generate network pred + succ
This commit is contained in:
138
model.py
138
model.py
@@ -21,6 +21,8 @@ class Model(ap.Model):
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self.int_n_max_trial = int(self.p.n_max_trial)
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self.int_netw_sply_prf_n = int(self.p.netw_sply_prf_n)
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self.flt_netw_sply_prf_size = float(self.p.netw_sply_prf_size)
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self.int_netw_cust_prf_n = int(self.p.netw_cust_prf_n)
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self.flt_netw_cust_prf_size = float(self.p.netw_cust_prf_size)
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self.str_cap_limit_prob_type = str(self.p.cap_limit_prob_type)
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self.flt_cap_limit_level = float(self.p.cap_limit_level)
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self.flt_diff_remove = float(self.p.diff_remove)
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@@ -65,54 +67,54 @@ class Model(ap.Model):
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firm_prod_labels_dict[code] = firm_prod.loc[code].to_dict()
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nx.set_node_attributes(G_FirmProd, firm_prod_labels_dict)
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# # add edge to G_firm according to G_bom
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# for node in nx.nodes(G_Firm):
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# lst_pred_product_code = []
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# for product_code in G_Firm.nodes[node]['Product_Code']:
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# lst_pred_product_code += list(G_bom.predecessors(product_code))
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# lst_pred_product_code = list(set(lst_pred_product_code))
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# # to generate consistant graph
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# lst_pred_product_code = list(sorted(lst_pred_product_code))
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# for pred_product_code in lst_pred_product_code:
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# # for each product predecessor (component) the firm need
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# # get a list of firm producing this component
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# lst_pred_firm = \
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# Firm['Code'][Firm[pred_product_code] == 1].to_list()
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# lst_pred_firm_size_damp = \
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# [G_Firm.nodes[pred_firm]['Revenue_Log'] **
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# self.flt_netw_sply_prf_size
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# for pred_firm in lst_pred_firm]
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# lst_prob = \
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# [size_damp / sum(lst_pred_firm_size_damp)
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# for size_damp in lst_pred_firm_size_damp]
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# # select multiple supplier (multi-sourcing)
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# n_pred_firm = self.int_netw_sply_prf_n
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# if n_pred_firm > len(lst_pred_firm):
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# n_pred_firm = len(lst_pred_firm)
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# lst_choose_firm = self.nprandom.choice(lst_pred_firm,
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# n_pred_firm,
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# replace=False,
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# p=lst_prob)
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# lst_add_edge = [(pred_firm, node,
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# {'Product': pred_product_code})
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# for pred_firm in lst_choose_firm]
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# G_Firm.add_edges_from(lst_add_edge)
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# add edge to G_firm according to G_bom
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for node in nx.nodes(G_Firm):
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lst_pred_product_code = []
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for product_code in G_Firm.nodes[node]['Product_Code']:
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lst_pred_product_code += list(G_bom.predecessors(product_code))
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lst_pred_product_code = list(set(lst_pred_product_code))
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# to generate consistant graph
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lst_pred_product_code = list(sorted(lst_pred_product_code))
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for pred_product_code in lst_pred_product_code:
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# for each product predecessor (component) the firm need
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# get a list of firm producing this component
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lst_pred_firm = \
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Firm['Code'][Firm[pred_product_code] == 1].to_list()
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lst_pred_firm_size_damp = \
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[G_Firm.nodes[pred_firm]['Revenue_Log'] **
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self.flt_netw_sply_prf_size
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for pred_firm in lst_pred_firm]
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lst_prob = \
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[size_damp / sum(lst_pred_firm_size_damp)
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for size_damp in lst_pred_firm_size_damp]
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# select multiple supplier (multi-sourcing)
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n_pred_firm = self.int_netw_sply_prf_n
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if n_pred_firm > len(lst_pred_firm):
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n_pred_firm = len(lst_pred_firm)
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lst_choose_firm = self.nprandom.choice(lst_pred_firm,
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n_pred_firm,
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replace=False,
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p=lst_prob)
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lst_add_edge = [(pred_firm, node,
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{'Product': pred_product_code})
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for pred_firm in lst_choose_firm]
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G_Firm.add_edges_from(lst_add_edge)
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# # graph firm prod
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# set_node_prod_code = set(G_Firm.nodes[node]['Product_Code'])
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# set_pred_succ_code = set(G_bom.successors(pred_product_code))
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# lst_use_pred_prod_code = list(
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# set_node_prod_code & set_pred_succ_code)
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# for pred_firm in lst_choose_firm:
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# pred_node = [n for n, v in G_FirmProd.nodes(data=True)
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# if v['Firm_Code'] == pred_firm and
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# v['Product_Code'] == pred_product_code][0]
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# for use_pred_prod_code in lst_use_pred_prod_code:
