Cyber-Physical System (CPS) is one of the most promising directions of Industry 4.0 smart manufacturing. Abundant manufacturing data and information are available for decision-makers in real-time thanks to the application of various frontier technologies in CPS. However, the inherent complexity and uncertainty of manufacturing optimization still plague scholars and practitioners and impede further progress of smart manufacturing. The production planning and scheduling is such a complex and stochastic problem that has received considerable research attention. Whereas how to leverage the strengths of CPS for breaking the bottleneck of complexity and uncertainty, is still a question that needs further exploration. This paper proposes a novel “divide and conquer” approach, Spatial–Temporal Out-Of-Order execution (ST-OOO), for achieving real-time planning and scheduling in cyber-physical factories. ST-OOO divides the space and time scopes of a factory into finite areas and intervals to reduce complexity and localize uncertainties so that the original complex optimization problem is decomposed into a set of subproblems with different spatial and temporal characteristics. These small-size subproblems can be assembled using data and information visibility and traceability, and then solved in a rolling spatiotemporal manner to generate a global solution. A case study shows that ST-OOO has a well-balanced and more stable performance compared to traditional strategies. Sensitivity analysis is carried out to study the impacts of spatial and temporal scales on the results.

网络物理系统（CPS）是工业4.0智能制造最有希望的方向之一。由于在CPS中应用了各种前沿技术，因此决策者可以实时获取大量的制造数据和信息。但是，制造优化的内在复杂性和不确定性仍然困扰着学者和实践者，并阻碍了智能制造的进一步发展。生产计划和调度是一个复杂而随机的问题，已经引起了相当多的研究关注。尽管如何利用CPS的优势来克服复杂性和不确定性的瓶颈，仍然是一个需要进一步探索的问题。本文提出了一种新颖的“分而治之”方法，即时空无序执行（ST-OOO），用于在网络物理工厂中实现实时计划和调度。ST-OOO将工厂的时空范围划分为有限的区域和区间，以减少复杂性并确定不确定性，从而将原始的复杂优化问题分解为具有不同时空特征的子问题集。可以使用数据和信息的可见性和可追溯性来组合这些小子问题，然后以时空滚动方式进行求解以生成全局解决方案。案例研究表明，与传统策略相比，ST-OOO具有良好的平衡性和更稳定的性能。进行敏感性分析以研究时空尺度对结果的影响。ST-OOO将工厂的时空范围划分为有限的区域和区间，以降低复杂性并定位不确定性，从而将原始的复杂优化问题分解为具有不同时空特征的子问题集。可以使用数据和信息的可见性和可追溯性来组合这些小子问题，然后以时空滚动方式进行求解以生成全局解决方案。案例研究表明，与传统策略相比，ST-OOO具有良好的平衡性和更稳定的性能。进行敏感性分析以研究时空尺度对结果的影响。ST-OOO将工厂的时空范围划分为有限的区域和区间，以降低复杂性并定位不确定性，从而将原始的复杂优化问题分解为具有不同时空特征的子问题集。可以使用数据和信息的可见性和可追溯性来组合这些小子问题，然后以时空滚动方式进行求解以生成全局解决方案。案例研究表明，与传统策略相比，ST-OOO具有良好的平衡性和更稳定的性能。进行敏感性分析以研究时空尺度对结果的影响。