Editorial: Special issue on operations research and machine learning

Many machine learning techniques work through optimizing specific objective functions.Supervised learning techniques are to minimize the prediction error such as mean square error (MSE) and misclassification rate, or maximize the conditional likelihood, posterior probability, etc. Unsupervised learning techniques usually group instances into clusters in a way that instances within each group are optimally similar while they are distant from instances in other groups.In reinforcement learning, the goal of an agent is to maximize its cumulative reward.However, there is still room to exploit optimization and operations research (OR) in machine learning, and vice versa.Both machine learning and OR can gain advantages through integration and interaction.Optimization and OR techniques play a pivotal role in mitigating machine learning challenges.From feature selection to handling incomplete data and imbalance learning, they can enhance model accuracy and diversity.Their versatile applications encompass addressing bias, selecting optimal training sets, and developing classifiers that minimize misclassification errors across classes, offering comprehensive solutions to multiple machine learning hurdles.Every machine learning technique has several hyperparameters that should be tuned to select the model that achieves the best performance on the learning task at hand.Normally, there are multiple criteria (or objectives) such as bias, variance, complexity, level of explainability, and fairness to be considered in model selection.The existing approach to address multiple criteria in machine learning is to transform the problem into a single-objective optimization problem using, for example, a weighted sum approach.However, this bears the issue of setting the importance of objectives in one way or the other, which is not a straightforward task, nor does the single solution to a multi-objective problem provide insights into the trade-offs between the objectives being optimized.Multi-objective optimization and multi-criteria decisionmaking as OR techniques can provide an opportunity to meet these criteria in machine learning.On the other hand, machine learning techniques can contribute to finding the optimal solutions and making the best decision efficiently.Machine learning techniques can automate the process of the problem reduction in combinatorial optimization.Unsupervised learning can be used in Pareto pruning methods for multi-(or many-) objective optimization problems.Supervised learning can guide solutions through iterations to achieve convergence faster.Reinforcement learning can learn optimal controllers during the optimization process to improve the performance of optimizers.We are pleased to publish this special issue on 'Operations Research and Machine Learning' consisting of five articles.In 'Real-Time Production Scheduling Using a Deep Reinforcement Learning-Based Multi-Agent Approach', Namoura et al. introduce a novel Deep Reinforcement

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