Beyond Silicon

The sim-restaurant example shows that GWR is not limited to silicon or packet models. It uses the same engine to explore a restaurant as a system of queues, workers, time delays, and business outcomes.

Objective

To see how the GWR core can be used to model many types of systems beyond silicon. This example shows how to run a staffing sweep to identify what combination results in the most profitable restaurant.

Run The Simulation

cargo run --bin sim-restaurant -- --min-till-staff 1 --max-till-staff 2 --min-kitchen-staff 1 --max-kitchen-staff 4 --top-results 4

The above command should produce output that looks like:

Restaurant demand plan: 2642 customers from 07:00 to 22:00 (15.0 hours, seed 7).

Till Kitchen  Served   Balked   GaveUp    Revenue      Costs    Profit  Finish h  Max Queue
   1       4    1040     1375      225   13122.40    5251.01   7871.39     15.44   13/24
   2       4    1029     1357      254   13136.50    5495.15   7641.35     15.44   13/24
   1       3     773     1475      391    9880.10    4006.90   5873.20     15.52   12/24
   2       3     781     1452      405    9886.20    4252.65   5633.55     15.55   12/24

Explore Using a TUI

Take a look at which staffing mix maximises profit, where queues build up, and whether the till or kitchen is the real bottleneck. There is a command-line TUI that allows you to inspect the details of what happens during a simulation of a given staffing configuration:

cargo run --bin sim-restaurant-tui -- --till-staff 2 --kitchen-staff 5

Mapping Restaurant Ideas To GWR

This model uses the same building blocks as a silicon simulation:

• Customers are modelled as async tasks that trigger time-based events.
• Till workers and kitchen workers are concurrent async tasks.
• Queues are explicit shared state.
• Service completion is modeled through events.
• Profitability is derived from measurable end-of-run metrics.

Explore The Problem Space

Change one pressure point and rerun:

• Increase --max-till-staff to see whether the till is the bottleneck.
• Increase --max-kitchen-staff to see whether food preparation is the bottleneck.
• Reduce --join-base-probability or raise --join-queue-sensitivity to model less patient customers.