remove crit_supplier

controller_db.py

@@ -117,7 +117,7 @@ class ControllerDB:
                          dct_lst_init_disrupt_firm_prod, g_bom,
                          n_max_trial, prf_size, prf_conn,
                          cap_limit_prob_type, cap_limit_level,
-                         diff_new_conn, crit_supplier,
+                         diff_new_conn,
                          proactive_ratio, remove_t, netw_prf_n):
         e = Experiment(
             idx_scenario=idx_scenario,
@@ -132,7 +132,6 @@ class ControllerDB:
             cap_limit_prob_type=cap_limit_prob_type,
             cap_limit_level=cap_limit_level,
             diff_new_conn=diff_new_conn,
-            crit_supplier=crit_supplier,
             proactive_ratio=proactive_ratio,
             remove_t=remove_t,
             netw_prf_n=netw_prf_n

firm.py

@@ -1,5 +1,4 @@
 import agentpy as ap
-import math
 
 
 class FirmAgent(ap.Agent):
@@ -26,7 +25,6 @@ class FirmAgent(ap.Agent):
         self.str_cap_limit_prob_type = str(self.p.cap_limit_prob_type)
         self.flt_cap_limit_level = float(self.p.cap_limit_level)
         self.flt_diff_new_conn = float(self.p.diff_new_conn)
-        self.flt_crit_supplier = float(self.p.crit_supplier)
 
         # init size_stat (self para)
         # (size, time step)
@@ -36,9 +34,14 @@ class FirmAgent(ap.Agent):
         for prod in a_lst_product:
             self.dct_prod_up_prod_stat[prod] = {
                 # (Normal / Disrupted / Removed, time step)
-                'status': [('N', 0)],
+                'p_stat': [('N', 0)],
                 # have or have no supply
-                'supply': dict.fromkeys(prod.a_predecessors(), True)
+                'supply': dict.fromkeys(prod.a_predecessors(), True),
+                # supply for each component and respective disrupted supplier
+                # lst_disrupt_firm is refreshed to [] at each update
+                's_stat': dict.fromkeys(prod.a_predecessors(),
+                                        {'stat': True,
+                                         'lst_disrupt_firm': []})
             }
 
         # init extra capacity (self para)
@@ -62,45 +65,58 @@ class FirmAgent(ap.Agent):
                 # print(firm_agent.name, extra_cap)
                 self.dct_prod_capacity[product] = extra_cap
 
-    def remove_edge_to_cus_disrupt_cus_up_prod(self, disrupted_prod):
+    def remove_edge_to_cus(self, disrupted_prod):
         # para disrupted_prod is the product that self got disrupted
         lst_out_edge = list(
             self.firm_network.graph.out_edges(
                 self.firm_network.positions[self], keys=True, data='Product'))
         for n1, n2, key, product_code in lst_out_edge:
             if product_code == disrupted_prod.code:
+                # update customer up product supplier status
+                customer = ap.AgentIter(self.model, n2).to_list()[0]
+                for prod in customer.dct_prod_up_prod_stat.keys():
+                    if disrupted_prod in \
+                            customer.dct_prod_up_prod_stat[
+                                prod]['s_stat'].keys():
+                        if self not in customer.dct_prod_up_prod_stat[
+                                prod]['s_stat'][disrupted_prod][
+                                    'lst_disrupt_firm']:
+                            customer.dct_prod_up_prod_stat[
+                                prod]['s_stat'][disrupted_prod][
+                                    'lst_disrupt_firm'].append(self)
                 # remove edge to customer
                 self.firm_network.graph.remove_edge(n1, n2, key)
 
