Also reported as Factories Assessment · Factory Optimization Game · Factory Assembly Line Optimization
A Roblox OA mini-game (25 minutes) in which you manage multiple factory production lines and must maximize total profit. You are given several production lines, each with adjustable parameters such as output volume and machine efficiency. You can run multiple experiments (configurations) within the time limit, and your best (most profitable) run is recorded as your score. The goal is to find the combination of settings that yields the highest total factory revenue/profit. No traditional coding is required — the task is presented as an interactive simulation or game (compared by candidates to games like Dyson Sphere Program / Factorio).
Write a 250–1000 word essay describing your strategy for maximizing profit and what optimizations you would pursue if given more time.
Common mistakes: Not fully understanding the factory task instructions and just experimenting randomly, resulting in low profit scores (e.g., around 45,000).; Not preparing for the essay: one report mentions a 25-minute, 250-1000 word write-up of your strategy and possible optimizations after the factory task, so keep notes on what you changed and why while you play.; Reddit commentary (cited by candidates) suggested Roblox weighs the game results heavily — low scores may disqualify candidates regardless of other sections.
Interviewer hints: Multiple experiment runs are allowed and only the most profitable run counts. Reports disagree on the clock: one gives 25 minutes for the factory game itself, another describes a 45-minute block that ends with a 25-minute essay.; After the interactive factory simulation, there is a mandatory 25-minute essay section (250–1000 words) asking candidates to describe their strategy and potential future optimizations.
What passers do: Candidates who fared better in the factory task focused on adjusting variables with fewer dependencies first (top and bottom of the line), made incremental changes rather than large ones, and actively used productivity boosters.; Familiarity with factory/production games like Dyson Sphere Program or Factorio helped candidates understand input/output ratios and get up to speed quickly.; Candidates took advantage of the multiple-experiment mechanic (best run is recorded) by iterating many configurations within the time limit.
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