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.

Advances in image editing models have enabled intelligent, rapid fashion customization. Vision-guided editing models, in particular, offer more precise and flexible control over fine-grained garment attributes. However, existing methods are limited to coarse-grained edits and fail to achieve attribute-level manipulation, thereby restricting the flexibility and composability required in fashion customization. To address these issues, this paper proposes a Vision-Guided Fashion Fine-Grained Attribute Editing (VFFAE) framework, which leverages visual references to achieve customized editing of both style and structure in fine-grained garment regions. The VFFAE framework involves three key components: (1) a text-driven fashion fine-grained attribute segmenter that incorporates garment keypoints as spatial priors and applies deformable attention to enhance spatial perception, with CLIP-based multimodal alignment for accurate segmentation; (2) a clothing attribute disentanglement module based on orthogonal subspace projection of CLIP embeddings, enabling zero-shot explicit separation of style and structure attributes; and (3) a conditional diffusion pipeline that leverages disentangled representations of segmented regions to fine-tune a pretrained Stable Diffusion model under classifier-free guidance, enabling controllable attribute editing. Experiments on multiple public datasets show that VFFAE surpasses state-of-the-art methods, and ablation analyses confirm the effectiveness of its segmentation and disentanglement modules, establishing it as a practical solution for high-fidelity attribute-level fashion customization.

Enhancing large language models (LLMs) with external knowledge base retrieval in the fashion manufacturing industry can provide more reliable technical support and decision-making assistance, significantly improving process control and boosting intelligent production efficiency. However, the field of fashion manufacturing involves highly specialized terminology, logically complex technical knowledge, and intricate query tasks. Existing simple query-matching techniques often return a large number of contextually loose and redundant document chunks, severely impacting the model's understanding and response quality. To address this issue, this article proposes a retrieval optimization framework based on a dynamic capsule routing network with embedded semantic graph (SGDCR), which models semantic relations among multiple retrieved documents by simulating a team collaboration mechanism. Specifically, the framework consists of two steps: filtering and reranking. First, a capsule routing mechanism embedded in a semantic association graph dynamically captures complex contextual relationships among coarse-grained document blocks, learns contribution scores for multiple documents, and filters irrelevant or redundant documents based on ranking. Subsequently, the filtered documents are matched with the query through deep semantic similarity measurement, and the documents are reranked by integrating relevance scores and contribution scores and generating efficient, accurate, and contextually coherent document prompts. Experimental results on publicly available dense open-domain QA datasets and a constructed fashion manufacturing process QA dataset demonstrate the effectiveness and superiority of the proposed method over existing reranking approaches in the fashion manufacturing knowledge QA system.

To meet personalized user demands, customized and personalized production (CPP) has become an effective manufacturing paradigm. However, wired network connections inhibit flexible production line reconfiguration and current DRL methods cannot converge and obtain eligible scheduling results for CPP due to the high-dimensional solution space and the negligence of significant machine reconfiguration time. To address this challenge, we first propose a wireless manufacturing system framework to support ultra-flexible reconfiguration and resource scheduling. Next, we build a reconfiguration oriented scheduling model to reflect the significant impact of reconfiguration time. Then, we design a knowledge guided deep reinforcement learning algorithm to effectively solve the CPP scheduling problem facing the dimension explosion problem. The knowledge guidance incorporates reconfiguration time and machine workload to significantly reduce the feasible action space, enabling the rapid convergence of KGDRL. The experiment results show that our approach provides a robust and scalable solution and obtains shorter total makespan of whole production during scheduling.

