Automated Design of Heuristics for the Container Relocation Problem

TL;DR

This paper introduces a genetic programming approach to automatically design relocation rules for the container relocation problem, outperforming manually created rules and demonstrating good generalization across various problem instances.

Contribution

The paper presents a novel application of genetic programming to automatically generate effective heuristics for the container relocation problem, reducing reliance on manual rule design.

Findings

GP-evolved rules outperform existing manual rules

Rules generalize well to unseen problems

Performance of rules can be further improved

Abstract

The container relocation problem is a challenging combinatorial optimisation problem tasked with finding a sequence of container relocations required to retrieve all containers by a given order. Due to the complexity of this problem, heuristic methods are often applied to obtain acceptable solutions in a small amount of time. These include relocation rules (RRs) that determine the relocation moves that need to be performed to efficiently retrieve the next container based on certain yard properties. Such rules are often designed manually by domain experts, which is a time-consuming and challenging task. This paper investigates the application of genetic programming (GP) to design effective RRs automatically. The experimental results show that GP evolved RRs outperform several existing manually designed RRs. Additional analyses of the proposed approach demonstrate that the evolved rules generalise well across a wide range of unseen problems and that their performance can be further enhanced. Therefore, the proposed method presents a viable alternative to existing manually designed RRs and opens a new research direction in the area of container relocation problems.