def objective_rule(model):
Workers_Cost = model.Ca * (model.alpha_put + model.alpha_pick) + \
model.Cb * sum((model.beta_put[t] + model.beta_pick[t])
for t in model.TIMES)
MHE_Cost = summation(model.MHE_cost)
Tech_Cost = summation(model.Cth_put, model.theta_put) + \
summation(model.Cth_pick, model.theta_pick)
Holding_Cost = sum(model.C_hp[p] * model.y_pt[p,t]
for p in model.PRODUCTS for t in model.TIMES)
Holding_Cost += sum(model.C_hq[q] * model.X_osq[s,q] * model.tau_sq[s,q]
for s in model.STORES for q in model.FASHION)
Holding_Cost += sum(model.C_hsp[s,p] * model.y_spt[s,p,t]
for s in model.STORES for p in model.PRODUCTS for t in model.TIMES)
Holding_Cost += sum(model.C_hsq[s,q] * model.y_sqt[s,q,t]
for s in model.STORES for q in model.FASHION for t in model.TIMES)
Fixed_Shipping_Cost = sum(model.C_fv[v] * model.n_vt[v,t]
for v in model.VENDORS for t in model.TIMES)
Fixed_Shipping_Cost += sum(model.C_fs[s] * model.n_st[s,t]
for s in model.STORES for t in model.TIMES)
Var_Shipping_Cost = sum(model.C_vv[v] * model.W_p[p] * model.x_vpt[v,p,t]
for v, p in model.OMEGA_P for t in model.TIMES)
Var_Shipping_Cost += sum(model.C_vv[v] * model.W_q[q] * model.X_ivq[v,q] * model.rho_vqt[v,q,t]
for v, q in model.OMEGA_Q for t in model.TIMES)
Var_Shipping_Cost += sum(model.C_vs[s] * model.W_p[p] * model.x_spt[s,p,t]
for s in model.STORES for p in model.PRODUCTS for t in model.TIMES)
Var_Shipping_Cost += sum(model.C_vs[s] * model.W_q[q] * model.X_osq[s,q] * model.rho_sqt[s,q,t]
for s in model.STORES for q in model.FASHION for t in model.TIMES)
FS_Expr = (Workers_Cost + MHE_Cost + Tech_Cost + Holding_Cost
+ Fixed_Shipping_Cost + Var_Shipping_Cost)
return FS_Expr
def objective(model):
"""Abstract representation of our model objective."""
obj = pyomo.summation(model.c, model.x)
return obj
def obj_rule(m):
return pyomo.summation(m.costs)
def res_co2_emission_rule(m):
return (pyomo.summation(m.co2_pro_out) * m.weight <=
m.commodity.loc['Global', 'CO2', 'Env']['max'])
def obj_rule(m):
return pyomo.summation(m.flow) # == short for sum{a1,a2} flow[a1,a2]
def res_co2_emission_rule(m):
return pyomo.summation(m.co2_pro_out) <= \
m.commodity.loc['CO2','Env']['max']
def obj_rule(m):
""" Return sum of total costs over all cost types.
Simply calculates the sum of m.costs over all m.cost_types.
"""
return pyomo.summation(m.costs)
def constraint4_rule(model):
expr = model.gamma * model.A_put - \
summation(model.Lambda_put, model.zeta_put)
return (None, expr, 0)
def constraint7_rule(model):
expr = model.gamma * model.A_pick - \
summation(model.Lambda_pick, model.zeta_pick)
return (None, expr, 0)
def constraint2_rule(model):
return (summation(model.theta_pick), 1)
def constraint1_rule(model):
return (summation(model.theta_put), 1)
def objectiveB_rule(model):
expr = model.Ca * (model.alpha_put + model.alpha_pick) + \
summation(model.Cth_put, model.theta_put) + \
summation(model.Cth_pick, model.theta_pick)
return (expr, model.FirstStageCost)
def obj_rule(m):
""" Return sum of total costs over all cost types.
Simply calculates the sum of m.costs over all m.cost_types.
"""
return pyomo.summation(m.costs)
def res_co2_emission_rule(m):
return pyomo.summation(m.co2_pro_out) <= \
m.commodity.loc['CO2','Env']['max']