Fit‐out operations in modular construction exhibit unique features, such as limited room space and diversly distributed operations in the building. These features pose significant challenges to planning, scheduling, and execution (PSE) of fit‐out activities due to operational parallelism, distributional diversity, and narrower constrained time window in modular construction. Hence, logistics‐operation and multi‐operations synchronizations in a real‐time manner are crucial for PSE of modular construction fit‐out management. With the support of cutting‐edge information technologies, the real‐time data inside the generated digital twins can simplify online optimization models and convert stochastic factors into deterministic parameters. This paper formulates a novel real‐time data‐driven out‐of‐order (OoO) model for synchronized PSE in modular construction fit‐out management. Drawing inspiration from OoO mechanism in Central Processing Unit (CPU), a real‐time data‐driven and rolling‐horizon‐based OoO model is proposed for PSE of modular construction fit‐out, employing a forward heuristic algorithm for solution. Time–space–state data from digital twins are updated to facilitate dynamic decision‐making of managers. Through stochastic computational experiments, we demonstrate the effectiveness of OoO model in optimizing project metrics and the improved resilience in uncertain environments.

中文翻译：

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数据驱动的无序模型，用于模块化施工装修管理中的同步规划、调度和执行

模块化建筑中的装修操作表现出独特的特征，例如有限的房间空间和建筑物内多样化的分布操作。由于模块化施工中的操作并行性、分布多样性和更窄的约束时间窗口，这些功能对装修活动的规划、调度和执行 (PSE) 提出了重大挑战。因此，实时的物流作业和多作业同步对于模块化建筑装修管理的 PSE 至关重要。在尖端信息技术的支持下，生成的数字孪生内部的实时数据可以简化在线优化模型，并将随机因素转化为确定性参数。本文为模块化施工装修管理中的同步 PSE 制定了一种新颖的实时数据驱动的无序 (OoO) 模型。受中央处理单元（CPU）中的OoO机制的启发，针对模块化施工装修的PSE，提出了一种实时数据驱动的、基于滚动水平的OoO模型，采用前向启发式算法进行求解。来自数字孪生的时空状态数据被更新，以促进管理者的动态决策。通过随机计算实验，我们证明了 OoO 模型在优化项目指标方面的有效性以及在不确定环境中提高的弹性。