Branch-and-Bound (B&B) is the dominant exact solution method for Mixed Integer Linear Programs (MILP), yet its exponential time complexity poses significant challenges for large-scale instances. The growing capabilities of machine learning have spurred efforts to improve B&B by learning data-driven branching policies. However, most existing approaches rely on Imitation Learning (IL), which tends to overfit to expert demonstrations and struggles to generalize to structurally diverse or unseen instances. In this work, we propose Tree-Gate Proximal Policy Optimization (TGPPO), a novel framework that employs Proximal Policy Optimization (PPO), a Reinforcement Learning (RL) algorithm, to train a branching policy aimed at improving generalization across heterogeneous MILP instances. Our approach builds on a parameterized state space representation that dynamically captures the evolving context of the search tree. Empirical evaluations show that TGPPO often outperforms existing learning-based policies in terms of reducing the number of nodes explored and improving p-Primal-Dual Integrals (PDI), particularly in out-of-distribution instances. These results highlight the potential of RL to develop robust and adaptable branching strategies for MILP solvers.

Predictive Maintenance (PdM) is crucial in manufacturing, enabling early detection of equipment failures and optimized maintenance to improve reliability and reduce costs. Yet, extracting accurate and explainable predictions from complex time-series data remains challenging. Deep learning models such as Long Short-Term Memory (LSTM) networks offer strong predictive power but act as ‘black boxes,’ while fuzzy systems provide interpretability but lack robustness in dynamic industrial settings. To address these limitations, recent studies have integrated fuzzy inference with LSTMs. In this paper, we propose FiLSTM (Fuzzy Rule Induction for LSTM), a hybrid model that leverages Decision Trees to generate fuzzy rules and membership functions directly from data. This approach enhances both interpretability and prediction, achieving higher accuracy, faster computation, and lower execution time compared to FLSTM. FiLSTM advances PdM systems toward real-time, scalable, and transparent industrial applications.

International audience

International audience