Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represented by digital twins for facilitating efficient monitoring, prediction, and optimization of factory operations. A Federated Digital Twin (FDT) emerges by combining heterogeneous digital twins, enabling real-time collaboration, data sharing, and collective decision-making. However, deploying FDTs introduces new concurrency control challenges, such as priority inversion and synchronization failures, which can potentially cause process delays, missed deadlines, and reduced customer satisfaction. Traditional concurrency control approaches in the computing domain, due to their reliance on static priority assignments and centralized control, are inadequate for managing dynamic, real-time conflicts effectively in real production lines. To address these challenges, this study proposes a novel concurrency control framework combining Deep Reinforcement Learning with the Priority Ceiling Protocol. Using SimPy-based discrete-event simulations, which accurately model the asynchronous nature of FDT interactions, the proposed approach adaptively optimizes resource allocation and effectively mitigates priority inversion. The results demonstrate that against the rule-based PCP controller, our hybrid DRLCC enhances completion time maximum of 24.27% to a minimum of 1.51%, urgent-job delay maximum of 6.65% and a minimum of 2.18%, while preserving lower-priority inversions.

As global energy demand intensifies across industrial, commercial, and residential domains, efficient and accurate energy management and control become crucial. Energy Digital Twins (EDTs), leveraging sensor measurement data and precise time-series forecasting models, offer promising monitoring, prediction, and optimization solutions for such services. Edge computing enables EDTs to deliver real-time management services placed closer to users. However, the existing energy management methodologies may fail to consider the limited resources of edge environments, which may cause service delays and reduced accuracy in management services. To solve this problem, we propose a novel energy control digital twin system with a resource-aware optimal forecasting mode selection scheme. The system dynamically selects optimal forecasting models by integrating statistical features of the input time series with available resources. It employs a two-stage approach: first, it identifies promising models through similarity detection in past time series; second, this initial recommendation is refined by considering the available computing resources to pinpoint the optimal forecasting model. This mechanism enhances adaptability and responsiveness in resource-constrained environments. Utilizing real-world LPG consumption data from 887 sensors, the proposed system achieves forecasting accuracy comparable to previous methods while reducing latency by up to 19 times in low-resource settings.

Humanoid robots are emerging as flexible robotic resources for autonomous manufacturing systems, where different types of tasks must be assigned to suitable robots while shared production resources are coordinated effectively. However, realistic manufacturing environments involve dynamic task arrivals, event-driven priority changes, heterogeneous robot capabilities, and shared-resource contention. In addition, digital twin-based scheduling may rely on state information that is delayed or uncertain, which can reduce the reliability of scheduling decisions. To address this issue, this paper extends the previously proposed deep reinforcement learning-based concurrency control (DRLCC) framework for robustness-aware scheduling and shared-resource control of a multi-functional humanoid robot team. The extended framework integrates capability-aware task assignment, feasibility-based action masking, and priority ceiling protocol (PCP)-based shared-resource coordination under delayed and uncertain digital twin observations. The framework is evaluated in a humanoid-based autonomous manufacturing scenario using performance indicators including task completion, urgent-task delay, resource contention, humanoid utilization, and robustness degradation under imperfect state feedback. Compared with the greedy ceiling-based baseline, DRLCC reduces high-priority task delay by approximately 10.9%, priority inversions by 42.1%, average waiting time by 73.8%, average block count by 74.0%, and temporary infeasible events by 73.5%, while maintaining comparable humanoid utilization. The robustness analysis further shows that overall task-completion performance remains stable under imperfect digital twin feedback, although coordination-level metrics are more sensitive to observation uncertainty and delay. These results suggest that the extended DRLCC framework can support robustness-aware humanoid robot team scheduling in autonomous manufacturing environments where digital twin observations are delayed or uncertain.