279 lines
14 KiB
Python
279 lines
14 KiB
Python
import agentpy as ap
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import math
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class FirmAgent(ap.Agent):
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def setup(self, code, name, type_region, revenue_log, a_lst_product):
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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.code = code
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self.name = name
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self.type_region = type_region
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self.revenue_log = revenue_log
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self.a_lst_product = a_lst_product
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self.dct_prod_capacity = dict.fromkeys(self.a_lst_product)
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# self.a_lst_up_product_removed = ap.AgentList(self.model, [])
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# self.a_lst_product_disrupted = ap.AgentList(self.model, [])
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self.a_lst_product_removed = ap.AgentList(self.model, [])
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self.dct_prod_up_prod_stat = {}
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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': [('N', 0)],
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# have or have no supply
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'supply': dict.fromkeys(prod.a_predecessors(), True)
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}
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# print(self.dct_prod_up_prod_stat)
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self.dct_n_trial_up_prod_removed = {}
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self.dct_cand_alt_supply_up_prod_removed = {}
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self.dct_request_prod_from_firm = {}
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# para
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self.is_prf_size = self.model.is_prf_size
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self.is_prf_conn = bool(self.p.prf_conn)
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self.flt_diff_new_conn = float(self.p.diff_new_conn)
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self.flt_crit_supplier = float(self.p.crit_supplier)
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def remove_edge_to_cus_remove_cus_up_prod(self, remove_product):
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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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for n1, n2, key, product_code in lst_out_edge:
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if product_code == remove_product.code:
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# remove edge
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self.firm_network.graph.remove_edge(n1, n2, key)
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# remove customer up product conditionally
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customer = ap.AgentIter(self.model, n2).to_list()[0]
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lst_in_edge = list(
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self.firm_network.graph.in_edges(n2,
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keys=True,
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data='Product'))
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lst_select_in_edge = [
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edge for edge in lst_in_edge
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if edge[-1] == remove_product.code
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]
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prod_remove = math.exp(-1 * self.flt_crit_supplier *
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len(lst_select_in_edge))
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if self.model.nprandom.choice([True, False],
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p=[prod_remove,
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1 - prod_remove]):
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# if remove_product not in \
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# customer.a_lst_up_product_removed:
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# customer.a_lst_up_product_removed.append(
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# remove_product)
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# customer.dct_n_trial_up_prod_removed[
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# remove_product] = 0
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for prod in customer.dct_prod_up_prod_stat.keys():
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if remove_product in \
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customer.dct_prod_up_prod_stat[
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prod]['supply'].keys():
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customer.dct_prod_up_prod_stat[
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prod]['supply'][remove_product] = False
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customer.dct_prod_up_prod_stat[
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prod]['status'].append(('D', self.model.t))
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customer.dct_n_trial_up_prod_removed[
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remove_product] = 0
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print(self.name, remove_product.code, 'affect',
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customer.name, prod.code)
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# print(customer.dct_prod_up_prod_stat)
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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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# for product in self.a_lst_up_product_removed:
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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_removed[
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product] <= self.model.int_n_max_trial:
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if self.dct_n_trial_up_prod_removed[product] == 0:
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# select a list of candidate firm that has the product
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self.dct_cand_alt_supply_up_prod_removed[product] = \
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self.model.a_lst_total_firms.select([
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product in firm.a_lst_product
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and product not in firm.a_lst_product_removed
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for firm in self.model.a_lst_total_firms
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])
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if self.dct_cand_alt_supply_up_prod_removed[product]:
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# select based on connection
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lst_firm_connect = []
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if self.is_prf_conn:
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for firm in \
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self.dct_cand_alt_supply_up_prod_removed[product]:
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out_edges = self.model.firm_network.graph.out_edges(
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self.model.firm_network.positions[firm], keys=True)
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in_edges = self.model.firm_network.graph.in_edges(
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self.model.firm_network.positions[firm], keys=True)
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lst_adj_firm = []
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lst_adj_firm += \
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[ap.AgentIter(self.model, edge[1]).to_list()[
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0].code for edge in out_edges]
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lst_adj_firm += \
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[ap.AgentIter(self.model, edge[0]).to_list()[
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0].code for edge in in_edges]
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if self.code in lst_adj_firm:
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lst_firm_connect.append(firm)
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if len(lst_firm_connect) == 0:
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# select based on size or not
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if self.is_prf_size:
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lst_size = \
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[size for size in
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self.dct_cand_alt_supply_up_prod_removed[
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product].revenue_log]
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lst_prob = [size / sum(lst_size)
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for size in lst_size]
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select_alt_supply = self.model.nprandom.choice(
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self.dct_cand_alt_supply_up_prod_removed[product],
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p=lst_prob)
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else:
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select_alt_supply = self.model.nprandom.choice(
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self.dct_cand_alt_supply_up_prod_removed[product])
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elif len(lst_firm_connect) > 0:
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# select based on size of connected firm or not
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if self.is_prf_size:
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lst_firm_size = \
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[firm.revenue_log for firm in lst_firm_connect]
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lst_prob = \
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[size / sum(lst_firm_size)
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for size in lst_firm_size]
