simpy

You build a discrete-event simulation in Python by defining system behavior using standard Python generator functions and leveraging an event scheduling mechanism to manage time-based processes. This allows you to model entities, queues, and shared resources over time.

Yes, you can model shared resource management and queues for manufacturing systems by defining shared resources like machines or servers. The framework handles process synchronization and event scheduling to analyze resource utilization and wait times.

Process-based discrete-event simulation is a modeling technique where system behavior is defined using Python generator functions to represent active processes. You need it to analyze complex systems with time-based events, such as logistics, service operations, and manufacturing networks.

No external dependencies are required to run these process-based simulations beyond standard Python. The framework relies on standard Python generator functions and its own internal event scheduling mechanisms to execute the discrete-event modeling.

To simulate a customer service center and analyze wait times, you model customers as processes arriving over time and request shared server resources. The simulation tracks queue lengths and wait times to help optimize staffing levels and understand resource utilization.

Using Python generators for discrete-event modeling limits you to sequential process-based logic handled by the framework's event scheduling mechanisms. It is designed for analyzing manufacturing, logistics, and service operations rather than continuous or agent-based modeling approaches.