Production logistics (PL) is essential for linking manufacturing activities through the timely transportation of work-in-progress items. However, increasing product customization, a broader range of materials, and more complex spatial-temporal constraints among resources have made PL orchestration significantly more challenging. To address these issues, we propose the cyber-physical computer (CPC), an intelligent edge terminal designed to coordinate operators, vehicles, robots, and materials involved in PL tasks. The CPC continuously collects Internet of Things (IoT) signals and extracts semantic-level relationships among PL resources, constructing a resource semantic graph that forms the basis for cyber-physical twinning. Upon receiving a new resource request, the CPC identifies relevant nodes to generate a corresponding spatial-temporal subgraph and performs subgraph reasoning by integrating contextual information with its local knowledge memory. Informed by task-specific insights distilled from prior expert decisions, the CPC recommends a prioritized allocation plan to the manager and enables interactive refinement using a large language model. To validate the proposed approach, we conduct comparative experiments across four simulated environments representing typical PL scenarios with varying degrees of dynamicity, along with a real-world case study at an air conditioning equipment manufacturer. Results demonstrate that the CPC, empowered by subgraph reasoning, outperforms existing methods in punctuality rate and delivery distance.

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.

With increasing pressure from customized demands, discrete manufacturing systems face challenges due to fluctuating resource requirements. These challenges hinder the synchronization of production logistics (PL), which is essential for coordinating resources and ensuring smooth production. Poor synchronization will result in resources waiting on each other, leading to delays and idle time. Accordingly, this paper proposes ChatSync, a framework leveraging large language model (LLM) and spatial-temporal knowledge reasoning to optimize resource allocation, delivery, and monitoring in industrial applications, particularly within the Industrial Internet of Things (IIoT) environment. First, the resource spatial-temporal graph (RSTG) is constructed by integrating real-time IIoT data and expert operational experience, enhancing the knowledge base of LLM through cross-domain knowledge fusion. Second, graph-based reasoning optimization is presented, incorporating spatial-temporal, contextual, and relational reasoning mechanisms, enabling LLM to achieve credible and responsible analysis and decision-making. Third, the PL-oriented ChatSync framework with knowledge and reasoning engines is proposed, supporting chat-based interactions for resilient resource allocation, personalized suggestion, and precise traceability. A case study in air conditioning manufacturing demonstrates that ChatSync outperforms existing benchmark methods in various PL phases, achieving a delivery punctuality rate of 91.2%.

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.

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.

Foundation models have demonstrated remarkable capabilities in various tasks such as natural language processing, content generation, and complex reasoning and have the potential to spark new technology and application revolutions in the industrial domain. However, Industrial Foundation Models (IFMs) remain almost unexplored, and the industrial sector has domain-specific issues and challenges to address when harnessing the capabilities of foundation models. Therefore, we introduce the concept and construction paradigm of IFMs and propose a 5-dimensional general framework of the IFMs. Moreover, we present the key research issues and technologies of IFMs and discuss some advanced and potential industrial applications. We hope this paper can serve as a useful resource for researchers seeking to innovate within the domain of IFMs.

Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigating the problem of cross-modality gait recognition, with the objective of accurately identifying pedestrians across diverse vision sensors. We present CrossGait inspired by the feature alignment strategy, capable of cross retrieving diverse data modalities. Specifically, we investigate the cross-modality recognition task by initially extracting features within each modality and subsequently aligning these features across modalities. To further enhance the cross-modality performance, we propose a Prototypical Modality-shared Attention Module that learns modality-shared features from two modality-specific features. Additionally, we design a Cross-modality Feature Adapter that transforms the learned modality-specific features into a unified feature space. Extensive experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of CrossGait: (1) it exhibits promising cross-modality ability in retrieving pedestrians across various modalities from different sensors in diverse scenes, and (2) CrossGait not only learns modality-shared features for cross-modality gait recognition but also maintains modality-specific features for single-modality recognition.

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.

