alterlab-simpy

SimPy simulates discrete-event systems in Python by using generator functions to model processes and built-in primitives like Resource, Container, and Store to manage shared resources. This allows you to analyze complex workflows where entities compete for limited capacity.

Process-based discrete-event simulation models systems where events unfold over time and entities contend for resources. You need it to forecast performance and experiment with design choices in domains like manufacturing, logistics, service operations, and telecommunications.

Yes, you can monitor queue lengths and resource utilization during a simulation using built-in monitoring and data collection utilities. These primitives allow you to observe throughput, track resource usage, and analyze system performance over time.

SimPy supports priority and preemptive resource allocation through PriorityResource and PreemptiveResource classes. These components allow you to model complex service operations where higher-priority tasks can interrupt or bypass lower-priority tasks in the queue.

The best way to model manufacturing lines and logistics networks is using process-based discrete-event simulation. This method allows you to represent workflows, queues, and resource interactions, helping you forecast performance and experiment with operational design choices.

Yes, you need Python generator functions to build event-driven simulations in SimPy. Generators represent activities and interactions, yielding control to the environment to manage event scheduling, synchronization, and inter-process communication.