  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.
