Why use this?

SimPy models the behavior of active components such as vehicles, customers, machines, or messages as Python generator-based processes. These processes interact through events and shared resources, allowing engineers to represent limited-capacity congestion points such as servers, machines, checkout counters, and tunnels.

Industrial engineers choose SimPy when they need a lightweight, code-first DES framework for manufacturing lines, warehouses, service systems, logistics networks, and process improvement studies. Simulations can run as fast as possible, in real time, or event-by-event, and SimPy integrates naturally with pandas, NumPy, optimization tools, and Jupyter-based analysis workflows. [386][389][391]

Key Features

• Process-based discrete-event simulation using Python generators
• Shared resources for capacity, queues, and congestion modeling
• As-fast-as-possible, real-time, and manual event stepping
• Suitable for manufacturing, logistics, service, and agent-based systems
• MIT-licensed and easy to integrate with Python data workflows