The recent developments in the field of scheduling and matching algorithms have seen significant advancements, particularly in addressing complex and dynamic scenarios. Researchers are increasingly focusing on approximation algorithms that can handle real-time changes and uncertainties, such as those in polyamorous scheduling and dynamic carpooling. These algorithms are designed to maintain optimal or near-optimal solutions under varying conditions, which is crucial for applications in social networks and transportation systems. Additionally, there has been progress in improving the efficiency and accuracy of online bipartite matching algorithms, which are essential for scenarios involving time-sensitive resource allocation, like organ donation and task assignments. The integration of stochastic processes and local computation algorithms has also been highlighted, offering new ways to approach problems with probabilistic elements and adaptive adversaries. Notably, the field is moving towards more practical implementations with algorithms that are not only theoretically sound but also computationally feasible and easy to implement in real-world settings.