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firm.py
28
firm.py
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@ -6,7 +6,7 @@ class FirmAgent(ap.Agent):
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self.firm_network = self.model.firm_network
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self.product_network = self.model.product_network
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# self para
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# self parameter
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self.code = code
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self.name = name
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self.type_region = type_region
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@ -14,7 +14,7 @@ class FirmAgent(ap.Agent):
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self.dct_prod_up_prod_stat = {}
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self.dct_prod_capacity = {}
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# para in trial
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# parameter in trial
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self.dct_n_trial_up_prod_disrupted = {}
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self.dct_cand_alt_supp_up_prod_disrupted = {}
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self.dct_request_prod_from_firm = {}
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@ -26,14 +26,14 @@ class FirmAgent(ap.Agent):
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self.flt_cap_limit_level = float(self.p.cap_limit_level)
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self.flt_diff_new_conn = float(self.p.diff_new_conn)
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# init size_stat (self para)
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# initialize size_stat (self parameter)
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# (size, time step)
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self.size_stat.append((revenue_log, 0))
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# init dct_prod_up_prod_stat (self para)
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# init dct_prod_up_prod_stat (self parameter)
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for prod in a_lst_product:
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self.dct_prod_up_prod_stat[prod] = {
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# (Normal / Disrupted / Removed, time step)
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# status: (Normal / Disrupted / Removed, time step)
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'p_stat': [('N', 0)],
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# supply for each component and respective disrupted supplier
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# set_disrupt_firm is refreshed to empty at each update
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@ -43,9 +43,9 @@ class FirmAgent(ap.Agent):
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# Note: do not use fromkeys as it's a shallow copy
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}
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# init extra capacity (self para)
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# initialize extra capacity (self parameter)
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for product in a_lst_product:
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# init extra capacity based on discrete uniform distribution
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# initialize extra capacity based on discrete uniform distribution
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assert self.str_cap_limit_prob_type in ['uniform', 'normal'], \
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"cap_limit_prob_type other than uniform, normal"
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if self.str_cap_limit_prob_type == 'uniform':
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@ -63,7 +63,7 @@ class FirmAgent(ap.Agent):
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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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# para disrupted_prod is the product that self got disrupted
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# parameter disrupted_prod is the product that self got disrupted
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lst_out_edge = list(
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self.firm_network.graph.out_edges(
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self.firm_network.positions[self], keys=True, data='Product'))
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@ -84,8 +84,8 @@ class FirmAgent(ap.Agent):
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self.firm_network.graph.remove_edge(n1, n2, key)
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def disrupt_cus_prod(self, prod, disrupted_up_prod):
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# para prod is the product that has disrupted_up_prod
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# para disrupted_up_prod is the product that
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# parameter prod is the product that has disrupted_up_prod
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# parameter disrupted_up_prod is the product that
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# self's component exists disrupted supplier
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num_lost = \
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len(self.dct_prod_up_prod_stat[prod]['s_stat']
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@ -101,6 +101,8 @@ class FirmAgent(ap.Agent):
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[firm.size_stat[-1][0] for firm in self.model.a_lst_total_firms]
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std_size = (self.size_stat[-1][0] - min(lst_size) + 1) \
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/ (max(lst_size) - min(lst_size) + 1)
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# calculate probability of disruption
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prob_disrupt = 1 - std_size * (1 - lost_percent)
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if self.model.nprandom.choice([True, False],
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p=[prob_disrupt,
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@ -117,7 +119,7 @@ class FirmAgent(ap.Agent):
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# f"disrupted supplier of {disrupted_up_prod.code}")
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def seek_alt_supply(self, product):
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# para product is the product that self is seeking
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# parameter product is the product that self is seeking
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# print(f"{self.name} seek alt supply for {product.code}")
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if self.dct_n_trial_up_prod_disrupted[
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product] <= self.model.int_n_max_trial:
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@ -258,8 +260,8 @@ class FirmAgent(ap.Agent):
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# )
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def accept_request(self, down_firm, product):
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# para product is the product that self is selling
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# connected firm has no probability
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# parameter product is the product that self is selling
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# connected firm has no probability for accepting request
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node_self = self.get_firm_network_node()
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node_d_firm = down_firm.get_firm_network_node()
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if self.model.firm_network.graph.has_edge(node_self, node_d_firm) or \
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29
model.py
29
model.py
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@ -10,7 +10,7 @@ import json
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class Model(ap.Model):
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def setup(self):
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# self para
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# self parameter
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self.sample = self.p.sample
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self.int_stop_ts = 0
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self.int_n_iter = int(self.p.n_iter)