-                # customer up product affected conditionally
+    def disrupt_cus_prod(self, prod, disrupted_up_prod):
-                customer = ap.AgentIter(self.model, n2).to_list()[0]
+        # para prod is the product that has disrupted_up_prod
-                lst_in_edge = list(
+        # para disrupted_up_prod is the product that
-                    self.firm_network.graph.in_edges(n2,
+        # self's component exists disrupted supplier
-                                                     keys=True,
+        num_lost = \
-                                                     data='Product'))
+            len(self.dct_prod_up_prod_stat[prod]['s_stat']
-                lst_select_in_edge = [
+                [disrupted_up_prod]['lst_disrupt_firm'])
-                    edge for edge in lst_in_edge
+        num_remain = \
-                    if edge[-1] == disrupted_prod.code
+            len([u for u, _, _, d in
-                ]
+                 self.firm_network.graph.in_edges(self.get_firm_network_node(),
-                prob_lost_supp = math.exp(-1 * self.flt_crit_supplier *
+                                                  keys=True,
-                                          len(lst_select_in_edge))
+                                                  data='Product')
-                if self.model.nprandom.choice([True, False],
+                 if d == disrupted_up_prod.code])
-                                              p=[prob_lost_supp,
+        lost_percent = num_lost / (num_lost + num_remain)
-                                                 1 - prob_lost_supp]):
+        lst_size = \
-                    customer.dct_n_trial_up_prod_disrupted[disrupted_prod] = 0
+            [firm.size_stat[-1][0] for firm in self.model.a_lst_total_firms]
-                    for prod in customer.dct_prod_up_prod_stat.keys():
+        std_size = (self.size_stat[-1][0] - min(lst_size) + 1) \
-                        if disrupted_prod in \
+            / (max(lst_size) - min(lst_size) + 1)
-                                customer.dct_prod_up_prod_stat[
+        prob_disrupt = 1 - std_size * (1 - lost_percent)
-                                    prod]['supply'].keys():
+        if self.model.nprandom.choice([True, False],
-                            customer.dct_prod_up_prod_stat[
+                                      p=[prob_disrupt,
-                                prod]['supply'][disrupted_prod] = False
+                                         1 - prob_disrupt]):
-                            status, _ = customer.dct_prod_up_prod_stat[
+            self.dct_n_trial_up_prod_disrupted[disrupted_up_prod] = 0
-                                prod]['status'][-1]
+            self.dct_prod_up_prod_stat[
-                            if status != 'D':
+                prod]['s_stat'][disrupted_up_prod]['stat'] = False
-                                customer.dct_prod_up_prod_stat[
+            status, _ = self.dct_prod_up_prod_stat[
-                                    prod]['status'].append(('D', self.model.t))
+                prod]['p_stat'][-1]
-                            # print(self.name, disrupted_prod.code, 'disrupt',
+            if status != 'D':
-                            #       customer.name, prod.code)
+                self.dct_prod_up_prod_stat[
+                    prod]['p_stat'].append(('D', self.model.t))
 
     def seek_alt_supply(self, product):
         # para product is the product that self is seeking
@@ -270,7 +286,7 @@ class FirmAgent(ap.Agent):
                     down_firm.dct_prod_up_prod_stat[
                         prod]['supply'][product] = True
                     down_firm.dct_prod_up_prod_stat[
-                        prod]['status'].append(('N', self.model.t))
+                        prod]['p_stat'].append(('N', self.model.t))
             del down_firm.dct_n_trial_up_prod_disrupted[product]
             del down_firm.dct_cand_alt_supp_up_prod_disrupted[product]
 
@@ -296,17 +312,21 @@ class FirmAgent(ap.Agent):
 
         # update the status of firm
         for prod in self.dct_prod_up_prod_stat.keys():
-            status, ts = self.dct_prod_up_prod_stat[prod]['status'][-1]
+            status, ts = self.dct_prod_up_prod_stat[prod]['p_stat'][-1]
             if ts != self.model.t:
-                self.dct_prod_up_prod_stat[prod]['status'].append(
+                self.dct_prod_up_prod_stat[prod]['p_stat'].append(
                     (status, self.model.t))
+            # refresh lst_disrupt_firm
+            for up_prod in self.dct_prod_up_prod_stat[prod]['s_stat'].keys():
+                self.dct_prod_up_prod_stat[prod][
+                    's_stat'][up_prod]['lst_disrupt_firm'] = []
 
     def get_firm_network_node(self):
         return self.firm_network.positions[self]
 
