Optimizing Automotive Manufacturing with Industrial Generative AI

技术

功能应用 - 制造执行系统 (MES)

适用行业

汽车

用例

添加剂制造
制造过程模拟

挑战

像宝马这样的全球制造商面临着一个困难的优化问题：他们如何安排工人实现生产目标，同时最大限度地减少闲置时间？有多种可能的配置和许多限制。不同的车间有不同的生产率，并且每个车间都有自己独立的一组班次安排。此外，制造商需要防止制造过程中各步骤之间缓冲区的溢出和短缺。

关于客户

宝马集团是一家全球汽车制造商，面临着优化制造工厂调度的挑战，以实现生产目标，同时最大限度地减少闲置时间。

解决方案

作为麻省理工学院量子工程中心 (CQE) 成员资格的一部分，萨帕塔和宝马集团合作，将生成式人工智能技术应用于宝马的工厂调度优化问题。具体来说，他们根据现有最先进的求解器生成的最佳解决方案训练了一个受量子启发的生成模型。然后生成模型生成新的、以前未考虑的解决方案。这种方法称为生成器增强优化 (GEO)。

运营影响

The application of the Generator-Enhanced Optimization (GEO) approach led to significant improvements in BMW's plant scheduling optimization. By generating new, previously unconsidered solutions, GEO was able to provide a wider range of potential configurations for BMW to consider. This not only improved the efficiency of the scheduling process but also allowed for greater flexibility in managing production rates and shift schedules. Furthermore, the use of Zapata's computational workflow platform, Orquestra®, enabled a comprehensive benchmarking process, allowing BMW to identify the best algorithm for each problem configuration. This has resulted in a more robust and effective optimization process.

数量效益

GEO tied or outperformed other state-of-the-art solvers in 71% of problem configurations.
GEO outperformed all other solvers in the configuration with the largest solution space.
Approximately one million optimization runs were conducted to benchmark performance.