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WareMax vs. AnyLogic

AnyLogic is a general-purpose, multi-paradigm commercial simulation product; WareMax is a single-purpose open-source benchmark. They overlap on "can simulate a warehouse" and almost nothing else.

License: AnyLogic: commercial. WareMax: MIT.Paradigms: AnyLogic: DES + agent-based + system dynamics. WareMax: DES only.Scope: AnyLogic: general (logistics, manufacturing, health, supply chain, traffic). WareMax: RMFS dispatching only.RL interface: AnyLogic: via Pypeline / cloud RL service. WareMax: Gymnasium env, native PyO3 bindings.

Fair-rows comparison

Each row below is a question we have actually been asked about WareMax in the same breath as AnyLogic. We try to answer both columns accurately; corrections are welcome via the GitHub issue tracker.

dimension WareMax AnyLogic
Primary scope Robotic Mobile Fulfillment Systems only (pod-to-person AMR dispatching). General-purpose multi-paradigm simulator across many verticals.
License & cost model MIT, source-available, no per-seat cost. Commercial license; pricing not discussed here.
Simulation paradigm Pure discrete-event, event-queue kernel. Discrete-event, agent-based, and system-dynamics — mixable.
Determinism guarantee Tested property: same seed + same action sequence ⇒ byte-identical trajectory. ChaCha8 RNG, canonical id-based tie-breaking, tests in waremax-rl/tests/determinism.rs. Reproducibility supported via seeded RNG; canonicalization of all tie-breaking is the user's responsibility.
Core implementation Rust, single-threaded per scenario, mimalloc allocator. Java-based simulation engine with a graphical IDE.
Scenario format YAML or JSON parsed by waremax-config; schema-validated. Visual modeling in the IDE; exportable.
RL interface Gymnasium env (WaremaxAllocEnv) via PyO3. Action mask + dict observation. SMDP framing. Reinforcement learning via the AnyLogic Cloud RL service or external bridges; not native Gymnasium.
Heuristic baselines nearest_robot, least_busy, round_robin, auction, workload-balanced shipped in-box. Selectable by policy name. User-implemented; the platform does not ship dispatching policies out of the box.
Delay attribution Per-task decomposition: assignment / travel / queue / congestion / service; sums to cycle time. Used as a reward signal. Custom statistics collection per model; not a built-in canonical decomposition.
A/B testing & sweeps CLI: waremax compare, waremax sweep, waremax ab-test (Welch's t), waremax benchmark (regression detection). Parameter variation experiments built into the IDE.
GUI / visualization None at runtime; report generation (HTML / PDF) via waremax-metrics. Full graphical IDE, 2D and 3D animation, dashboards.
What's out of scope AS/RS cranes, conveyors, AGV tuggers, human pickers walking aisles, CAD/DWG import. Nothing structurally out of scope; the platform is general-purpose.
Best fit RMFS dispatching studies, multi-seed reproducible RL experiments, fleet-sizing for pod-to-person warehouses. Mixed-paradigm enterprise modeling; cross-vertical consulting work; teams that prefer visual modeling.
Verdict

Pick AnyLogic when you need a multi-paradigm general-purpose modeler and an interactive IDE. Pick WareMax when you specifically need an open, deterministic RMFS dispatching benchmark with a native Gymnasium environment.

Spot a mistake about AnyLogic on this page? Open an issue at github.com/Skelf-Research/waremax/issues and we will fix it.

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