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# current_node = \
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# [n for n, v in G_FirmProd.nodes(data=True)
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# if v['Firm_Code'] == node and
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# v['Product_Code'] == use_pred_prod_code][0]
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# G_FirmProd.add_edge(pred_node, current_node)
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# graph firm prod
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set_node_prod_code = set(G_Firm.nodes[node]['Product_Code'])
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set_pred_succ_code = set(G_bom.successors(pred_product_code))
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lst_use_pred_prod_code = list(
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set_node_prod_code & set_pred_succ_code)
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for pred_firm in lst_choose_firm:
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pred_node = [n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == pred_firm and
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v['Product_Code'] == pred_product_code][0]
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for use_pred_prod_code in lst_use_pred_prod_code:
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current_node = \
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[n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == node and
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v['Product_Code'] == use_pred_prod_code][0]
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G_FirmProd.add_edge(pred_node, current_node)
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# nx.to_pandas_adjacency(G_Firm).to_csv('adj_g_firm.csv')
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# nx.to_pandas_adjacency(G_FirmProd).to_csv('adj_g_firm_prod.csv')
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@@ -121,6 +123,12 @@ class Model(ap.Model):
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if G_Firm.degree(node) == 0:
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for product_code in G_Firm.nodes[node]['Product_Code']:
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# unconnect node does not have possible suppliers
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# current node in graph firm prod
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current_node = \
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[n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == node and
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v['Product_Code'] == product_code][0]
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lst_succ_product_code = list(
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G_bom.successors(product_code))
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# different from for different types of product,
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@@ -131,8 +139,8 @@ class Model(ap.Model):
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# for each product successor (finished product)
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# the firm sells to,
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# get a list of firm producing this finished product
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lst_succ_firm = \
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Firm['Code'][Firm[succ_product_code] == 1].to_list()
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lst_succ_firm = Firm['Code'][
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Firm[succ_product_code] == 1].to_list()
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lst_succ_firm_size_damp = \
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[G_Firm.nodes[succ_firm]['Revenue_Log'] **
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self.flt_netw_cust_prf_size
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@@ -149,27 +157,17 @@ class Model(ap.Model):
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replace=False,
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p=lst_prob)
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lst_add_edge = [(node, succ_firm,
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{'Product': pred_product_code})
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{'Product': product_code})
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for succ_firm in lst_choose_firm]
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G_Firm.add_edges_from(lst_add_edge)
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# graph firm prod
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set_node_prod_code = set(
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G_Firm.nodes[node]['Product_Code'])
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set_pred_succ_code = set(
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G_bom.successors(pred_product_code))
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lst_use_pred_prod_code = list(
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set_node_prod_code & set_pred_succ_code)
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for pred_firm in lst_choose_firm:
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pred_node = [n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == pred_firm and
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v['Product_Code'] == pred_product_code][0]
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for use_pred_prod_code in lst_use_pred_prod_code:
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current_node = \
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[n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == node and
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v['Product_Code'] == use_pred_prod_code][0]
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G_FirmProd.add_edge(pred_node, current_node)
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for succ_firm in lst_choose_firm:
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succ_node = \
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[n for n, v in G_FirmProd.nodes(data=True)
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if v['Firm_Code'] == succ_firm and
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v['Product_Code'] == succ_product_code][0]
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G_FirmProd.add_edge(current_node, succ_node)
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self.sample.g_firm = json.dumps(nx.adjacency_data(G_Firm))
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self.firm_network = ap.Network(self, G_Firm)
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@@ -336,10 +334,10 @@ class Model(ap.Model):
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'Product': di_supp_prod.code
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})
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])
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# print(f"proactive add {select_cand.code} to "
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# f"{pro_firm.code} "
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# f"for {di_supp_node['Firm_Code']} "
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# f"{di_supp_node['Product_Code']}")
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print(f"proactive add {select_cand.code} to "
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f"{pro_firm.code} "
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f"for {di_supp_node['Firm_Code']} "
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f"{di_supp_node['Product_Code']}")
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# change capacity
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select_cand.dct_prod_capacity[di_supp_prod] -= 1
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break
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