remove disrupt to removed, 3 status (Normal / Affected / Removed) + size_stat
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@ -79,8 +79,8 @@ class ControllerDB:
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for product_code in row.index[row == 1].to_list():
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dct = {code: [product_code]}
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list_dct.append(dct)
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# list_dct = [{'140': ['1.4.5.1']}]
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list_dct = [{'133': ['1.4.4.1']}]
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list_dct = [{'140': ['1.4.5.1']}]
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# list_dct = [{'133': ['1.4.4.1']}]
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# list_dct = [{'2': ['1.1.3']}]
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# list_dct = [{'135': ['1.3.2.1']}]
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# list_dct = [{'79': ['2.1.3.4']}]
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@ -128,7 +128,7 @@ class ControllerDB:
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dct_lst_init_disrupt_firm_prod, g_bom,
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n_max_trial, prf_size, prf_conn,
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cap_limit_prob_type, cap_limit_level,
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diff_new_conn, crit_supplier, diff_disrupt,
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diff_new_conn, crit_supplier,
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proactive_ratio, remove_t, netw_prf_n):
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e = Experiment(
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idx_scenario=idx_scenario,
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@ -144,7 +144,6 @@ class ControllerDB:
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cap_limit_level=cap_limit_level,
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diff_new_conn=diff_new_conn,
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crit_supplier=crit_supplier,
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diff_disrupt=diff_disrupt,
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proactive_ratio=proactive_ratio,
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remove_t=remove_t,
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netw_prf_n=netw_prf_n
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30
firm.py
30
firm.py
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@ -11,8 +11,7 @@ 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.ori_size = revenue_log
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self.size = revenue_log
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self.size_stat = []
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self.dct_prod_up_prod_stat = {}
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self.dct_prod_capacity = {}
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@ -29,11 +28,15 @@ class FirmAgent(ap.Agent):
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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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# init size_stat (self para)
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# (size, time step), ts -1 denotes initialization
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self.size_stat.append((revenue_log, -1))
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# init dct_prod_up_prod_stat (self para)
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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 / Affected / Disrupted / Removed, time step)
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'status': [('N', 0)],
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# (Normal / Affected / Removed, time step)
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'status': [('N', -1)], # ts -1 denotes initialization
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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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@ -45,7 +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.size / self.flt_cap_limit_level
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self.size_stat[0][0] / 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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@ -53,7 +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.size / self.flt_cap_limit_level
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self.size_stat[0][0] / 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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@ -133,7 +136,7 @@ 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_supp_up_prod_disrupted[
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product].size]
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product].size_stat[-1][0]]
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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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@ -146,7 +149,8 @@ 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.size for firm in lst_firm_connect]
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[firm.size_stat[-1][0]
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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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@ -215,7 +219,7 @@ 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.size for firm in lst_firm]
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[firm.size_stat[-1][0] 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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@ -230,7 +234,8 @@ 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.size for firm in lst_firm_connect]
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[firm.size_stat[-1][0]
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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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@ -273,6 +278,11 @@ class FirmAgent(ap.Agent):
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else:
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down_firm.dct_cand_alt_supp_up_prod_disrupted[product].remove(self)
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print(
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f"{self.name} denied {product.code} request "
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f"from {down_firm.name}"
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)
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def clean_before_trial(self):
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self.dct_request_prod_from_firm = {}
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67
model.py
67
model.py
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@ -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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@ -1,2 +1,2 @@
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X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11
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0,0,0,0,0,0,0,0,0,0,0
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X1,X2,X3,X4,X5,X6,X7,X8,X9,X10
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0,0,0,0,0,0,0,0,0,0
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1
orm.py
1
orm.py
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@ -61,7 +61,6 @@ class Experiment(Base):
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cap_limit_level = Column(DECIMAL(8, 4), nullable=False)
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diff_new_conn = Column(DECIMAL(8, 4), nullable=False)
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crit_supplier = Column(DECIMAL(8, 4), nullable=False)
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diff_disrupt = Column(DECIMAL(8, 4), nullable=False)
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proactive_ratio = Column(DECIMAL(8, 4), nullable=False)
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remove_t = Column(Integer, nullable=False)
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netw_prf_n = Column(Integer, nullable=False)
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@ -1,2 +1,2 @@
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n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,crit_supplier,diff_disrupt,proactive_ratio,remove_t,netw_prf_n
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10,TRUE,TRUE,uniform,10,0.5,0.1,0.01,1,5,2
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n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,crit_supplier,proactive_ratio,remove_t,netw_prf_n
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10,TRUE,TRUE,uniform,10,0.1,0.1,1,5,2
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