Decision Optimization

Hi everyone,

I have found the situation when function alwaysConstant is not working with OPL. The problem occurs when constraints using alwaysConstant are enumerated in a loop. However, such problem doesn't occur when modeling with Python.

1 case (OPL with loop)

using CP;int period_size = 50;range steps = 0..3;stateFunction my_state_function;subject to {  forall( h in steps )   {    alwaysConstant(my_state_function, h * period_size, (h + 1) * period_size);  }}execute{  writeln(my_state_function);}

gives

Computation error: an overflow occurred.

2 case (OPL without loop)

using CP;int period_size = 50;range steps = 0..3;stateFunction my_state_function;subject to {  alwaysConstant (my_state_function, 0, 50);  alwaysConstant (my_state_function, 50, 100);  alwaysConstant (my_state_function, 100, 150);  alwaysConstant (my_state_function, 150, 200);}execute{  writeln(my_state_function);}

gives

stepwise{ -1 -> 0; 0 -> 200; -1 }

3 case (Python with loop)

import docplex.cp.model as cp_modelif __name__ == "__main__":    model = cp_model.CpoModel("alwaysConstant with cycle")    period_size = 50    step_max = 3    my_state_function = cp_model.state_function(name="my state function")    for h in range(step_max + 1):        model.add(cp_model.always_constant(my_state_function, (h * period_size, (h + 1) * period_size)))    solution = model.solve(TimeLimit=10, trace_log=False)    print(solution.get_var_solution(my_state_function))

gives

my state function: ((-4503599627370494, 0, -1), (0, 50, 0), (50, 100, 0), (100, 150, 0), (150, 200, 0), (200, 4503599627370494, -1))

4 case (Python without loop)

import docplex.cp.model as cp_modelif __name__ == "__main__":    model = cp_model.CpoModel("alwaysConstant without cycle")    period_size = 50    step_max = 3    my_state_function = cp_model.state_function(name="my state function")    model.add(cp_model.always_constant(my_state_function, (0, 50)))    model.add(cp_model.always_constant(my_state_function, (50, 100)))    model.add(cp_model.always_constant(my_state_function, (100, 150)))    model.add(cp_model.always_constant(my_state_function, (150, 200)))    solution = model.solve(TimeLimit=10, trace_log=False)    print(solution.get_var_solution(my_state_function))

gives

my state function: ((-4503599627370494, 0, -1), (0, 50, 0), (50, 100, 0), (100, 150, 0), (150, 200, 0), (200, 4503599627370494, -1))

------------------------------
Rustam Salikhov
------------------------------

Hello Rustam,

Unfortunately, you've found a bug in the OPL.
We are working to fix it.
We are very sorry for the inconvenience.
In the meantime you can use the following workaround.

range steps1 = 0..4;int k[i in steps1]= i*period_size;...   alwaysConstant(my_state_function, k[h], k[h+1]);

I hope this helps.

Hi everyone,

I have found the situation when function alwaysConstant is not working with OPL. The problem occurs when constraints using alwaysConstant are enumerated in a loop. However, such problem doesn't occur when modeling with Python.

1 case (OPL with loop)

using CP;int period_size = 50;range steps = 0..3;stateFunction my_state_function;subject to {  forall( h in steps )   {    alwaysConstant(my_state_function, h * period_size, (h + 1) * period_size);  }}execute{  writeln(my_state_function);}

gives

Computation error: an overflow occurred.

2 case (OPL without loop)

using CP;int period_size = 50;range steps = 0..3;stateFunction my_state_function;subject to {  alwaysConstant (my_state_function, 0, 50);  alwaysConstant (my_state_function, 50, 100);  alwaysConstant (my_state_function, 100, 150);  alwaysConstant (my_state_function, 150, 200);}execute{  writeln(my_state_function);}

gives

stepwise{ -1 -> 0; 0 -> 200; -1 }

3 case (Python with loop)

import docplex.cp.model as cp_modelif __name__ == "__main__":    model = cp_model.CpoModel("alwaysConstant with cycle")    period_size = 50    step_max = 3    my_state_function = cp_model.state_function(name="my state function")    for h in range(step_max + 1):        model.add(cp_model.always_constant(my_state_function, (h * period_size, (h + 1) * period_size)))    solution = model.solve(TimeLimit=10, trace_log=False)    print(solution.get_var_solution(my_state_function))

gives

my state function: ((-4503599627370494, 0, -1), (0, 50, 0), (50, 100, 0), (100, 150, 0), (150, 200, 0), (200, 4503599627370494, -1))

4 case (Python without loop)

import docplex.cp.model as cp_modelif __name__ == "__main__":    model = cp_model.CpoModel("alwaysConstant without cycle")    period_size = 50    step_max = 3    my_state_function = cp_model.state_function(name="my state function")    model.add(cp_model.always_constant(my_state_function, (0, 50)))    model.add(cp_model.always_constant(my_state_function, (50, 100)))    model.add(cp_model.always_constant(my_state_function, (100, 150)))    model.add(cp_model.always_constant(my_state_function, (150, 200)))    solution = model.solve(TimeLimit=10, trace_log=False)    print(solution.get_var_solution(my_state_function))

gives

my state function: ((-4503599627370494, 0, -1), (0, 50, 0), (50, 100, 0), (100, 150, 0), (150, 200, 0), (200, 4503599627370494, -1))

------------------------------
Rustam Salikhov
------------------------------