The intrinsic stochasticity of manufacturing is one of the main factors that hinder system resilience. Planning and scheduling problems are typical examples plagued by uncertainties such as stochastic processing time, arrivals of new orders, and breakdowns of workstations. Frequent uncertainties disturb the workflow, and their cascading effects create chaos in the whole system. The transparency and traceability analytics in Cyber-Physical Manufacturing Systems (CPMS) bring new hope to tackle uncertainties. Inspired by the core spirit of Out-of-Order (OoO) Execution in CPU, this paper proposes a novel OoO architecture for resilient planning and scheduling in CPMS with real-time data analytics. Following OoO principles, multi-level instruction queues are constructed, under which manufacturing operations (instructions) are sequenced and performed by analyzing real-time dependencies and executability. This study contributes a new perspective to enhance decision resilience using real-time data in CPMS. Results validate the effectiveness and resilience of OoO under different levels of uncertainty, showing improvements in the on-time delivery rate and reductions in the average order flow time.

The proposed Address Resolution Protocol (CPI-ARP) is designed for the Cyber-Physical Internet (CPI) environment, integrating physical and digital logistics networks. By extending traditional ARP mechanisms, CPI-ARP addresses the practical requirement for coordinated asset tracking and state awareness of vehicles and Physical Shipment Units (PSUs) within global logistics systems. The protocol enables coordinated interaction between digital and physical operations by linking logical network addresses (PIP) with physical addresses (PMAC) and physical locations. Key challenges related to synchronizing network and physical address systems, including scalability, mobility, and real-time data synchronization, are addressed in this protocol. Performance evaluations through simulation experiments under various network conditions demonstrate CPI-ARP’s ability to reduce latency, improve packet delivery, and support logistics system responsiveness in intra-city and cross-border contexts. These findings pave the way for further development and standardization of CPI networks, contributing to the optimization of global logistics operations.

Transportation systems often use agent-to-agent communication technologies to enhance control performance by transmitting information across wireless networks to keep a reasonable inter-distance between agents. The agent-to-agent data transmission is influenced by the fading channel and the limited communication bandwidth. Denial of Service attack model is used to describe the attack’s influence on control system. Thus, the communication model is built as Bernoulli distribution, Markovian distribution and so on. But in practical situations, it is more complicated than we imagine. To further push the theoretical results to practical situations, we investigate the jamming attack with non-stationary stochastic process in the leader-follower systems and introduce hidden Markovian distribution model (HMM) to describe this special jamming attack. Furthermore, the controller design method is presented to achieve stochastic stability of the leader-follower system. The structure of the control algorithm is a special MPC method, which is divided into a state feedback part and a modification part. The state feedback part is a typical LMIs controller to guarantee the system’s stability, while the modification part is used to improve the influence of the model mismatch and uncertainty. The highlights of the results are the following two points. Firstly, the more complex and precise transportation system model is founded by the hidden Markovian distribution. Secondly, the controller design method is also presented for the multi-agent systems with this HMM transportation system model. The existing results always find the worst case scenario, and design the controller in this scenario. Usually, the controller is very conservative and even unstable. The results in this paper can change the controller in different scenarios to adapt to the corresponding jamming stochastic distribution model, which is the novelty of this paper. We use two examples to illustrate the proposed results. The first one is used to show the ineffectiveness of the results by the simple stochastic distribution model method and the effectiveness of the proposed results in this paper. The second one is used to show that the method can be used in a 3-vehicle-agent system.Note to Practitioners—The paper’s results have some potential applications in logistics industry, automated factory, and flexible automated manufacture systems. In these scenarios, the feed flow mobile robots are widely used to exchange a mass of manufacturing materials. On one hand, usually, single vehicle control theory does not have the group intelligence ability. Its actions only consider the safety and efficiency itself, which may not be the best actions for the whole system. In this paper, we only consider that the vehicles form a platooning, which is the most time of working conditions for mobile robots. On the other hand, usually, the network data exchange environment in manufacturing industry is complex, which is main reason to cause instability of the networked platooning system and also the bottleneck limiting the network data into the closed loop of the control system. In this paper, the new transmission interference model (HMM) is built, whose parameters can be confirmed by the algorithm and the observation data. Based on this new model, the corresponding unique controller is also designed accordingly. The challenge of this method is to find a method that can suppress large disturbances to the maximum extent and maintain system stability. But if the big interference lasts long enough, it means that the system lasts long enough in the open-loop condition, and the system eventually becomes unstable, which is the limitation of our research in theory.

