Methods, systems and apparatuses to enable an optimum bin selection by implementing a neural network with a network scheduling and configuration tool (NST), the method includes: configuring an agent with a critic function from neural networks wherein the agent neural network represents each bin of the collection of bins in the network that performs an action, and a critic function evaluates a criteria of success for performing the action; processing, by a scheduling algorithm, the VLs by the NST; determining one or more reward functions using global quality measurements based on criteria comprising: a lack of available bins, a lack of available VLs, and successfully scheduling operations of a VL into a bin; and training the network based on a normalized state model of the scheduled network by using input data sets to arrive at an optimum bin selection.