     def is_prod_in_current_normal(self, prod):
         if prod in self.dct_prod_up_prod_stat.keys():
-            if self.dct_prod_up_prod_stat[prod]['status'][-1][0] == 'N':
+            if self.dct_prod_up_prod_stat[prod]['p_stat'][-1][0] == 'N':
                 return True
             else:
                 return False

model.py

@@ -228,7 +228,7 @@ class Model(ap.Model):
                 assert product in firm.dct_prod_up_prod_stat.keys(), \
                     f"product {product.code} not in firm {firm.code}"
                 firm.dct_prod_up_prod_stat[
-                    product]['status'].append(('D', self.t))
+                    product]['p_stat'].append(('D', self.t))
                 # print(f"initial disruption {firm.name} {product.code}")
 
         # proactive strategy
@@ -349,7 +349,7 @@ class Model(ap.Model):
         # reduce the size of disrupted firm
         for firm in self.a_lst_total_firms:
             for prod in firm.dct_prod_up_prod_stat.keys():
-                status, ts = firm.dct_prod_up_prod_stat[prod]['status'][-1]
+                status, ts = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
                 if status == 'D':
                     size = firm.size_stat[-1][0] - \
                         firm.size_stat[0][0] \
@@ -360,19 +360,19 @@ class Model(ap.Model):
                     #       f'to {firm.size_stat[-1][0]} due to {prod.code}')
                     lst_is_disrupt = \
                         [stat == 'D' for stat, _ in
-                         firm.dct_prod_up_prod_stat[prod]['status']
+                         firm.dct_prod_up_prod_stat[prod]['p_stat']
                          [-1 * self.remove_t:]]
                     if all(lst_is_disrupt):
                         # turn disrupted firm into removed firm
                         # when last self.remove_t times status is all disrupted
                         firm.dct_prod_up_prod_stat[
-                            prod]['status'].append(('R', self.t))
+                            prod]['p_stat'].append(('R', self.t))
 
         # stop simulation if any firm still in disrupted except inital removal
         if self.t > 0:
             for firm in self.a_lst_total_firms:
                 for prod in firm.dct_prod_up_prod_stat.keys():
-                    status, _ = firm.dct_prod_up_prod_stat[prod]['status'][-1]
+                    status, _ = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
                     is_init = \
                         firm in self.dct_lst_init_disrupt_firm_prod.keys() \
                         and prod in self.dct_lst_init_disrupt_firm_prod[firm]
@@ -397,9 +397,17 @@ class Model(ap.Model):
             for prod in firm.dct_prod_up_prod_stat.keys():
                 # repetition of disrupted firm that last for multiple ts is ok,
                 # as their edge has already been removed
-                status, ts = firm.dct_prod_up_prod_stat[prod]['status'][-1]
+                status, ts = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1]
                 if status == 'D' and ts == self.t-1:
-                    firm.remove_edge_to_cus_disrupt_cus_up_prod(prod)
+                    firm.remove_edge_to_cus(prod)
+
+        for firm in self.a_lst_total_firms:
+            for prod in firm.dct_prod_up_prod_stat.keys():
+                for up_prod in firm.dct_prod_up_prod_stat[prod][
+                        's_stat'].keys():
+                    if firm.dct_prod_up_prod_stat[prod][
+                            's_stat'][up_prod]['lst_disrupt_firm']:
+                        firm.disrupt_cus_prod(prod, up_prod)
 
         for n_trial in range(self.int_n_max_trial):
             # print('=' * 10, 'trial', n_trial, '=' * 10)
@@ -410,7 +418,7 @@ class Model(ap.Model):
             for firm in self.a_lst_total_firms:
                 lst_seek_prod = []
                 for prod in firm.dct_prod_up_prod_stat.keys():
-                    status = firm.dct_prod_up_prod_stat[prod]['status'][-1][0]
+                    status = firm.dct_prod_up_prod_stat[prod]['p_stat'][-1][0]
                     if status == 'D':
                         for supply in firm.dct_prod_up_prod_stat[
                                 prod]['supply'].keys():
@@ -448,11 +456,11 @@ class Model(ap.Model):
                 for prod, dct_status_supply in \
                         firm.dct_prod_up_prod_stat.items():
                     lst_is_normal = [stat == 'N' for stat, _
-                                     in dct_status_supply['status']]
+                                     in dct_status_supply['p_stat']]
                     if not all(lst_is_normal):
                         # print(f"{firm.name} {prod.code}:")
-                        # print(dct_status_supply['status'])
+                        # print(dct_status_supply['p_stat'])
-                        for status, ts in dct_status_supply['status']:
+                        for status, ts in dct_status_supply['p_stat']:
                             db_r = Result(s_id=self.sample.id,
                                           id_firm=firm.code,
                                           id_product=prod.code,