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@ -27,11 +27,11 @@ class Model(ap.Model):
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self.remove_t = int(self.p.remove_t)
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self.int_netw_prf_n = int(self.p.netw_prf_n)
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# init graph bom
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# initialize graph bom
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G_bom = nx.adjacency_graph(json.loads(self.p.g_bom))
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self.product_network = ap.Network(self, G_bom)
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# init graph firm
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# initialize graph firm
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Firm = pd.read_csv("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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@ -49,7 +49,7 @@ class Model(ap.Model):
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firm_labels_dict[code] = Firm_attr.loc[code].to_dict()
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nx.set_node_attributes(G_Firm, firm_labels_dict)
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# init graph firm prod
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# initialize graph firm prod
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Firm_Prod = pd.read_csv("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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@ -122,11 +122,12 @@ class Model(ap.Model):
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# nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm.csv')
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# nx.to_pandas_adjacency(G_FirmProd).to_csv('adj_g_firm_prod.csv')
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# unconnected node
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# connect unconnected nodes
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for node in nx.nodes(G_Firm):
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if G_Firm.degree(node) == 0:
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for product_code in G_Firm.nodes[node]['Product_Code']:
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# unconnect node does not have possible suppliers
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# unconnected node does not have possible suppliers,
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# therefore find possible customer instead
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# current node in graph firm prod
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current_node = \
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[n for n, v in G_FirmProd.nodes(data=True)
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@ -135,10 +136,10 @@ class Model(ap.Model):
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lst_succ_product_code = list(
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G_bom.successors(product_code))
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# different from for different types of product,
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# different from: for different types of product,
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# finding a common supplier (the logic above),
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# for different types of product,
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# finding a custormer for each product
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# instead: for different types of product,
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# finding a customer for each product
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for succ_product_code in lst_succ_product_code:
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# for each product successor (finished product)
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# the firm sells to,
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@ -187,14 +188,14 @@ class Model(ap.Model):
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# nx.draw(G_FirmProd)
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# plt.show()
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# init product
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# initialize product
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for ag_node, attr in self.product_network.graph.nodes(data=True):
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product = ProductAgent(self, code=ag_node.label, name=attr['Name'])
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self.product_network.add_agents([product], [ag_node])
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self.a_lst_total_products = ap.AgentList(self,
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self.product_network.agents)
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# init firm
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# initialize firm
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for ag_node, attr in self.firm_network.graph.nodes(data=True):
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firm_agent = FirmAgent(
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self,
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@ -210,7 +211,7 @@ class Model(ap.Model):
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self.firm_network.add_agents([firm_agent], [ag_node])
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self.a_lst_total_firms = ap.AgentList(self, self.firm_network.agents)
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# init dct_lst_init_disrupt_firm_prod (from string to agent)
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# initialize dct_lst_init_disrupt_firm_prod (from string to agent)
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t_dct = {}
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for firm_code, lst_product in \
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self.dct_lst_init_disrupt_firm_prod.items():
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@ -368,7 +369,7 @@ class Model(ap.Model):
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firm.dct_prod_up_prod_stat[
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prod]['p_stat'].append(('R', self.t))
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# stop simulation if any firm still in disrupted except inital removal
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# stop simulation if any firm still in disrupted except initial removal
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if self.t > 0:
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for firm in self.a_lst_total_firms:
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for prod in firm.dct_prod_up_prod_stat.keys():
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@ -443,8 +444,6 @@ class Model(ap.Model):
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# reset dct_request_prod_from_firm
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self.a_lst_total_firms.clean_before_trial()
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# do not use:
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# self.a_lst_total_firms.dct_request_prod_from_firm = {} why?
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def end(self):
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# print('/' * 20, 'output', '/' * 20)
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@ -9,6 +9,7 @@ class ProductAgent(ap.Agent):
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self.name = name
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def a_successors(self):
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# find successors of a product, return in AgentList (ProductAgent)
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nodes = self.product_network.graph.successors(
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self.product_network.positions[self])
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return ap.AgentList(
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@ -16,6 +17,7 @@ class ProductAgent(ap.Agent):
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[ap.AgentIter(self.model, node).to_list()[0] for node in nodes])
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def a_predecessors(self):
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# find predecessors of a product, return in AgentList (ProductAgent)
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nodes = self.product_network.graph.predecessors(
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self.product_network.positions[self])
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return ap.AgentList(
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