Optimization of Container Relocation Problem via Reinforcement Learning

Abstract

This paper presents an optimization method of Container Relocation Problem (CRP) via Reinforcement Learning (RL) based on heuristic rules. The method to calculate theoretical lowest relocation rate is also briefly explained. As the result, training models for different dimensional bays are provided. Compared to the theoretical value, the result relocation rate is acceptable with high inference speed. Furthermore, extended CRP in block will be briefly demonstrated.