程
|
||
consumed_material = []
|
||
for product in firm.indus_i:
|
||
for sub_list_data_consumed in product.j_comp_data_consumed:
|
||
consumed_material_id = sub_list_data_consumed[0]
|
||
consumed_material_num = sub_list_data_consumed[1]
|
||
consumed_material.append([consumed_material_id, consumed_material_num])
|
||
for sub_list_consumed_material in consumed_material:
|
||
for sub_list_material in firm.R:
|
||
if sub_list_material[0] == sub_list_consumed_material[0]:
|
||
sub_list_material[1] = sub_list_material[1] - sub_list_consumed_material[1]
|
||
# 生产产品过程
|
||
produced_products = []
|
||
for product in firm.indus_i:
|
||
for sub_list_produced_products in product.j_comp_data_consumed:
|
||
produced_products_id = sub_list_produced_products[0]
|
||
produced_products_num = sub_list_produced_products[1]
|
||
produced_products.append([produced_products_id, produced_products_num])
|
||
for sub_list_data_produced_products in produced_products:
|
||
for sub_list_products in firm.P:
|
||
if sub_list_products[0] == sub_list_data_produced_products[0]:
|
||
sub_list_products[1] = sub_list_products[1] - sub_list_data_produced_products[1]
|
||
# 刷新 R状态
|
||
firm.refresh_R()
|
||
# 刷新 C状态
|
||
firm.refresh_C()
|
||
# 刷新 P状态
|
||
firm.refresh_P()
|
||
|
||
# Increment the time step
|
||
self.t += 1
|