在定制化被动式木窗加工过程中，减少边框材下料过程中的原料浪费是降低成本的关键。为此，将该问题建模为一维下料问题，针对传统遗传算法中个体编码方式在迭代过程中容易导致切割模式被破坏和探索效率低下的问题，提出一种新的个体编码方式，以保护进化过程中切割模式的完整性。同时，设计启发式策略和修正策略，用于个体修正和种群进化。仿真试验表明，在不同算例下，除末根外的原料平均利用率均可达到99%，且末根余料长度相较其他算法也有所提高。在2组企业的实际生产数据中，与企业现有软件相比，该算法不仅达到了理论下界，还在除末根外的平均利用率上分别达到99.49%和99.66%，优于企业软件的计算结果。该算法有助于降低成本，能为工程实践提供可靠的解决方案。

In the customization process of passive wooden window manufacturing， reducing material waste during frame cutting is key to cost reduction. This problem is modeled as a one-dimensional cutting stock problem. To address the issue of traditional genetic algorithms where the individual encoding method tends to lead to the destruction of cutting patterns and low exploration efficiency during iterations， a new individual encoding method is proposed to maintain the integrity of cutting patterns throughout the evolutionary process. Additionally， a heuristic strategy and a correction strategy are introduced for individual correction and population evolution. Simulation results show that for different test cases， the average material utilization rate， excluding the last remnants， exceeds 99%， with some improvements in the length of the last remnants compared to other algorithms. For two sets of real production data from enterprises， the proposed algorithm achieves the theoretical lower bound， with average utilization rates （excluding the last remnants） of 99.49% and 99.66%， respectively， outperforming the results of the company's existing software. This demonstrates the algorithm's potential to effectively reduce costs and provide practical solutions in engineering applications.