simpy

What problem does it solve?

Modeling and optimizing complex systems with interacting entities, shared resources, and time-based events (like manufacturing lines or service queues) is challenging without a flexible simulation framework. This Skill leverages SimPy for process-based discrete-event simulations in Python.

Core Features & Use Cases

• Process-Based Modeling: Define system behavior using Python generator functions for entities, resources, and events.
• Resource Management: Simulate contention for limited resources (servers, machines, containers) and analyze their utilization.
• Queue Analysis & Optimization: Study waiting lines, service times, and throughput to identify bottlenecks and improve system efficiency.
• Use Case: If you need to optimize the flow of customers through a service center with limited staff, use this skill to build a SimPy simulation. You can model customer arrivals, service times, and staff capacity to analyze wait times and staff utilization, helping you make data-driven decisions.

Quick Start

Create a basic SimPy simulation with two processes that each wait for 5 time units and print their start and finish times.