A modern MILP-backed scheduler for batch processes — built so you can sweep parameters in a meeting instead of waiting on SchedulePro for a quarter.
The data shape is the same one you already keep in your simulation models. We don't ask you to re-author your recipe.
Structured input — TSV, JSON, or schema-bound table. Procedures decompose into operations and phases; resources are renewable (equipment, operators) or non-renewable (utilities, materials).
Makespan, throughput, or weighted multi-objective. Sequence-dependent setups, CIP windows, hold times, mass balance on intermediates, utility limits — first-class, not bolted on.
Solve time, lower bound, and optimality gap reported. Infeasibility cores when constraints conflict. Re-run from a saved input file — reproducible, versionable.
Every schedule Batchwise generates is guaranteed conflict-free. If your constraints can't be satisfied, it tells you exactly which one is binding — never an invalid Gantt with overlapping equipment or broken precedence.
SchedulePro is paired with a process simulator and pays for it in iteration speed. Excel is honest about being a spreadsheet. Batchwise is the missing third option: a real solver, surfaced as a tool you can iterate in.
Every solve is reproducible from a saved input file. We report lower bounds and optimality gaps rather than just a feasible schedule. Benchmark methodology is published with each performance claim — if you want to replicate, ask and we'll send the instance spec. Recipe data is encrypted in transit and at rest, never used for model training.
A MILP formulation as the primary engine, with a CP fallback for instances where MILP struggles. Commercial backend (Gurobi-class) under the hood; the formulation, not the backend, is the interesting part.
Optimal within a reported gap. We expose the lower bound, upper bound, and optimality gap on every solve. If you set a time budget that doesn't close the gap, we tell you exactly how far we are.
First-class. Sequence-dependent setups (e.g. CIP that depends on the previous batch's product), utility envelopes, and consumable inventory are part of the base formulation — not an afterthought.
Yes for batch splitting across compatible equipment in the same pool. Blending of intermediates is supported through mass-balance constraints; ask us about your specific case.
We track the standard PSPLib-style benchmarks internally and can share results on request. Most academic instances don't model the real-world constraints (CIP, utilities, mass balance) that make pharma scheduling hard — so we benchmark on industrial instances too.
An API is available for design-partner customers. The same input file you'd paste into the UI works against the API. Reproducibility is a first-class goal.
Try the solver with your own TSV — feasibility, makespan, optimality gap, all reported. If you want to dig in on a specific debottlenecking question, book a call.