This is just one example of an SNC problem. Depending on the complexity of the final product, there might be many different queues and resources.These intermediate products are then moved to another place (another queue) ready to be processed further or stored until they are needed.This raw material is then processed by some operator and/or machine (also a resource) to produce some intermediate products.The raw material arrives at the factory and is stored ready to be processed (the place where it is stored can be modeled as a queue).The factory orders raw material from its supplier (which is also a resource).

The demand arrives as orders from customers.We can model the production line as a network of processes: These problems arise in many real-world scenarios and systems. SNC tackles optimal sequential decision-making over a class of networks that consist of queues (sometimes called buffers in the literature) and resources that can drain the queues, moving their content to other queues or out of the network entirely. In this post, we’re going to explain what SNC is, and describe our work in this field to help businesses optimize their decision-making processes. We also incorporate stochastic optimal control theory to find the optimal policy. These techniques use probabilistic modeling to estimate the network and its environment. Stochastic Network Control (SNC) is one way of approaching a particular class of decision-making problems by using model-based reinforcement learning techniques. The networks most businesses operate in are highly complex, and both large and small disruptions can destroy the best-made plans. In reality, finding the right trade-off is an extremely difficult undertaking. How can you make the best business decisions to optimize the balance between cost and quality? Most organizations apply a trade-off between their cost and service levels, where they try to find the sweet spot to maximize both their returns and their customers’ experience.

Inetwork control