Improving neural-based heuristics for 2D strip packing through local search in heuristic space

This paper presents a novel approach to the two-dimensional orthogonal rectangular strip packing problem (2D-SPP), focusing on arranging rectangular items on a strip of fixed width to minimize the total height. We extend the recently proposed novel neural-based constructive heuristic by integrating a local search strategy that explores the space of heuristics, encoded as neural network weights, rather than the solution space. Utilizing the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for both training and problem-specific refinement, our method dynamically learns and optimizes placement decisions. We introduce benchmarking datasets inspired by real-world fast-moving consumer goods (FMCG) logistics, demonstrating the approach’s efficacy. Experimental results show that our refined constructive heuristic (RCH) significantly outperforms traditional heuristics like MaxRects and Skyline, and achieves up to tenfold reductions in wasted space compared to constraint programming for larger, heterogeneous instances. The proposed method offers scalability and adaptability, making it particularly suited for practical applications in logistics and manufacturing.