【摘要】 针对柔性生产线中因产品产量大且需求不确定性高而导致的传统生产线平衡算法难以适用的问题，提出了一种创新的遗传算法优化方案。该算法旨在实现柔性生产环境下的生产线平衡问题，特别是在调整产品生产比例及存在重复工序的生产场景中。具体而言，对传统遗传算法的交叉与变异操作进行了适应性改进，使其能够有效应对柔性生产线在单节拍中存在工序重复的生产线平衡问题，确保算法在搜索最优解时能够顺利执行。此外，算法中引入了复制操作环节，该操作使算法能够动态调整不同产品的生产数量，从而探索出最优的生产比例的配置，以满足最大化生产效率的需求。实验结果表明，该算法能够显著提升柔性生产线的平衡度，优化资源配置，减少设备空闲时间，为企业面对多变市场需求时提供了有力的决策支持。更多还原

【Abstract】 In response to the challenge of traditional algorithms for production line balance being inadequate for flexible assembly lines with large product output and high demand uncertainty, an innovative genetic algorithm optimization approach is proposed, which aims to achieve balance in flexible production environments, especially in adjusting product production ratios and production scenarios with repetitive processes. More precisely, the optimization approach has adaptively improved crossover and mutation operations of genetic algorithm, which provides an effective response to the problem of production line balance with repeated processes in a single beat of flexible production lines and ensures a smooth search for optimal solutions. Additionally, a replication mechanism is introduced to dynamically adjust the production quantities of different products, thus exploring the optimal production ratio configuration to meet the demand for maximizing production efficiency. The experiment result shows that the proposed algorithm significantly improves balance of flexible production lines, optimizes resource allocations, reduces equipment free time and provides valuable decision support for companies navigating dynamic market demands.更多还原