The Factory Must Grow: Automation in Factorio

TL;DR

This paper models and optimizes logistic transport belt problems in the game Factorio using integer programming and meta-heuristic algorithms, providing a new benchmark for game-based operational research.

Contribution

It introduces a mathematical model for the logistic transport belt problem in Factorio and develops an interface for optimization, benchmarking multiple meta-heuristics.

Findings

Simulated Annealing achieved significant optimization results.

Genetic Programming showed competitive performance.

Evolutionary Reinforcement Learning demonstrated promising solutions.

Abstract

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.