To promote the sharing of medical data assets (MDAs) in a more secure and sustainable manner, this article presents a blockchain-based MDA sharing framework. The contributions of this article are threefold. First, we designed a layered-architecture to decouple the privacy-preserving responsibilities among technologies considering the incentive rewarding and parallelization of execution. Second, we introduce zero-knowledge proofs (ZKPs) in smart contracts with a group signature to construct a supervisory privacy-preserving sharing mechanism, which can be executed in a decentralized environment to protect the privacy of MDAs. Third, we introduce an incentive mechanism that motivates MDA sharing by capturing the decentralized features of the participants to deliver fair rewards. The experiments show that our framework achieves a comprehensive privacy protection on sharing MDAs, comparing with single blockchain sharing schema, with only 2.2% sacrifice on TPS (throughput/second). Moreover, our framework has better potential for large-scale application due to the paralleled execution on ZKP-based smart contracts.

In the field of mineral processing, controlling the paste thickener is a highly challenging and critical task because of the high complexity, incomplete observation space, and excessive environmental noises. In this article, we propose an offline-data-driven controlling strategy to optimize the operational indices in the thickening system based on offline reinforcement learning (RL). Compared to common RL methods that rely on online interactive training, our approach ensures the safety of the production process by training the controller solely using offline datasets, thereby avoiding dangerous online exploration. In terms of offline dataset collection, this study utilizes the prior knowledge of the thickening mechanism to design a proportional–integral–derivative controller as the behavior policy to collect operational trajectories as the offline dataset. In addition, to tackle a critical issue in controlling the thickening system: constrained observation space, this article analyzes the dynamical properties of the thickening system and introduces a novel offline RL algorithm, temporal batch-constrained Q-learning (TBCQ). The algorithm and associated model framework are specifically developed for controlling partially observed Markov decision processes. The TBCQ and trained policy are evaluated in both a simulated thickening environment and a real industrial paste thickener in a copper mine. The real-world experiments demonstrate that the proposed controller outperforms the baselines and effectively reduces the tracking error of underflow concentration by over 12%. The successful application of our pipeline in paste thickener also offers an innovative perspective on addressing optimization problems in complex industrial systems: performing offline RL on a dataset sampled from a suboptimal policy.

This paper explores the application of Cyber-physical Internet (CPI) in prefabricated construction logistics to enhance module shipment efficiency through a new scheduling framework. Drawing parallels between the TCP/IP model’s data transmission process and the physical shipment of construction modules, the study identifies inefficiencies in current logistics practices, including obstructed information sharing and collaboration among practitioners. To address these challenges, the paper proposes a suite of CPI protocols, like the internet protocols, to standardize information sharing, scheduling & routing rules among logistics practitioners and nodes. Based on the CPI protocols, a hierarchical and decentralized shipment decision framework is proposed to govern how the routing decisions and shipment scheduling decisions are made at each logistics node. A set of numerical experiments is conducted based on a real-life shipment case of construction modules in the Greater Bay Area to exhibit the great efficiency and resilience of the proposed protocol-based decision framework. And a case study is designed to show how the proposed protocols influence the decision process. The study’s contributions are threefold: demonstrating CPI application in a logistics scenario, developing protocols for efficient information sharing, and proposing a new decision framework for resilient and timely scheduling in complex logistics networks.

Flexible and reconfigurable manufacturing systems with independent multiskilled cells are pivotal for mass customization under volatile demand, yet internal/external uncertainties persistently disrupt planning, scheduling, and execution (PSE), causing idle time and workflow instability. This study proposes a hybrid synchronization framework with hierarchical real-time data feedback to address heterogeneous demand-capacity synchronization (HDCS), reconciling demand volatility, system reconfigurability, and dynamic human capacity. The framework first adopts the ticket-enabled queuing mechanism of the graduation intelligent manufacturing system (GiMS) to enable seamless PSE integration, ensuring resilient operations. Next, it hybridizes a global optimization model with local adjustment mechanisms, utilizing multigranularity data to balance global optimality and local practicality under uncertainty. Finally, it implements a human-cyber-physical digitalization architecture to establish smart human-machine collaborative assembly. Validated through a case study, the method enhances cost-efficiency by 1.7%, punctuality by 29.5%, and resource utilization by 3.9% compared to conventional rescheduling strategies, demonstrating superior adaptability to uncertainties. The results validate its effectiveness in advancing human-centric smart manufacturing aligned with Industry 5.0 objectives, offering an integrated solution to HDCS challenges through synergistic coordination of hierarchical control, hybrid optimization, and human-machine collaboration.

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.