Video-based gait recognition has achieved impressive results in constrained scenarios. However, visual cameras neglect human 3D structure information, which limits the feasibility of gait recognition in the 3D wild world. Instead of extracting gait features from images, this work explores precise 3D gait features from point clouds and proposes a simple yet efficient 3D gait recognition framework, termed LidarGait. Our proposed approach projects sparse point clouds into depth maps to learn the representations with 3D geometry information, which outperforms existing point-wise and camera-based methods by a significant margin. Due to the lack of point cloud datasets, we build the first large-scale LiDAR-based gait recognition dataset, SUSTech1K, collected by a LiDAR sensor and an RGB camera. The dataset contains 25,239 sequences from 1,050 subjects and covers many variations, including visibility, views, occlusions, clothing, carrying, and scenes. Extensive experiments show that (1) 3D structure information serves as a significant feature for gait recognition. (2) LidarGait outperforms existing point-based and silhouette-based methods by a significant margin, while it also offers stable cross-view results. (3) The LiDAR sensor is superior to the RGB camera for gait recognition in the outdoor environment. The source code and dataset have been made available at https://lidargait.github.io.

Most gait recognition methods exploit spatial-temporal representations from static appearances and dynamic walking patterns. However, we observe that many part-based methods neglect representations at boundaries. In addition, the phenomenon of overfitting on training data is relatively common in gait recognition, which is perhaps due to insufficient data and low-informative gait silhouettes. Motivated by these observations, we propose a novel mask-based regularization method named ReverseMask. By injecting perturbation on the feature map, the proposed regularization method helps convolutional architecture learn the discriminative representations and enhances generalization. Also, we design an Inception-like ReverseMask Block, which has three branches composed of a global branch, a feature-dropping branch, and a feature scaling branch. Precisely, the dropping branch can extract fine-grained representations when partial activations are zero-outed. Meanwhile, the scaling branch randomly scales the feature map, keeping structural information of activations and preventing overfitting. The plug-and-play Inception-like ReverseMask block is simple and effective, improving the performance of many state-of-the-art methods. Extensive experiments demonstrate that the ReverseMask regularization help baseline achieves higher accuracy and better generalization. Moreover, the base-line with Inception-like Block significantly outperforms state-of-the-art methods on the two most popular datasets, CASIA-B and OUMVLP.

LiDAR is a new type of sensor used for gait recognition. Previous LiDAR-based state-of-the-art methods mostly exploit gait features from the depth maps generated by projecting point clouds in a 3D-to-2D manner, rather than directly using the raw 3D point data. However, these projection-based methods require an additional preprocessing step, which obstructs the universality of the method among different types of LiDARs. On the other hand, while existing point-based methods have achieved promising results in 3D object recognition, they have underperformed in 3D gait recognition, indicating the presence of a domain gap between coarse-grained 3D object classification and fine-grained 3D pedestrians recognition. By analyzing the success achieved by camera-based methods, we perceive that point-based gait recognition fails mainly because of neglecting to capture local representation. To address this issue, we propose an end-to-end 3D gait recognition framework named PointGait, which can directly capture informative gait features from point cloud data. Specifically, PointGait is a multi-stream model consisting of a Global and Local Gait Feature Extractor to extract holistic and fine-grained spatial features. Besides, a Personalized Motion Extractor is introduced to capture inter-frame motion features. Our experimental results on a LiDAR gait dataset, SUSTech1K, outperform all popular point-based methods, demonstrating the effectiveness and potential of our approach. In conclusion, the proposed PointGait promotes the development of point-based gait recognition by highlighting the importance of incorporating fine-grained spatiotemporal information.

In perishable supply chain logistics, even a small departure from the required storage conditions at any distribution link can compromise the quality of transported products, such as food, pharmaceuticals, and other bioproducts, resulting in big losses for the businesses involved or even threats to public health. To enhance quality management (QM) and consumer confidence, an edge-cloud blockchain and Internet of Everything (IoE)-enabled QM platform is proposed to achieve low delay and rapid response for sensor data acquisition, authentication, consistency, and transparency in cold supply chain logistics. Then, we design an adaptive data smoothing and compression (ADSC) mechanism to reduce IoE data size, and analyze and store those data in the edge gateways with limited computation and storage capacity for correctly characterizing logistics operations and transactions. Moreover, to ensure the data integrity during last-mile delivery, the mobile edge gateway is adopted when the goods are temporarily off the communication range of the fixed edge gateway in the truck. Then, we propose a synchronization engine with a formal workflow applied at mobile and fixed edge gateways where data blocks are generated, validated, and synchronized with the cloud. Finally, a real-life case study on vaccine logistics is introduced to verify our proposed approach with results presented.