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select_alt_supply = \
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self.model.nprandom.choice(lst_firm_connect,
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p=lst_prob)
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else:
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select_alt_supply = \
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self.model.nprandom.choice(lst_firm_connect)
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print(
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f"{self.name} selct alt supply for {product.code} "
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f"from {select_alt_supply.name}"
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)
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assert product in select_alt_supply.a_lst_product, \
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f"{select_alt_supply} \
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does not produce requested product {product}"
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if product in select_alt_supply.dct_request_prod_from_firm.\
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keys():
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select_alt_supply.dct_request_prod_from_firm[
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product].append(self)
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else:
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select_alt_supply.dct_request_prod_from_firm[product] = [
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self
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]
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print(
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select_alt_supply.name, 'dct_request_prod_from_firm', {
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key.code: [v.name for v in value]
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for key, value in
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select_alt_supply.dct_request_prod_from_firm.items()
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})
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self.dct_n_trial_up_prod_removed[product] += 1
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def handle_request(self):
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print(self.name, 'handle_request')
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for product, lst_firm in self.dct_request_prod_from_firm.items():
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if self.dct_prod_capacity[product] > 0:
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if len(lst_firm) == 0:
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continue
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elif len(lst_firm) == 1:
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self.accept_request(lst_firm[0], product)
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elif len(lst_firm) > 1:
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# handling based on connection
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lst_firm_connect = []
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if self.is_prf_conn:
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for firm in lst_firm:
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out_edges = \
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self.model.firm_network.graph.out_edges(
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self.model.firm_network.positions[firm],
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keys=True)
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in_edges = \
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self.model.firm_network.graph.in_edges(
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self.model.firm_network.positions[firm],
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keys=True)
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lst_adj_firm = []
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lst_adj_firm += \
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[ap.AgentIter(self.model, edge[1]).to_list()[
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0].code for edge in out_edges]
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lst_adj_firm += \
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[ap.AgentIter(self.model, edge[0]).to_list()[
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0].code for edge in in_edges]
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if self.code in lst_adj_firm:
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lst_firm_connect.append(firm)
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if len(lst_firm_connect) == 0:
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# handling based on size or not
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if self.is_prf_size:
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lst_firm_size = \
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[firm.revenue_log for firm in lst_firm]
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lst_prob = \
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[size / sum(lst_firm_size)
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for size in lst_firm_size]
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select_customer = \
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self.model.nprandom.choice(lst_firm,
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p=lst_prob)
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else:
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select_customer = \
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self.model.nprandom.choice(lst_firm)
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self.accept_request(select_customer, product)
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elif len(lst_firm_connect) > 0:
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# handling based on size of connected firm or not
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if self.is_prf_size:
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lst_firm_size = \
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[firm.revenue_log for firm in lst_firm_connect]
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lst_prob = \
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[size / sum(lst_firm_size)
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for size in lst_firm_size]
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select_customer = \
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self.model.nprandom.choice(lst_firm_connect,
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p=lst_prob)
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else:
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select_customer = \
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self.model.nprandom.choice(lst_firm_connect)
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self.accept_request(select_customer, product)
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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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prod_accept = self.flt_diff_new_conn
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if self.model.nprandom.choice([True, False],
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p=[prod_accept, 1 - prod_accept]):
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self.firm_network.graph.add_edges_from([
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(self.firm_network.positions[self],
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self.firm_network.positions[down_firm], {
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'Product': product.code
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})
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])
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self.dct_prod_capacity[product] -= 1
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self.dct_request_prod_from_firm[product].remove(down_firm)
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# down_firm.a_lst_up_product_removed.remove(product)
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for prod in down_firm.dct_prod_up_prod_stat.keys():
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if product in down_firm.dct_prod_up_prod_stat[
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prod]['supply'].keys():
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down_firm.dct_prod_up_prod_stat[
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prod]['supply'][product] = True
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down_firm.dct_prod_up_prod_stat[
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prod]['status'].append(('N', self.model.t))
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# print(down_firm.dct_prod_up_prod_stat)
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del down_firm.dct_n_trial_up_prod_removed[product]
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del down_firm.dct_cand_alt_supply_up_prod_removed[product]
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print(
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f"{self.name} accept {product.code} request "
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f"from {down_firm.name}"
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)
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else:
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down_firm.dct_cand_alt_supply_up_prod_removed[product].remove(self)
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def clean_before_trial(self):
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self.dct_request_prod_from_firm = {}
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def clean_before_time_step(self):
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self.dct_n_trial_up_prod_removed = \
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dict.fromkeys(self.dct_n_trial_up_prod_removed.keys(), 0)
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self.dct_cand_alt_supply_up_prod_removed = {}
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# self.a_lst_up_product_removed = ap.AgentList(self.model, [])
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def get_firm_network_node(self):
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return self.firm_network.positions[self]
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