# The Factory Must Grow: Automation in Factorio

arXiv: https://arxiv.org/abs/2102.04871 · HTML: https://mlsanity.com/paper/2102.04871/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari](https://mlsanity.com/paper/1802.08842/) (2018)

## Built on by (most cited first, 2 of 2)

- [Factorio Learning Environment](https://mlsanity.com/paper/2503.09617/) (2025)
- [Towards Automatic Design of Factorio Blueprints](https://mlsanity.com/paper/2310.01505/) (2023)

## Abstract (arXiv)

Efficient optimization of resources is paramount to success in many problems faced today. In the field of operational research the efficient scheduling of employees; packing of vans; routing of vehicles; logistics of airlines and transport of materials can be the difference between emission reduction or excess, profits or losses and feasibility or unworkable solutions. The video game Factorio, by Wube Software, has a myriad of problems which are analogous to such real-world problems, and is a useful simulator for developing solutions for these problems. In this paper we define the logistic transport belt problem and define mathematical integer programming model of it. We developed an interface to allow optimizers in any programming language to interact with Factorio, and we provide an initial benchmark of logistic transport belt problems. We present results for Simulated Annealing, quick Genetic Programming and Evolutionary Reinforcement Learning, three different meta-heuristic techniques to optimize this novel problem.