Dynamic dispatching rules that allocate resources to tasks in real-time play a critical role in ensuring efficient operations of many automated material handling systems across industries. Traditionally, the dispatching rules deployed are typically the result of manually crafted heuristics based on domain experts' knowledge. Generating these rules is time-consuming and often sub-optimal. As enterprises increasingly accumulate vast amounts of operational data, there is significant potential to leverage this big data to enhance the performance of automated systems. One promising approach is to use Decision Transformers, which can be trained on existing enterprise data to learn better dynamic dispatching rules for improving system throughput. In this work, we study the application of Decision Transformers as dynamic dispatching policies within an actual multi-agent material handling system and identify scenarios where enterprises can effectively leverage Decision Transformers on existing big data to gain business value. Our empirical results demonstrate that Decision Transformers can improve the material handling system's throughput by a considerable amount when the heuristic originally used in the enterprise data exhibits moderate performance and involves no randomness. When the original heuristic has strong performance, Decision Transformers can still improve the throughput but with a smaller improvement margin. However, when the original heuristics contain an element of randomness or when the performance of the dataset is below a certain threshold, Decision Transformers fail to outperform the original heuristic. These results highlight both the potential and limitations of Decision Transformers as dispatching policies for automated industrial material handling systems.

翻译：动态调度规则通过实时分配资源给任务，在确保各行业自动化物料搬运系统高效运行方面发挥着关键作用。传统上，部署的调度规则通常是基于领域专家知识手工设计的启发式方法的结果。生成这些规则耗时且往往不是最优的。随着企业日益积累大量运营数据，利用这些大数据提升自动化系统性能具有巨大潜力。一种有前景的方法是使用决策Transformer，它可以在现有企业数据上进行训练，以学习更好的动态调度规则，从而提高系统吞吐量。在本研究中，我们探讨了决策Transformer作为动态调度策略在实际多智能体物料搬运系统中的应用，并识别了企业能够有效利用现有大数据上的决策Transformer来获取商业价值的场景。我们的实证结果表明，当企业数据中最初使用的启发式方法表现中等且不涉及随机性时，决策Transformer可以显著提高物料搬运系统的吞吐量。当原始启发式方法性能很强时，决策Transformer仍能提高吞吐量，但改进幅度较小。然而，当原始启发式方法包含随机性元素，或者数据集的性能低于某个阈值时，决策Transformer无法超越原始启发式方法。这些结果凸显了决策Transformer作为自动化工业物料搬运系统调度策略的潜力和局限性。