Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, and user preferences. This paper addresses the optimization challenges in V2G systems and explores the use of artificial intelligence (AI) methods to tackle these challenges. The paper provides a comprehensive analysis of existing work on optimization in V2G systems and identifies gaps where AI-driven algorithms, machine learning, metaheuristic extensions, and agile optimization concepts can be applied. Case studies and examples demonstrate the efficacy of AI-driven algorithms in optimizing V2G operations, leading to improved grid stability, cost optimization, and user satisfaction. Furthermore, agile optimization concepts are introduced to enhance flexibility and responsiveness in V2G optimization. The paper concludes with a discussion on the challenges and future directions for integrating AI-driven methods into V2G systems, highlighting the potential for these intelligent algorithms and methods.

The adoption of electric vehicles (EVs) has gained significant momentum in recent years as a sustainable alternative to traditional internal combustion engine vehicles. However, the efficient utilization of batteries in EVs, coupled with the growing demand for sustainable transportation, has posed complex challenges for battery management in the context of electric vehicle routing problems in a broad sense, which includes vehicle routing problems, team orienteering problems, and arc routing problems, all of them using EVs. This paper presents a comprehensive review of the state-of-the-art approaches, methodologies, and strategies for battery management in each of the aforementioned optimization problems. We explore the relevant factors influencing battery performance and the interplay between routing, charging, and energy management in the context of EVs. The paper also discusses the advances in optimization algorithms, vehicle-to-grid integration, and intelligent decision-making techniques aimed at enhancing the range, reliability, and sustainability of EV operations. Key findings indicate a paradigm shift towards addressing uncertainties, dynamic conditions, and synchronization challenges inherent in large-scale and dynamic routing problems within the context of EVs that require efficient battery management.

Anomalous diffusion is ubiquitous in systems ranging from intracellular transport and porous-medium flow to animal foraging, and its quantification requires robust methods that cope with short and noisy trajectories. We present AnomalousNet, a unified three-stage pipeline for analyzing anomalous particle dynamics. First, up to 64 particles per 128 × 128 field of view are tracked using the Crocker–Grier algorithm via Trackpy. Next, an Attention U-Net trained on over 8.6 × 10⁴ simulated experiments infers frame-wise anomalous exponents α, generalized diffusion coefficients K, and discrete motion states. Finally, regime changes are identified using an L2-regularized, windowed pruned exact linear time change-point detection algorithm. On the 2nd Anomalous Diffusion Challenge benchmark the method achieved MAE ( α ) = 0.32 , MSLE ( K ) = 0.10 , state-classification F 1 = 0.93 , and change-point RMSE = 0.09, ranking second in the video single-trajectory task and ensemble task. These results demonstrate precise discrimination of subdiffusive, normal, and superdiffusive regimes, and frame-level identification of state transitions establishing AnomalousNet as a powerful tool for quantitative analysis of heterogeneous diffusion in video data.

This paper presents a reinforcement learning (RL) approach for solving the team orienteering problem under both deterministic and dynamic travel time conditions. The proposed method builds on the transformer architecture and is trained to construct routes that adapt to real-time variations, such as traffic and environmental changes. A key contribution of this work is the model’s ability to generalize across problem instances with varying numbers of nodes and vehicles, eliminating the need for retraining when problem size changes. To assess performance, a comprehensive set of experiments involving 27,000 synthetic instances is conducted, comparing the RL model with a variable neighborhood search metaheuristic. The results indicate that the RL model achieves competitive solution quality while requiring significantly less computational time. Moreover, the RL approach consistently produces feasible solutions across all dynamic instances, demonstrating strong robustness in meeting time constraints. These findings suggest that learning-based methods can offer efficient, scalable, and adaptable solutions for routing problems in dynamic and uncertain environments.

This paper introduces a sim-exact methodology for stochastic combinatorial optimization problems. The approach combines exact optimization models with Monte Carlo or discrete-event simulation to evaluate candidate solutions under uncertainty. The method iteratively adjusts a control parameter based on simulation feedback and solves a sequence of deterministic optimization problems. Unlike scenario-based stochastic programming, the approach does not rely on explicit scenario enumeration, and unlike simheuristics, it preserves optimality with respect to each deterministic subproblem. The methodology is tested on the vehicle routing problem with stochastic demands under different levels of demand variability. Results are compared with a simheuristic approach and a sample average approximation (SAA) method. The results show that sim-exact performance is comparable to simheuristics, with no statistically significant differences in most cases, while SAA shows weaker performance under medium and high variability.