Use Cases for Simulation and AI

Pathmind offers a powerful, robust optimization method for complex simulations. Compared to other optimizers, Pathmind’s AI is better suited to handle the complexities of real-world operations that are key to successful models.

What Makes Pathmind RL Different?

Respond to Variability

Manage Large and Complex Systems

Balance Multiple, Conflicting Objectives


Enhance and Debug Heuristics

Respond to Variability

A reinforcement learning policy can dynamically adjust to variability in the system. For example, a policy can proactively mitigate and respond to equipment breakdowns or unexpected system delays, ensuring that production targets are always met.

Featured Case Study

Mining operation

AI-Based Optimization Cuts Energy Costs by 10%

A global engineering firm needed to minimize energy costs at a metals processor that faced fluctuating electricity prices. This variability in price made production planning a challenge.

Pathmind’s AI learned to pick up on indicators that prices would surge, and recalibrated production in anticipation of price increases, achieving a 10% total savings in energy costs.

Example Models Featuring Variability

Warehouse Putaway & Picking Processes Powered by Reinforcement Learning 

Deep Reinforcement Learning for Optimal Operation and Maintenance of Energy Systems 

Interconnected Call Center Using Reinforcement Learning  

Deep Reinforcement Learning for Order Sequencing  

Manage Large and Complex Systems

Traditional heuristics cannot easily optimize large and complex state spaces: imagine a decision tree with 1,000,000 possible outcomes. The process is painful and slow, if it is even possible. The nature of reinforcement learning makes this task straightforward, especially in coordinating the action of many machines together.

Featured Case Study

Two workers discuss machine scheduling on a factory floor

Princeton Consultants: Schedule Machines Efficiently with AI

A client of Princeton Consultants faced a machine scheduling problem. When new types of items needed to be processed, the existing optimizer could not respond quickly. It always had to be recalculated, and sometimes it had to be rewritten, which could take weeks.

Partnering with Pathmind, Princeton Consultants was able to produce an AI policy that could handle new items efficiently while also increasing the number of items successfully processed.

Example Models Featuring Large and Complex Systems

Product Delivery Powered by Reinforcement Learning 

Automated Guided Vehicle (AGV) Powered by AI 

Balance Several Conflicting Objectives

Compared to heuristics that typically optimize one KPI at a time, a reinforcement learning policy can be trained to optimize multiple, independent KPIs simultaneously, even in complex scenarios. Instead of solely maximizing revenue, a reinforcement policy simultaneously show how to minimize carbon emissions — two seemingly competing objectives.

Featured Case Study

Vehicles in a traffic jam

Accenture: Reducing Carbon Emissions and Maximizing Efficiency


Accenture’s Applied Intelligence team partnered with Pathmind to expand on the AnyLogic product delivery simulation by adding carbon emission monitoring.

With Pathmind AI, the completed model was able to maximize product delivery efficiency whie simultaneously minimizing carbon emissions.

Example Models Featuring Multiple, Conflicting Objectives

Deep RL for Optimal Sequential Decision Making 

AI Crane Warehouse 

Enhance and Debug Heuristics

Deploying a reinforcement learning policy in real-world operational systems might make some stakeholders wonder how they will ensure that those systems remain stable. Instead of jumping to deployment, a reinforcement learning policy can be used to enhance an existing heuristic in a working system.

Pathmind Toolkit

Pathmind allows you to experiment with state-of-the-art tools from the reinforcement learning ecosystem out of box.

  • A simple plugin to add reinforcement learning to existing simulations.
  • Support for single and multiple reinforcement learning agents.
  • Experiment with discrete, continuous, and tuple action spaces.
  • Automatic hyperparameter tuning and algorithm selection.
  • Hands-on assistance with a Pathmind reinforcement learning expert.
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