Logistics interfaces with manufacturing throughout the entire production process need synchronous operations. For achieving integrated organization and operations between manufacturing and logistics, this article introduces the concept of shop-floor logistics and manufacturing synchronization with four principles, including: 1) synchronization-oriented manufacturing system; 2) synchronized information sharing; 3) synchronized decision making; and 4) synchronized operations. The marriage of the principles rendered the development of an overall framework of the Industrial Internet of Things (IIoT) and digital twin-enabled graduation intelligent manufacturing system (GiMS). A mixed-integer programming-based synchronization mechanism is proposed under GiMS. To meet the requirement of fast decision making in real-life shop-floor logistics and manufacturing synchronization problems, an equivalent constraint programming model is developed and tested. The observation and analysis of the case company show the advantage of the proposed concept and approach with the best performance regarding key performance indicators. The concept of synchronization provides an insight for understanding the interaction of logistics and manufacturing at the operational level. This article potentially enables manufacturers to reevaluate and develop their manufacturing planning and control strategies in the IIoT and digital twin-based manufacturing environment.

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.

With the increasingly developed industry and more comprehensive product offerings, customized and personalized products (CPPs) gradually become a main business model of many enterprises. However, the characteristics of CPPs, such as large differences in product modules and short product delivery cycles, put forward very high demands for the intelligence, flexibility and real-time performance of cloud manufacturing (CMfg). To satisfy the above typical demands, a cloud-edge-device collaborative framework of CMfg is proposed to support distributed data processing and fast decision-making. In the context of Cloud-edge-device collaboration, the vertically and horizontally distributed deployment and update mechanisms of deep learning models (DLMs) are brought forward and analyzed in detail to provide rapid response and high-performance decision-making services for CPPs. In addition, related key technologies are presented to provide references for the technical research direction.

This IEEE Transactions on Automation Science and Engineering (T-ASE) Special Section on New Frontiers in Smart Factories: Smart Automation and Human–Robot Interaction focuses on promising, innovative research outcomes and industrial applications of different key technologies for smart automation and human–robot interaction.

Industrial parks are regarded as a possible fashion to reduce the manufacturing costs and increase the efficiency by sharing cost-effective infrastructure and communal services. However, the waste of manufacturing resources could impede the development of manufacturing enterprises that locate in an industrial park when market demands fluctuate dynamically. This article aims to use the cloud manufacturing (CMfg) to relieve the temporary shortage of manufacturing resources and capabilities by sharing them among these enterprises in an industrial park. This article focuses on manufacturing cloud service allocation (MCSA), which is one of the critical processes to implement CMfg. Two multiunit double auction mechanisms are proposed to allocate the manufacturing cloud services (MCSs) with reasonable trade price in bilateral markets, and their properties and bidding strategies are proved. Numerical studies are conducted, and the results show the effectiveness and efficiency of two mechanisms for dealing with MCSA and verify the theoretical proofs.Note to Practitioners—MCSA in CMfg is a complicated and challenging task. The current industrial applications rely on optimization methods, such as all-in-one methods (e.g., genetic algorithm) and multidiscipline design optimization methods (e.g., analytical target cascading), to allocate MCSs. While these methods can help cloud operators obtain feasible allocation solutions rapidly, there exist several disadvantages: 1) they can only solve problems in one-sided settings (one customer with many providers); 2) there is an assumption that the customer’s demand can be provided by any providers, which means providers’ capacities are infinite; and 3) the problem is optimized based on the fixed prices provided by providers, without considering supply and demand. This article presents two multiunit double auction mechanisms that allow multiple customers to trade with multiple providers. Allocation rules can break the assumption and providers’ capacities are limited. MCSs are also priced dynamically based on supply and demand. We show how cloud operators apply two mechanisms first and demonstrate their properties and bidding strategies of customers and providers when the mechanism is not incentive compatible. Finally, our experiments and analysis fully validate the applicability and efficiency of two mechanisms and illustrate how cloud operators can maximize their utilities.

