The random seat generator that respects your constraints

Why "random" is more complex than it looks

A pure-random shuffle violates constraints half the time. A constrained shuffle that still preserves randomness requires retry logic — try a random arrangement, check constraints, retry if violated, up to 50 attempts before falling back to best-effort. That's what this generator does under the hood. The output looks random because it is — within the rules you've set.

• True randomness within constraint satisfaction

• Separation pairs (don't seat adjacent)

• Keep-together pairs (seat adjacent)

• Fallback to best-effort if constraints over-constrain

Use cases for random seating

First week of school, before you know the class dynamics. Test days, for integrity. Group-work variety, to prevent cliques. Substitute teachers inheriting a class with no existing chart. Homeroom rearrangements after break. Each use case has different constraint needs — the generator handles them all through one interface.

What the constraints protect

IEP accommodations: a student with low vision needs the front row. A student with ADHD benefits from a low-distraction corner. ELL students paired with bilingual peers. SEL-support pairs kept adjacent through transitions. Testing integrity: students with similar academic profiles separated. The random shuffle honors all of these; that's the difference between a real seating tool and a name-picker wheel.

When to re-shuffle vs. when to accept

If the first shuffle looks reasonable and respects constraints, use it. Randomness doesn't reward iteration past a point — seven re-shuffles won't produce a meaningfully "better" random arrangement. The exception: if the shuffle clusters your most disruptive students in one corner, re-shuffle until spatial distribution looks more even. The tool's randomness is genuine, but human judgment on the final output is still the deciding filter.