The emerging digital economy has facilitated the digital transformation of the logistics industry toward a standard and collaborative state. The logistics infrastructure has accordingly attracted much attention as the basis for supporting digital economy implementation. Moreover, the initiatives of regional collaborative development, such as in the Greater Bay Area (GBA) of China, have accelerated the integration of logistics infrastructure and operation-level collaboration. Fortunately, the Cyber-Physical Internet (CPI) has created new opportunities for promoting infrastructure integration through the cyber, physical and internet dimensions. However, regional infrastructure integration in the GBA faces various challenges associated with CPI network operations. Thus, a bottom-up approach is proposed to facilitate logistics infrastructure integration in the CPI environment based on the conceptual fusion of computer networks. Considering the fundamental difference in network fusion and operation mechanisms between CPI and computer networks, a CPI-LI 2 (logistics infrastructure integration) framework is designed as an integrated solution. Finally, a simulation study is conducted using real-life medical logistics data from the GBA to verify the effectiveness of the CPI-LI 2 framework. Experiments show that the framework is well adapted to large-scale logistics networks with dynamically changing links.

This study aims to address a novel production and intralogistics synchronization (PiLSync) problem in multiresource-constrained assembly systems (MRCASs). PiLSync is highly complex due to the presence of multiple resources (operators, workstations, and materials) and the dynamic interactions between the resources and decisions. Moreover, various uncertainties, such as new/urgent job arrivals, uncertain operating times, equipment failures, and operator absences, further complicate this problem. Emerging Industry 4.0 technologies help capture and monitor the real-time PiL workflow, while existing methods are typically formulated to handle static or periodically updated inputs. Thus, it is difficult to explicitly incorporate real-time data into models, leading to a gap between production and intralogistic (PiL) decisions and actual executions under various uncertainties. In this article, out-of-order synchronization (OoOSync) is developed to facilitate flexible, resilient, and coordinated PiL operations in MRCASs. A sliding synchronization window is proposed as an effective decomposition tool to limit system complexity and localize demand uncertainty. Ticket validation governs interactions between resources and constraints under uncertainty. Finally, real-time data-driven synchronization mechanisms are developed for PiL decision/adjustment. The superiority of OoOSync is confirmed in computational experiments. This article is a pioneering study considering multiresource-constrained PiL operations under uncertainty. Additionally, this article offers a new perspective on using real-time data for decisions and operations subject to the dynamic interactions of resources.

Acquiring the real-time spatial–temporal information of manufacturing resources holds the promise to enable efficient operation in factory logistics. This article proposes a system architecture using industrial Internet of Things and digital twin technologies to fulfill spatial–temporal traceability and visibility with seamless cyber-physical synchronization for finished goods logistics in the workshop. A long short-term memory network-enabled genetic indoor-tracking algorithm (GITA) is developed to locate product trolleys via a bluetooth low energy technology, with ultra-wideband applied to sample labeling in the training stage. It is enlightened by genetics to achieve self-adapting online for the long-term performance. A feature selection method based on received signal strength indicator is designed to deal with signal multipath fading and streamline the learning process. In addition, the spatial–temporal information obtained is leveraged to activate location-based services that can help promote operational efficiency. Moreover, a real-life case study is carried out in a world-leading computer manufacturer’s factory to illustrate the viability and practicality of the system and methods proposed, with hardware and software developed. By comparison, the GITA shows superiority over existing approaches despite various noises under the manufacturing scenario, attaining a location precision of about 2 m with a 98.12% accuracy.

This paper discussed the contradictory between wait time for computation and solution quality in solving flexible job shop scheduling problems. In order to reconcile this contradictory, a novel scheduling strategy called Simultaneous Scheduling is proposed. At first, a fairly good solution is obtained in a short time and the solution is set as the temporary processing plan so that the machines can start to work as soon as possible. Then while the machines are running, the temporary plan is being improved with evolutionary algorithms continuously. Experiments on both static and dynamic scheduling problems are performed. The results show that simultaneous scheduling is effective and efficient.

The traditional fault diagnosis methods for rolling bearing usually require the test data and training data to follow the same distribution, which cannot be always meet in real-world scenarios, since the working condition of rolling bearing is often variable. Hence, to overcome the low performance of fault diagnosis traditional methods for different data distributions, a fault diagnosis approach based on transfer learning is proposed in this paper. And the main idea of our approach is to combine joint distribution adaptation and support vector machine to diagnose bearing faults under variable working conditions. In this research, kernel-JDA is used to reduce the difference between distributions of datasets taking both the marginal and conditional distributions into consideration, while the parameters of kernel-JDA are optimized to improve the performance. Besides, multi-features including time domain features and the relative wavelet packet energy are constructed at first to prepare for fault diagnosis. After mapping the multi-features through kernel-JDA, SVM is utilized to diagnose faults of rolling bearing under different working conditions. In addition, comparison experiments on vibration signal datasets of rolling bearings are carried out to verify the effectiveness and applicability of this approach for both the normal and small sizes of the sample sets.