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.

Industrial trends and new generation information and communication technologies have become driving forces for advancement in the process control and manufacturing industry. This paper thoroughly investigates the future industrial trends from the perspectives of market, engineering system, product, innovation, etc., then incorporates the concept of software defined networking and proposes a new cloud based manufacturing model, Software Defined Cloud Manufacturing (SDCM). The key characteristics, reference architecture and emerging enabling technologies of SDCM are presented to support the SDCM's advantages in terms of real-time response, reconfiguration and operations of the manufacturing system. Resource virtualization and function programmability lie at the core of SDCM to empower the manufacturing sector. The paper is concluded with remarks and future work.

Customer service is transforming from traditional manual service toward automated service, which utilizes different computational informatics to achieve a higher efficient and quality services. Automated customer service requires big data and expertise in data analysis as prerequisites. However, many companies, especially small and medium enterprises, do not have sufficient data and experience due to their limited scale and resources. They need to rely on third parties, and this reliance results in the lack of development of core customer service competency. In order to overcome these challenges, an open and automated customer service platform based on Internet of things (IoT), blockchain, and automated machine learning (AutoML) is proposed. The data are gathered with the use of IoT devices during the customer service. An open but secured environment to achieve data trading is ensured by using blockchain. AutoML is adopted to automate the data analysis processes for reducing the reliance of costly experts. The proposed platform is analyzed through use case evaluation. A prototype system has also been developed and evaluated. The simulation results show that our platform is scalable and efficient.

This paper is among the first proposing an integrated auction and market design method for the parking space sharing and allocation problem. Drivers (agents) who fail to exchange their own parking spaces can then rent them to the platform. The platform receives private parking spaces from agents and manages some public parking spaces. We first develop the urban parking management cloud platform through Internet of Things. Based on this systemic framework, parking spaces are shared among agents via a price-compatible top trading cycles and chains (PC-TTCCs) mechanism and the platform's parking spaces are reassigned via a one-sided Vickrey-Clarke-Groves (O-VCG) auction. Both the PC-TTCC mechanism with rule e (PC-TTCC [e]) and O-VCG auction are effective in terms of strategy-proofness and (allocative or Pareto) efficiency. In the PC-TTCC [e] mechanism, the platform's payment rule used in private parking space sharing is determined based on historical O-VCG auction prices. Our experimental results further show that the proposed mechanism results in system profitability of 20%-30% and ex post budget balance for the platform.

Emergency fleet management has become one of the determinant success factors for post-disaster responses in urban flood control. However, it is challenging as multiple types of emergency vehicles are involved, and its performance is frequently threatened by the fluctuation of rescue demands and fleet capacity. Aiming at coping with the imbalances between rescue demands and vehicle supplies, and maintaining required service level of fleet management after flood occurs, this paper proposes a data-driven resilient fleet management solution under the context of cloud asset-enabled urban flood control. First, the problem of resilient fleet management is quantitatively defined, and then a data-driven dynamic management mechanism is proposed, which is highly effective on realizing resilient fleet management. Furthermore, considering the cooperation among different types of emergency vehicles, a greedy-based algorithm is proposed for resilient vehicle dispatching based on real-time scenarios. Finally, a simulation case is also conducted to verify the effectiveness and performance of the proposed solution.

