To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribution of operational data -- so-called distributional shifts. Distributional shifts can harm the performance of predictive models when deployed to future customer orders with new specifications. The literature provides limited advice on how such distributional shifts can be addressed in operations management. Here, we propose a data-driven approach based on adversarial learning and job shop scheduling, which allows us to account for distributional shifts in manufacturing settings with high degrees of product customization. We empirically validate our proposed approach using real-world data from a job shop production that supplies large metal components to an oil platform construction yard. Across an extensive series of numerical experiments, we find that our adversarial learning approach outperforms common baselines. Overall, this paper shows how production managers can improve their decision-making under distributional shifts.

翻译：为达成订单履行目标，制造商致力于优化生产调度。机器学习可通过根据订单规格预测生产线加工周期来支持这一目标。然而，当制造商生产定制化产品时，这一过程面临挑战——因为定制化常导致运营数据概率分布的变化，即所谓的分布偏移。当预测模型被部署到具有新规格的未来客户订单时，分布偏移会损害模型性能。现有文献对如何在运营管理中应对此类分布偏移提供的指导有限。本文提出了一种基于对抗学习与作业车间调度的数据驱动方法，使我们能够在高度产品定制化的制造场景中处理分布偏移。我们利用某为石油平台建造场供应大型金属构件的作业车间生产的真实数据，对所提方法进行了实证验证。通过一系列广泛的数值实验，我们发现对抗学习方法优于常见基准模型。总体而言，本文展示了生产管理者如何能在分布偏移环境下改进决策制定。