  Using machine learning in solving constraint optimization and combinatorial
problems is becoming an active research area in both computer science and
operations research communities. This paper aims to predict a good solution for
constraint optimization problems using advanced machine learning techniques. It
extends the work of \cite{abbasi2020predicting} to use machine learning models
for predicting the solution of large-scaled stochastic optimization models by
examining more advanced algorithms and various costs associated with the
predicted values of decision variables. It also investigates the importance of
loss function and error criterion in machine learning models where they are
used for predicting solutions of optimization problems. We use a blood
transshipment problem as the case study. The results for the case study show
that LightGBM provides promising solutions and outperforms other machine
learning models used by \cite{abbasi2020predicting} specially when mean
absolute deviation criterion is used.
