pyomo Неявная замена атрибута Component

Я работаю над сценарием оптимизации pyomo. Одно из моих ограничений - неявная замена. Ищу идеи о том, как решить эту проблему.
Данные загружаются в объекты модели из базы данных через списки Python и dicts. Ниже приведены параметры, переменные, целевая функция и ограничения. Оскорбительное ограничение обозначено заглавными буквами.

#Sets
#clinics
model.C = Set(initialize=list(clinics.keys()))

#clients
model.B = Set(initialize=list(client_blocks.keys()))

#scalar params
model.clt_stf_max = 50
model.tt_max = 45
model.stf_max = sum(staff_cap.values()) 
model.tot_unsrvd_clients = sum(client_blocks.values())
model.z_M = 5000 
model.e_M = 500

#indexed params
model.cnc_stf_cap = Param(C, initialize=staff_cap)
model.clt_blk = Param(B, initialize=client_blocks)
model.trav_time = Param(T, initialize=trav_time)

#decision vars
#x new staff at each clinic
model.new_stf = Var(model.C, domain=NonNegativeIntegers, initialize=0) 

#y  new clients at each clinic/block combo  
model.new_clt = Var(model.C, model.B, domain=NonNegativeIntegers, initialize=0) 

#z  clinic has client(s) 
model.z =  Var(model.C, model.B, domain=Binary, initialize=0)

#e clinic has staff 
model.e = Var(model.C, domain=Binary, initialize=0) 

#objective function
def o_min_tt_rule(model):
    return sum(model.trav_time[c,b]*model.new_clt[c,b] for c in model.C for b in model.B)
model.o_min_tt = Objective(rule=o_min_tt_rule, sense=minimize)

#constraints
# limit new clients at clinic to staff capacity  
def limit_clients_to_clinic_staff_cap_rule(model, c):
    return sum(model.new_clt[c,b] for c in model.C for b in model.B) <= (model.cnc_stf_cap[c] * model.clt_stf_max)
model.limit_clients_to_clinic_staff_cap = Constraint(model.C, rule=limit_clients_to_clinic_staff_cap_rule)

#total of new clients served in block should not exceed the number new clients in the block 
def limit_newclient_block_rule(model, b):
    return sum(model.new_clt[c,b] for c in model.C for b in model.B) <= (model.clt_blk[b])
model.limit_newclient_block = Constraint(model.B, rule=limit_newclient_block_rule)

#limit new clients to selected clinics
def client_to_selected_clinic_rule(model, c, b):
   return model.new_clt[c,b] <= model.z[c,b] * model.z_M
model.client_to_selected_clinic = Constraint(model.C, model.B, rule=client_to_selected_clinic_rule)

#limit single client travel time to max travel time minutes
def limit_client_travtime_rule(model, c, b):
    return (model.trav_time[c,b] * model.z[c,b]) <= model.tt_max
model.limit_client_travtime = Constraint(model.C, model.B, rule=limit_client_travtime_rule)

#limit selected clinics to max number
def staff_to_selected_clinic_rule(model):
    return summation(model.e) <= model.selected_clinic_max
model.staff_to_selected_clinic = Constraint(rule=staff_to_selected_clinic_rule,)

#THIS CAUSES THE ERROR
#limit new staff to selected clinics
def staff_to_selected_clinic_rule(model, c):
    return model.new_stf[c] <= model.e[c] * model.e_M
model.staff_to_selected_clinic = Constraint(model.C, rule=staff_to_selected_clinic_rule)

#limit new staff at clinic to clinic capacity
def limit_staff_to_clnic_cap_rule(model, c):
    return model.new_stf[c] <= model.cnc_stf_cap[c]
model.limit_staff_to_clnic_cap = Constraint(model.C, rule=limit_staff_to_clnic_cap_rule)

#limit total new staff to staff max   
def limit_tot_staff_rule(model):
    return summation(model.new_stf) <= model.stf_max
model.limit_tot_staff = Constraint(rule=limit_tot_staff_rule)