Non Revenue Water (NRW) refers to the volume of water that is distributed from the water plant but does not get billed to customers, which is a major challenge for water bodies.It represents the difference between the total volume of water pumped into the water distribution system (WDS) and the volume actually billed to customers.NRW is composed of three components: physical losses, commercial losses, and unbilled authorized consumption.These losses cause financial deficits, increased operational costs, and infrastructure deterioration, making NRW reduction a critical challenge for water bodies globally. This study reviews optimization strategies for minimizing NRW, focusing on advanced metering infrastructure (AMI), remote leak detection (acoustic, pressure, and flow sensors), Geographic information systems (GIS), data analytics, machine learning, and digital twin modeling. Findings suggest that integrating emerging technologies, predictive analytics, and data-driven decision-making can significantly enhance water distribution efficiency. Future research should focus on AI-driven optimization, predictive maintenance, and sustainable water management strategies to optimize non revenue water.

This article introduces a novel three-phase angular perturbation technique for solving the School Bus Problem (SBP), an NP-hard combinatorial optimization challenge. The three phases—initial solution, global and local search, and solution exploration—focus on optimizing school bus routes by minimizing sharp turns and reducing travel distances. The performance of three metaheuristic algorithms (GAACOSA-AP, BFSPSO-AP, RL-AP) is evaluated, and significant improvements in routing efficiency are observed. we evaluated the performance of our hybrid algorithm against the industry-leading optimization software, CPLEX, and other optimization techniques across multiple school scenarios. The results show that the GAACOSA-AP algorithm consistently outperforms its counterparts, achieving substantial reductions in travel distances, particularly for complex routes with numerous stops. The integration of Angular Perturbation was key to improving route geometry by, reducing sharp turns, and enhancing overall path optimization by an average reduction of 27.12% compared to 22.37% without Angular Perturbation.