How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss function

TL;DR

This paper explores how advanced machine learning models, especially LightGBM with mean absolute deviation loss, can effectively predict solutions for large-scale stochastic optimization problems, demonstrating promising results in a case study.

Contribution

It extends prior work by analyzing the impact of different algorithms and loss functions on solution prediction accuracy for optimization problems.

Findings

LightGBM outperforms other models in solution accuracy

Mean absolute deviation loss improves prediction quality

Case study on blood transshipment problem validates approach

Abstract

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.