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

arXiv: https://arxiv.org/abs/2105.06618 · HTML: https://mlsanity.com/paper/2105.06618/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 6 of 6)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [A General and Adaptive Robust Loss Function](https://mlsanity.com/paper/1701.03077/) (2017)
- [Kaggle forecasting competitions: An overlooked learning opportunity](https://mlsanity.com/paper/2009.07701/) (2020)
- [Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information](https://mlsanity.com/paper/1807.11876/) (2018)
- [Machine learning applications in time series hierarchical forecasting](https://mlsanity.com/paper/1912.00370/) (2019)

## Built on by (most cited first, 5 of 5)

- [How to predict and optimise with asymmetric error metrics](https://mlsanity.com/paper/2211.13586/) (2022)
- [Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure](https://mlsanity.com/paper/2308.03472/) (2023)
- [The intersection of machine learning with forecasting and optimisation: theory and applications](https://mlsanity.com/paper/2211.13583/) (2022)
- [Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework](https://mlsanity.com/paper/2312.03413/) (2023)
- [Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment](https://mlsanity.com/paper/2404.14635/) (2024)

## Abstract (arXiv)

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 citeabbasi2020predicting 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 citeabbasi2020predicting specially when mean absolute deviation criterion is used.