The field of optimization and planning for complex systems is rapidly evolving, with a focus on developing innovative solutions to real-world problems. Recent research has emphasized the importance of integrating machine learning and advanced optimization techniques to improve the efficiency and effectiveness of complex systems. One notable trend is the increasing use of large neighborhood search and hybrid genetic algorithms to solve inventory routing and other complex optimization problems. Additionally, there is a growing interest in designing public goods and services that take into account the needs and preferences of multiple stakeholders, using cooperative game theory and linear programming techniques. Noteworthy papers include: Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search, which presents an improved solution method that integrates adaptive large neighborhood search with deep reinforcement learning. Another notable paper is Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems, which develops a new large neighborhood search operator tailored for the inventory routing problem and demonstrates its effectiveness in computational experiments.