<p>The container relocation problem is a critical combinatorial optimisation problem in warehouses and container ports. The goal is to retrieve all containers while minimising unnecessary relocations. As this problem is NP-hard, various heuristics have been proposed, including relocation rules (RRs), simple constructive heuristics that iteratively build solutions by determining how containers should be relocated within the yard for efficient retrieval. However, manually designing effective RRs is challenging, leading to the use of genetic programming to generate them automatically. A key limitation of both manually and automatically designed RRs is their restricted problem view and limited decision-making scope. This often results in suboptimal relocations, negatively impacting future operations and overall efficiency. A crucial aspect of RR design is defining effective relocation schemes that enhance decision-making by considering the long-term impact of relocations. This study investigates several relocation schemes that provide RRs with lookahead capabilities, enabling them to anticipate future consequences and make more informed moves. In addition to two standard schemes, four novel relocation schemes are introduced and evaluated using an established problem set. The results demonstrate that properly adapting relocation schemes can significantly enhance the performance of automatically designed RRs, leading to significantly better results.</p>