Role of an Optimal Multiagent Scheduling in Different Applications Using ML

Scheduling is regarded as one of the vital decision-making processes used frequently in many real-time cases. It manages everything from resource allocation to the task completion, with the goal to optimize the desired objectives. Subject to the problem, the resources, tasks, and goals can differ. The aim is to design a corporative multiagent system for optimal scheduling. Many of the scheduling available algorithms calculate optimality based on different perspectives. The proposal is to create the dataset using multiple algorithms with different performance metrics to find an optimal one. This data can be imported into machine learning tools for training and predicting, based on the selected performance metrics. The algorithm considered in the empirical analysis includes first come first serve, Round robin, and Ant colony approach. The major finding shows that scheduling using Ant colony is an optimal algorithm, which is based on speed and velocity. The future extension would be to check the correctness of optimality using machine learning tools