When simulation models become the basis of decision making in operational settings, they often face time constraints. Machine scheduling in factories is one situation where equipment needs to decide quickly how to respond to its environment. 

Traditional simulation optimizers work well in static environments, but aren’t equipped to handle the unexpected shifts of real-world operations. When conditions shift and data varies, traditional optimizers need to be recalculated, which can take minutes or hours. Sometimes they need to be rewritten, which can take weeks. 

Given those constraints, traditional solvers may not be able to deliver a steady flow of optimal decisions. That leads to waste and suboptimal business outcomes. 

The resource shortages and demand shifts caused by pandemics, lockdowns and other disruptions in 2020 have made these limitations even more acute.  

Pathmind reinforcement learning offers a faster, simpler tool for tackling changes in operations. The Pathmind platform generates decision-making policies better suited for handling new data without major rewrites or recalculations. Situations that require decisions in under a second, or less, can now be optimized, and decision-making agents deployed. That lets you focus on what’s important: solving hard problems with simulations.

Equipment on a factory floor

Pathmind’s work with Princeton Consultants is an example of how we help businesses respond quickly to change. A manufacturing client struggled to schedule machines efficiently because its optimizer was unable to respond effectively when new types of items needed to be processed. This production issue led to delays and reduced output. 

Pathmind offered an adaptive solution able to be modified in under an hour. The new AI solution was also able to be retrained in significantly less time than it took the manufacturing team to adjust code and undertake rewrites with the optimizer. 

Read the full case study here.

Whether it’s minor changes unique to your industry or massive supply chain shifts, being able to respond quickly can be the difference between a mission-critical simulation model, or an interesting thought experiment. Pathmind reinforcement learning enables you to prepare for the unexpected and offers decision-making systems that respond accurately when new challenges arise. 

Contact us to learn more.