revenue reduce
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15
firm.py
15
firm.py
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@ -10,7 +10,8 @@ class FirmAgent(ap.Agent):
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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.ori_size = revenue_log
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self.size = revenue_log
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self.a_lst_product = a_lst_product
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self.dct_prod_capacity = {}
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@ -47,7 +48,7 @@ class FirmAgent(ap.Agent):
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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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extra_cap_mean = \
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self.revenue_log / self.flt_cap_limit_level
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self.size / self.flt_cap_limit_level
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extra_cap = self.model.nprandom.integers(extra_cap_mean-2,
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extra_cap_mean+2)
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extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
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@ -55,7 +56,7 @@ class FirmAgent(ap.Agent):
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self.dct_prod_capacity[product] = extra_cap
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elif self.str_cap_limit_prob_type == 'normal':
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extra_cap_mean = \
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self.revenue_log / self.flt_cap_limit_level
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self.size / self.flt_cap_limit_level
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extra_cap = self.model.nprandom.normal(extra_cap_mean, 1)
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extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
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# print(firm_agent.name, extra_cap)
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@ -146,7 +147,7 @@ class FirmAgent(ap.Agent):
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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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product].size]
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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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@ -159,7 +160,7 @@ class FirmAgent(ap.Agent):
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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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[firm.size 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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@ -228,7 +229,7 @@ class FirmAgent(ap.Agent):
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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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[firm.size 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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@ -243,7 +244,7 @@ class FirmAgent(ap.Agent):
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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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[firm.size 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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20
model.py
20
model.py
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@ -300,7 +300,7 @@ class Model(ap.Model):
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for path in lst_shortest_path]) \
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/ len(lst_shortest_path)
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drs = n2n_betweenness / \
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(len(lst_cand) * di_supp_firm.revenue_log)
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(len(lst_cand) * di_supp_firm.size)
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dct_drs[di_supp_code] = drs
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dct_drs = dict(sorted(
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dct_drs.items(), key=lambda kv: kv[1], reverse=True))
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@ -352,6 +352,18 @@ class Model(ap.Model):
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# self.int_stop_times = self.t
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# self.stop()
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# reduce the size of removed firm
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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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status, ts = firm.dct_prod_up_prod_stat[prod]['status'][-1]
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if status == 'R':
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firm.size -= \
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firm.ori_size / len(firm.a_lst_product) / self.drop_t
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print(self.t, firm.name, prod.code, firm.size)
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if self.t - ts + 1 == self.drop_t:
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firm.dct_prod_up_prod_stat[
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prod]['status'].append(('N', self.t))
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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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@ -462,14 +474,14 @@ class Model(ap.Model):
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# mark disrupted firm as removed based conditionally
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lost_percent = n_up_product_removed / len(
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product.a_predecessors())
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lst_size = self.a_lst_total_firms.revenue_log
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lst_size = [firm.revenue_log for firm
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lst_size = self.a_lst_total_firms.size
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lst_size = [firm.size for firm
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in self.a_lst_total_firms
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if product in firm.a_lst_product
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and product
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not in firm.a_lst_product_removed
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]
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std_size = (firm.revenue_log - min(lst_size) +
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std_size = (firm.size - min(lst_size) +
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1) / (max(lst_size) - min(lst_size) + 1)
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prob_remove = 1 - std_size * (1 - lost_percent)
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# damp prod
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19
test.ipynb
19
test.ipynb
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@ -298,6 +298,25 @@
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" break\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2.25\n",
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"2.25\n"
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]
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}
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],
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"source": [
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"print(27 / (4 * 3))\n",
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"print(27 / 4 / 3)"
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]
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}
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],
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"metadata": {
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