»Boosting simulation performance with Python«
2019-06-04, 11:00–11:25, Hall 3

In this talk I will present the architecture of our simulation infrastructure written in Python which allows to simulate hours of real-life in only minutes of simulation. I will describe challenges and issues we encountered and how we handled them.

Our company's product uses a fleet of real (not virtual) robots to perform different tasks in a fulfillment warehouse. The importance of simulations is significant: it allows to test our solution, new features and perform regression tests without the need for real and expansive hardware, measure and analyze the impact of different algorithms and optimizations, and explore possible solutions before deploying them in production. Tasks performed by physical robots take time (movement over the warehouse, box lifting, etc.), but in simulation, where virtual robots are used, there is no need to wait all that time. Shortening simulation time improves the development process by providing faster feedback to developers and quicker CI and testing cycles. Another benefit is a more deterministic simulation - using this approach, each component (thread) in the system gets equal opportunity (CPU time) in each time tick, which is not affected by the underlying machine or operating system that the simulation is running on. Also, it is possible to simulate any hour of the day easily, and by that we wouldn't panic before the "Y2K bug". I will also show how we expanded SimPy to adjust this architecture when making the transition to microservices.