init extra capacity in firm.py

controller_db.py

@@ -129,7 +129,7 @@ class ControllerDB:
                          n_max_trial, prf_size, prf_conn,
                          cap_limit_prob_type, cap_limit_level,
                          diff_new_conn, crit_supplier, diff_remove,
-                         proactive_ratio, netw_prf_n):
+                         proactive_ratio, drop_t, netw_prf_n):
         e = Experiment(
             idx_scenario=idx_scenario,
             idx_init_removal=idx_init_removal,
@@ -146,6 +146,7 @@ class ControllerDB:
             crit_supplier=crit_supplier,
             diff_remove=diff_remove,
             proactive_ratio=proactive_ratio,
+            drop_t=drop_t,
             netw_prf_n=netw_prf_n
         )
         db_session.add(e)

firm.py

@@ -12,7 +12,7 @@ class FirmAgent(ap.Agent):
         self.type_region = type_region
         self.revenue_log = revenue_log
         self.a_lst_product = a_lst_product
-        self.dct_prod_capacity = dict.fromkeys(self.a_lst_product)
+        self.dct_prod_capacity = {}
 
         # self.a_lst_up_product_removed = ap.AgentList(self.model, [])
         # self.a_lst_product_disrupted = ap.AgentList(self.model, [])
@@ -35,9 +35,32 @@ class FirmAgent(ap.Agent):
         # para
         self.is_prf_size = self.model.is_prf_size
         self.is_prf_conn = bool(self.p.prf_conn)
+        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 extra capacity
+        for product in self.a_lst_product:
+            # init extra capacity based on discrete uniform distribution
+            assert self.str_cap_limit_prob_type in ['uniform', 'normal'], \
+                "cap_limit_prob_type other than uniform, normal"
+            if self.str_cap_limit_prob_type == 'uniform':
+                extra_cap_mean = \
+                    self.revenue_log / self.flt_cap_limit_level
+                extra_cap = self.model.nprandom.integers(extra_cap_mean-2,
+                                                         extra_cap_mean+2)
+                extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
+                # print(firm_agent.name, extra_cap)
+                self.dct_prod_capacity[product] = extra_cap
+            elif self.str_cap_limit_prob_type == 'normal':
+                extra_cap_mean = \
+                    self.revenue_log / self.flt_cap_limit_level
+                extra_cap = self.model.nprandom.normal(extra_cap_mean, 1)
+                extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
+                # print(firm_agent.name, extra_cap)
+                self.dct_prod_capacity[product] = extra_cap
+
     def remove_edge_to_cus_remove_cus_up_prod(self, remove_product):
         lst_out_edge = list(
             self.firm_network.graph.out_edges(

model.py

@@ -18,10 +18,9 @@ class Model(ap.Model):
 
         self.int_n_max_trial = int(self.p.n_max_trial)
         self.is_prf_size = bool(self.p.prf_size)
-        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_remove = float(self.p.diff_remove)
         self.proactive_ratio = float(self.p.proactive_ratio)
+        self.drop_t = int(self.p.drop_t)
         self.int_netw_prf_n = int(self.p.netw_prf_n)
 
         # init graph bom
@@ -203,25 +202,6 @@ class Model(ap.Model):
                     code in attr['Product_Code']
                     for code in self.a_lst_total_products.code
                 ]))
-            for product in firm_agent.a_lst_product:
-                # init extra capacity based on discrete uniform distribution
-                assert self.str_cap_limit_prob_type in ['uniform', 'normal'], \
-                    "cap_limit_prob_type other than uniform, normal"
-                if self.str_cap_limit_prob_type == 'uniform':
-                    extra_cap_mean = \
-                        firm_agent.revenue_log / self.flt_cap_limit_level
-                    extra_cap = self.nprandom.integers(extra_cap_mean-2,
-                                                       extra_cap_mean+2)
-                    extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
-                    # print(firm_agent.name, extra_cap)
-                    firm_agent.dct_prod_capacity[product] = extra_cap
-                elif self.str_cap_limit_prob_type == 'normal':
-                    extra_cap_mean = \
-                        firm_agent.revenue_log / self.flt_cap_limit_level
-                    extra_cap = self.nprandom.normal(extra_cap_mean, 1)
-                    extra_cap = 0 if round(extra_cap) < 0 else round(extra_cap)
-                    # print(firm_agent.name, extra_cap)
-                    firm_agent.dct_prod_capacity[product] = extra_cap
 
             self.firm_network.add_agents([firm_agent], [ag_node])
         self.a_lst_total_firms = ap.AgentList(self, self.firm_network.agents)
@@ -406,6 +386,7 @@ class Model(ap.Model):
             # seek_alt_supply
             # shuffle self.a_lst_total_firms
             self.a_lst_total_firms = self.a_lst_total_firms.shuffle()
+            is_stop_trial = True
             for firm in self.a_lst_total_firms:
                 # if len(firm.a_lst_up_product_removed) > 0:
                 #     firm.seek_alt_supply()
@@ -420,8 +401,12 @@ class Model(ap.Model):
                                 lst_seek_prod.append(supply)
                 # commmon supply only seek once
                 lst_seek_prod = list(set(lst_seek_prod))
+                if len(lst_seek_prod) > 0:
+                    is_stop_trial = False
                 for supply in lst_seek_prod:
                     firm.seek_alt_supply(supply)
+            if is_stop_trial:
+                break
 
             # handle_request
             # shuffle self.a_lst_total_firms

oa_without_exp.csv

@@ -1,2 +1,2 @@
-X1,X2,X3,X4,X5,X6,X7,X8,X9,X10
-0,0,0,0,0,0,0,0,0,0
+X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11
+0,0,0,0,0,0,0,0,0,0,0

orm.py

@@ -63,6 +63,7 @@ class Experiment(Base):
     crit_supplier = Column(DECIMAL(8, 4), nullable=False)
     diff_remove = Column(DECIMAL(8, 4), nullable=False)
     proactive_ratio = Column(DECIMAL(8, 4), nullable=False)
+    drop_t = Column(Integer, nullable=False)
     netw_prf_n = Column(Integer, nullable=False)
 
     sample = relationship(

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

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,diff_remove,proactive_ratio,netw_prf_n
-10,TRUE,TRUE,uniform,10,0.1,1,0.01,0,2
+n_max_trial,prf_size,prf_conn,cap_limit_prob_type,cap_limit_level,diff_new_conn,crit_supplier,diff_remove,proactive_ratio,drop_t,netw_prf_n
+10,TRUE,TRUE,uniform,10,0.1,1,0.01,0,5,2