How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss function
This work addresses the challenge of applying machine learning to optimization problems, but it is incremental as it extends prior research with more advanced algorithms and a specific case study.
This paper tackles the problem of predicting good solutions for constraint optimization problems using machine learning, focusing on the importance of loss functions and error criteria. The results show that LightGBM outperforms other models, particularly when using mean absolute deviation, in a blood transshipment case study.
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.