WareMax vs. a custom SimPy stack
SimPy is a process-based DES library in Python; rolling your own RMFS simulator on top is a fine first step. WareMax becomes the right choice when you need speed, reproducibility-as-a-property, and an RL interface that does not break under a worker thread.
Fair-rows comparison
Each row below is a question we have actually been asked about WareMax in the same breath as A custom SimPy stack. We try to answer both columns accurately; corrections are welcome via the GitHub issue tracker.
| dimension | WareMax | A custom SimPy stack |
|---|---|---|
| What it gives you | A complete RMFS DES with policies, attribution, RL env, and baselines. | A general DES kernel; you write the warehouse model. |
| Implementation language | Rust core (waremax-core) + Python bindings via PyO3. | Pure Python with generator-based coroutines. |
| Performance | Trains thousands of episodes on a laptop CPU; Rust + mimalloc. | Interpreted Python; orders of magnitude slower per simulated second. |
| Determinism | Tested property end-to-end. ChaCha8 RNG, id-based tie-breaking everywhere, crossbeam ping-pong handshake between agent and sim. | Depends entirely on your implementation. Python dict-iteration is insertion-ordered (3.7+) but tie-breaking and external RNG usage are your responsibility. |
| RL interface | Gymnasium env WaremaxAllocEnv, MaskablePPO-ready, candidate-scoring policy supplied. SMDP framing. | You write it. Wrapping SimPy in a Gymnasium env with action masking and worker-thread safety is non-trivial. |
| Heuristic baselines | Five shipped, selectable by name in the YAML. | You implement them. |
| Delay attribution | Five-bucket per-task decomposition that sums to cycle time. Used as reward signal in attribution / routed modes. | You design the schema and implement the bookkeeping. |
| Time to a useful experiment | One YAML file and one CLI invocation. | Days to weeks for a first credible RMFS model. |
| When SimPy is the right call | — | Small or unusual systems where the question is well-served by a one-off model; teaching DES; quick prototypes. |
| When WareMax is the right call | RMFS dispatching studies, RL experiments, multi-seed reproducible benchmarks, fleet sizing. | — |
| Extensibility | Implement TaskAllocationPolicy (one-method Rust trait) + a one-arm change in policy_factory.rs. Same pattern for station assignment, batching, priority, traffic. | Edit your Python code; no contract to honor. |
Start with SimPy for one-off RMFS prototypes or when you want a teaching exercise. Move to WareMax when you need speed, an RL benchmark, or a reproducibility guarantee that survives your own carelessness.
Spot a mistake about A custom SimPy stack on this page? Open an issue at github.com/Skelf-Research/waremax/issues and we will fix it.