2D nesting problems rank among the most challenging cutting and packing problems. Yet, despite their practical relevance, research over the past decade has seen remarkably little progress. One reasonable explanation could be that nesting problems are already solved to near optimality, leaving little room for improvement. However, as our paper demonstrates, we are not at the limit after all. This paper presents $\texttt{sparrow}$, an open-source heuristic approach to solving 2D irregular strip packing problems, along with ten new real-world instances for benchmarking. Our approach decomposes the optimization problem into a sequence of feasibility problems, where collisions between items are gradually resolved. $\texttt{sparrow}$ consistently outperforms the state of the art - in some cases by an unexpectedly wide margin. We are therefore convinced that the aforementioned stagnation is better explained by both a high barrier to entry and a widespread lack of reproducibility. By releasing $\texttt{sparrow}$'s source code, we directly address both issues. At the same time, we are confident there remains significant room for further algorithmic improvement. The ultimate aim of this paper is not only to take a single step forward, but to reboot the research culture in the domain and enable continued, reproducible progress.

翻译：二维排样问题属于最具挑战性的切割与装箱问题之一。然而，尽管其具有重要的实际意义，过去十年的研究进展却异常缓慢。一个合理的解释可能是排样问题已接近最优解，改进空间有限。但正如本文所证明的，我们远未达到极限。本文提出 $\texttt{sparrow}$——一种用于解决二维不规则条带装箱问题的开源启发式方法，并提供了十个新的真实场景算例用于基准测试。我们的方法将优化问题分解为一系列可行性问题，逐步消除零件间的干涉冲突。$\texttt{sparrow}$ 始终优于现有最优方法，在某些案例中优势幅度远超预期。因此我们确信，前述研究停滞现象更合理的解释在于领域的高准入门槛和普遍存在的可复现性缺失。通过公开 $\texttt{sparrow}$ 的源代码，我们直接解决了这两个问题。同时，我们确信算法层面仍存在显著的改进空间。本文的最终目标不仅是推动领域向前一步，更是要重启该领域的研究范式，实现可持续、可复现的进展。