Configure the Randomization
Paste run data as one column or as comma, tab, semicolon, or pipe separated rows.
Example Data Table
This sample shows a practical input structure for lab runs, test batches, or trial schedules.
| Run ID | Treatment | Block | Notes |
|---|---|---|---|
| Run 01 | A | Block 1 | High temperature |
| Run 02 | B | Block 1 | Low temperature |
| Run 03 | C | Block 1 | Standard mix |
| Run 04 | A | Block 2 | Repeat batch |
| Run 05 | B | Block 2 | Fresh sample |
| Run 06 | C | Block 2 | Control condition |
Formula Used
This calculator uses a seeded permutation process based on the Fisher-Yates shuffle. For
n total runs, every valid complete ordering has probability
1 / n! under pure random shuffling.
When anti-adjacency is enabled, the tool tests multiple candidate schedules and selects the one with the lowest treatment adjacency score:
A = Σ I(Tᵢ = Tᵢ₋₁)
Here, I() equals 1 when two neighboring runs share the same treatment,
otherwise 0. Lower values reduce local clustering and improve balance.
Ties are broken by maximizing total displacement from the original run positions:
D = Σ |Rᵢ - Oᵢ|
Rᵢ is the randomized position and
Oᵢ is the original position. This helps avoid weak reshuffles.
How to Use This Calculator
- Paste or type your run list into the input box. Use one row per run.
- Optionally include Treatment, Block, and Notes columns after the run ID.
- Set replicates if each original run must appear multiple times.
- Choose whether runs should randomize globally or inside blocks.
- Enter a seed when you want a reproducible schedule.
- Increase optimization iterations for better anti-adjacency balancing.
- Click Randomize Run Order to generate the schedule above the form.
- Use the CSV or PDF buttons to export the randomized plan.
FAQs
1. What does this calculator randomize?
It randomizes the execution order of experimental, laboratory, survey, or production runs. The goal is to reduce time-related bias, operator effects, and hidden sequence patterns.
2. Why would I use a seed?
A seed makes the shuffle reproducible. If you rerun the same input with the same settings and seed, you get the same schedule again.
3. What is anti-adjacency balancing?
It reduces consecutive runs with identical treatments. That can improve fairness when neighboring runs may share environmental drift, warm-up effects, or machine carryover.
4. When should I randomize within blocks only?
Use within-block randomization when blocks represent separate days, operators, chambers, or batches that must remain grouped while still needing internal random order.
5. Can this handle repeated runs?
Yes. Increase the replicate count to duplicate every original run. Each duplicate receives its own randomized position and replicate number.
6. What input format works best?
Comma-separated rows are easiest, but tabs, semicolons, and pipes also work. You may enter only run IDs or add treatment, block, and notes columns.
7. Does a lower adjacency score always mean a better design?
Not always. Lower adjacency helps balance treatment order, but practical constraints, blocking rules, setup costs, and regulatory requirements may still matter.
8. What does the Plotly chart show?
The chart compares each run’s original position with its randomized position. It helps you inspect reshuffling depth and visually confirm that the schedule changed meaningfully.