remove disrupt to removed, 3 status (Normal / Affected / Removed) + size_stat
This commit is contained in:
67
model.py
67
model.py
@@ -22,7 +22,6 @@ class Model(ap.Model):
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# external variable
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self.int_n_max_trial = int(self.p.n_max_trial)
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self.is_prf_size = bool(self.p.prf_size)
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self.flt_diff_disrupt = float(self.p.diff_disrupt)
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self.proactive_ratio = float(self.p.proactive_ratio)
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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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@@ -227,7 +226,8 @@ class Model(ap.Model):
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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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firm.dct_prod_up_prod_stat[
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product]['status'].append(('D', self.t))
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product]['status'].append(('A', self.t))
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print(f"initial removal {firm.name} {product.code}")
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# proactive strategy
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# get all the firm prod affected
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@@ -297,7 +297,7 @@ 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.size)
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(len(lst_cand) * di_supp_firm.size_stat[-1][0])
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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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@@ -341,23 +341,25 @@ class Model(ap.Model):
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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 == 'D':
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firm.size -= \
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firm.ori_size \
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if status == 'A':
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size = firm.size_stat[-1][0] - \
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firm.size_stat[0][0] \
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/ len(firm.dct_prod_up_prod_stat.keys()) \
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/ self.remove_t
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print(self.t, firm.name, prod.code, firm.size)
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firm.size_stat.append((size, self.t))
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print(f'in ts {self.t}, reduce {firm.name} size '
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f'to {firm.size_stat[-1][0]} due to {prod.code}')
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if self.t - ts + 1 == self.remove_t:
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# turn disrupted firm into removed firm
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firm.dct_prod_up_prod_stat[
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prod]['status'].append(('R', self.t))
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# stop simulation if all firm returned to normal except inital removal
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# stop simulation if any firm still in affected except inital 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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status, ts = firm.dct_prod_up_prod_stat[prod]['status'][-1]
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if status == 'D' and ts != 0:
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if status == 'A' and ts != 0:
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print("not stop because", firm.name, prod.code)
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break
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else:
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@@ -374,7 +376,7 @@ class Model(ap.Model):
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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 == 'D' and ts == self.t-1:
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if status == 'A' and ts == self.t-1:
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firm.remove_edge_to_cus_affect_cus_up_prod(prod)
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for n_trial in range(self.int_n_max_trial):
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@@ -387,7 +389,7 @@ class Model(ap.Model):
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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]['status'][-1][0]
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if status != 'N':
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if status == 'A':
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for supply in firm.dct_prod_up_prod_stat[
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prod]['supply'].keys():
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if not firm.dct_prod_up_prod_stat[
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@@ -414,49 +416,6 @@ class Model(ap.Model):
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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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# turn affected firm into disrupted firm conditionally
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for firm in self.a_lst_total_firms:
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for product in firm.dct_prod_up_prod_stat.keys():
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status = firm.dct_prod_up_prod_stat[product]['status'][-1][0]
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if status == 'A':
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print(firm.name, 'affected product:', product.code)
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n_up_product_lost = \
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sum([not stat for stat in
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firm.dct_prod_up_prod_stat[
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product]['supply'].values()])
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if n_up_product_lost == 0:
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continue
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else:
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lost_percent = n_up_product_lost / len(
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product.a_predecessors())
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# firm (affected) + other firm (same product, normal)
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lst_firm = [firm]
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lst_firm += \
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[firm for firm
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in self.a_lst_total_firms
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if firm.is_prod_in_current_normal(product)]
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lst_size = [firm.size for firm in lst_firm]
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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_disrupt = 1 - std_size * (1 - lost_percent)
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# damp prob
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prob_disrupt = prob_disrupt ** self.flt_diff_disrupt
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# sample prob
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prob_disrupt = self.nprandom.uniform(
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prob_disrupt - 0.1, prob_disrupt + 0.1)
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prob_disrupt = 1 if prob_disrupt > 1 else prob_disrupt
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prob_disrupt = 0 if prob_disrupt < 0 else prob_disrupt
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if self.nprandom.choice([True, False],
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p=[prob_disrupt,
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1 - prob_disrupt]):
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firm.dct_prod_up_prod_stat[
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product]['status'].append(('D', self.t))
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print(firm.name, 'disrupted product:',
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product.code)
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else:
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firm.dct_prod_up_prod_stat[
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product]['status'].append(('N', self.t))
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def end(self):
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print('/' * 20, 'output', '/' * 20)
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