oa_with_exp.csv

@@ -1,37 +1,37 @@
 X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16,X17,X18,X19,X20,X21,X22,X23
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-2,0,0,0,2,2,2,2,2,2,0,0,0,0,0,0,0,0,2,2,2,2,2
+2,0,0,0,2,2,2,2,2,0,0,0,0,0,0,0,0,2,2,2,2,2,2
-0,0,0,0,0,0,0,1,1,1,0,0,1,1,1,1,1,1,1,2,2,2,2
+0,0,0,0,0,0,0,1,1,0,0,1,1,1,1,1,1,1,1,2,2,2,2
-1,0,0,0,1,1,1,2,2,2,0,0,1,1,1,1,1,1,2,0,0,0,0
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-2,0,0,0,2,2,2,0,0,0,0,0,1,1,1,1,1,1,0,1,1,1,1
+2,0,0,0,2,2,2,0,0,0,0,1,1,1,1,1,1,0,0,1,1,1,1
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-2,1,1,0,1,1,1,0,1,0,1,0,1,0,0,0,1,1,0,2,0,2,2
+2,1,1,0,1,1,1,0,1,1,0,1,0,0,0,1,1,0,0,2,0,2,2
-0,1,1,0,2,0,1,2,1,2,0,1,0,1,0,1,1,0,0,1,1,2,0
+0,1,1,0,2,0,1,2,1,0,1,0,1,0,1,1,0,2,0,1,1,2,0
-1,1,1,0,0,1,2,0,2,0,0,1,0,1,0,1,1,0,1,2,2,0,1
+1,1,1,0,0,1,2,0,2,0,1,0,1,0,1,1,0,0,1,2,2,0,1
-2,1,1,0,1,2,0,1,0,1,0,1,0,1,0,1,1,0,2,0,0,1,2
+2,1,1,0,1,2,0,1,0,0,1,0,1,0,1,1,0,1,2,0,0,1,2

oa_without_exp.csv

@@ -1,2 +1,2 @@
-X1,X2,X3,X4,X5,X6,X7,X8,X9,X10
+X1,X2,X3,X4,X5,X6,X7,X8,X9
-0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0

orm.py

@@ -60,7 +60,6 @@ class Experiment(Base):
     cap_limit_prob_type = Column(String(16), nullable=False)
     cap_limit_level = Column(DECIMAL(8, 4), nullable=False)
     diff_new_conn = Column(DECIMAL(8, 4), nullable=False)
-    crit_supplier = Column(DECIMAL(8, 4), nullable=False)
     proactive_ratio = Column(DECIMAL(8, 4), nullable=False)
     remove_t = Column(Integer, nullable=False)
     netw_prf_n = Column(Integer, nullable=False)

xv_with_exp.csv

@@ -1,4 +1,4 @@
-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
+n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,proactive_ratio,remove_t,netw_prf_n
-7,TRUE,TRUE,uniform,5,0.3,2,0.3,3,3
+7,TRUE,TRUE,uniform,5,0.3,0.3,3,3
-5,FALSE,FALSE,normal,10,0.5,1,0.5,5,2
+5,FALSE,FALSE,normal,10,0.5,0.5,5,2
-3,,,,15,0.7,0.5,0.7,7,1
+3,,,,15,0.7,0.7,7,1

xv_without_exp.csv

@@ -1,2 +1,2 @@
-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
+n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,proactive_ratio,remove_t,netw_prf_n
-5,TRUE,TRUE,uniform,10,0.5,1,0,5,2
+5,TRUE,TRUE,uniform,10,0.5,0,5,2