Name the limits. Let the tool pick the numbers.

Point the optimizer at the cells it may change, the cell you want as big or as small as possible, and the rules it must not break. It searches, and writes back the split that wins.

Method guide for Google Sheets Three solver engines free, up to 2,000 decision cells

Optimization in Google Sheets, three engines

Simplex LP is for models made of straight lines: budgets, blends, routes, rosters. It is exact and it is fast, and it handles integer and binary decision variables, so “how many trucks” and “which sites do we open” are both fair questions.

Nonlinear takes over when the curve bends, which is what happens the moment a channel has diminishing returns or a price moves the volume it sells. Evolutionary is the one for models full of IF and LOOKUP, where the sheet is not smooth enough for either of the others to get a grip.

The report names the constraints that are actually binding your answer, which is usually the more useful half. Knowing the plan is capped by one warehouse rather than by the budget is what changes next quarter.

Which tool, and what it is for

One panel, one Solving method dropdown. If a model is not linear, Simplex says so rather than guessing, and you switch.

The three solving methods

  • Simplex LP straight-line models. Exact, fast, and takes integer and binary variables.
  • Nonlinear smooth curves: diminishing returns, price and volume, compounding.
  • Evolutionary rough or non-smooth models, the ones built from IF and LOOKUP.

What you can ask for

  • Maximize or minimize a cell profit, cost, miles, headcount, waste.
  • Hit a target value on the Simplex and Nonlinear engines.
  • Constraints, in your own cells floors, ceilings, totals that must balance, integers, binaries.

When the inputs themselves are uncertain

The answer, and what is holding it back

A number on its own does not survive the meeting. The report gives the status, the objective it reached, the value of every decision cell, and which constraints are binding, so you can argue with the limit instead of with the arithmetic.

It writes into a tab of your own spreadsheet, next to the model it solved. Nothing is exported, and nothing has to be re-imported.

Free or Pro

All three methods are free on every plan, and their size limit is the method, not the license. Simplex LP takes up to 2,000 decision cells and 600 constraints, Evolutionary up to 100 cells, Nonlinear up to 60. Ask Simplex LP for whole numbers or yes/no decisions and it proves the optimum for up to 500 of them; Evolutionary takes 30 and cannot prove one. The panel counts the cells you list under Cells Sortia may change and tells you the number before you run. There is no run counter, and Pro raises none of these: we charge for the Pro tools, never for the size of your model.

Those runs happen in your browser by default. A model the browser cannot read falls back to solving through the sheet itself, recalculating your workbook at every step, and that path is slower and its limits are lower: 200 decision cells on Simplex LP, 15 on Evolutionary, 10 on Nonlinear. The panel names the reason it fell back before it writes anything.

The Pro cousin is optimization under uncertainty, where every candidate is simulated rather than assumed. Every install gets five full-quality runs, shared across all five Pro engines rather than five for each. Pro is $199/year.

Worked examples to start from

Each one opens with the decision cells, the objective and the constraints already set, so you can press Run and then change one rule to see what moves.

The other 39 models in the library that use one of these:

Try it in your own sheet

  1. Open Sortia in Google Sheets and choose Start from a template.
  2. Pick one of the models above, and it loads with the inputs filled in.
  3. Change the assumptions to fit your situation and press Run.

Never used Google Sheets? Start here goes the whole way, in seven steps, and assumes nothing.

Other methods: Monte Carlo  Decision trees  Schedule risk  Critical chain  Optimization under uncertainty  Statistics  Machine learning  Forecasting  What-if analysis