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My goal is to write the following constraint with the doxplex.mp.model with Python API: P[t,j] >= s[t-j+1] - sum(s[k=t-j+2] from k to t) for t = 1,....,N, j = 1,...,t
my code.
from docplex.mp.model import Model
clsp = Model(name = 'capacitated lot sizing problem')
no_of_period = 8
period_list = [t for t in range(1, no_of_period+1)]
s_indicator = clsp.binary_var_dict(period_list, name = 's_indicator')
p_indicator = clsp.binary_var_matrix(period_list, period_list, name = 'p_indicator')
m_8 = clsp.add_constraints(p_indicator[t,j] >= s_indicator[t-j+1] - clsp.sum(s_indicator[t-j+2] for j in range(1,t+1))
for j in t for t in period_list )
Output: Keyerror 0 Any help would be appreciated @vlkong
The text was updated successfully, but these errors were encountered:
@HengChheangsum(s[k=t-j+2] from k to t) is not clear to me.
Isn't it something like:
sums = { (t,j) : clsp.sum(s_indicator[k-j+2] for k in range(j,t)) for t in period_list for j in range(1,t+1)}
m_8 = clsp.add_constraints( p_indicator[t,j] >= (s_indicator[t-j+1] - sums[t,j]) for t in period_list for j in range(1,t+1) )
@vberaudi It is actually P[t,j] >= s[t-j+1] - sum(s[t-j+2] from t-j+2 to t) for t = 1,....,N, j = 1,...,t
my code.
from docplex.mp.model import Model
clsp = Model(name = 'capacitated lot sizing problem')
no_of_period = 8
period_list = [t for t in range(1, no_of_period+1)]
s_indicator = clsp.binary_var_dict(period_list, name = 's_indicator')
p_indicator = clsp.binary_var_matrix(period_list, period_list, name = 'p_indicator')
m_8 = clsp.add_constraints(p_indicator[t,j] >= s_indicator[t-j+1] - clsp.sum(s_indicator[t-j+2] for j in range(1,t+1))
for j in t for t in period_list )
My goal is to write the following constraint with the doxplex.mp.model with Python API: P[t,j] >= s[t-j+1] - sum(s[k=t-j+2] from k to t) for t = 1,....,N, j = 1,...,t
my code.
from docplex.mp.model import Model
clsp = Model(name = 'capacitated lot sizing problem')
no_of_period = 8
period_list = [t for t in range(1, no_of_period+1)]
s_indicator = clsp.binary_var_dict(period_list, name = 's_indicator')
p_indicator = clsp.binary_var_matrix(period_list, period_list, name = 'p_indicator')
m_8 = clsp.add_constraints(p_indicator[t,j] >= s_indicator[t-j+1] - clsp.sum(s_indicator[t-j+2] for j in range(1,t+1))
for j in t for t in period_list )
Output: Keyerror 0 Any help would be appreciated
@vlkong
The text was updated successfully, but these errors were encountered: