Logistics Process Simulation & Digital Twinning

Develop a Python class to serve as a digital twin for port logistics entities (vehicles, cargo), serializable to JSON.

Model a key logistics workflow (e.g., container gate-in) as a BPMN 2.0 diagram using the Camunda Modeler.

Implement a discrete-event simulation in Python using SimPy to model port dynamics like vehicle arrivals and gate queuing.

Write a Python external task worker to connect to a Camunda engine and execute simulated tasks (e.g., API call to a mock Navis N4).

Integrate the SimPy simulation with the Camunda workflow engine to drive process instances from simulated events.

Define and calculate baseline performance metrics (e.g., cycle time, resource utilization) from the simulation outputs.

Set up and configure a network emulator (Toxiproxy) to programmatically inject latency into the mock Navis N4 API endpoint.