Based on the process of augmented Lagrangian coordination (ALC) solving, a solution framework of ALC for the complex system problem is proposed. Oriented to order fulfillment modes and the process of production outsourcing modes of process manufacturing enterprises in an industrial cluster and according to the hypothesis condition, an optimal configuration model of process cluster supply chain (PCSC) considering order horizontal subcontracting and cross-chain process production outsourcing is set up. By dividing the independent decision-making rights of the participants in single supply chains, a distributed optimal configuration model is established. Meanwhile, ALC is employed to solve the distributed model and solving results are analyzed and compared with the traditional method. Results comparison confirms the accuracy and efficiency of ALC for the PCSC configuration problem.

The order picking process has been considered as the most time-consuming and costly activity in warehouse management. Efficient location management, including product location management and storage location assignment management, serves as the key to reducing product searching time and traveling time during the process. This research is motivated by a real-life industrial case, which is from a forklift manufacturer that encountered the underperformance in warehouse management. Excessive time was consumed in searching and picking the forklift in finished product warehouse, which thus led to great waste in resources and even the postponement of delivery. To solve this problem, this paper first presents the details about the problem and related processes. A conceptual model of cloud forklifts and its life cycle oriented management platform is then proposed. Accordingly, a prototype system named cloud forklift warehouse management system (CFWMS) is built based on the proposed model. The suggested model and system architecture is designed and developed with the commonality for general warehousing processes, and therefore can be easily configured and applied to any general warehouse environment. Results and details of implementation for the real-life case are also provided to demonstrate the validity of the proposed model and CFWMS.

Customized/personalized products are gaining more shares in today's product market. Such products need collective efforts from consumers, manufacturers and third parties. On the other side, the Internet of Things (IoT) with pervasive sensing/actuating/networking ability greatly facilitates remote operation of manufacturing activities and efficient collaboration among stakeholders. This provides great opportunities to the above demand. Thus we propose a full-connection model of product lifecycle in the IoT-enabled cloud manufacturing environment. The model uses social networks to connect multiple parties and facilitate open innovations, IoT to glue physical space to cyber space and cloud manufacturing to provide various elastic services, so that the on-demand workspace, interaction, information sharing or collective problem solving are enabled. We also propose a supporting infrastructure for this model using the latest information and communication technologies. Finally, we present a RFID (Radio-frequency identification) enabled production system for customized/personalized products with the ability to enable a new paradigm of “dynamic processes and close collaborations among different roles” and secure robust production.

A Supply Hub in Industrial Park (SHIP) is a thirdparty business entity that leases storage space and logistics services among manufacturers located in the same industrial park. Manufacturers may hire warehouses outside the park if SHIP's storage price is exorbitant. This paper discusses how SHIP and manufacturers interact to optimize their decisions on storage pricing, replenishment, and delivery. A dynamic storage pricing strategy depending on storage length is adopted. This problem is modeled as a bilevel program where the SHIP is the leader and manufacturers are followers. Based on further assumptions, the proposed bilevel model is solved in closed-form. A series of sensitivity analyses are conducted to examine the influences of major cost parameters. The results show that SHIP's delivery charge has little impact on the total cost of large manufacturers, and its increase will not always bring profit improvement to SHIP. Contrary to intuition, the SHIP could attract more space demands from large manufacturers when charging higher in delivery or when the public warehouse's delivery charge is lower, and more space demands from small manufacturers when the market storage price is lower. Note to Practitioners-It becomes increasingly common for an industrial park to provide a supply hub for its member enterprises to share warehousing and transportation services. The SHIP gains its main revenue from leasing storage space. Hence, it is natural that SHIP must establish a suitable storage pricing strategy to attain the maximum profit. Being motivated by this challenge, this paper investigates a dynamic storage pricing of SHIP in a typical supply chain with one SHIP and multiple manufacturers. We develop a bilevel model to study this problem and derive the optimal solutions in closed-form for special cases. We provide valuable managerial guidance for the SHIP operator to charge dynamic storage price, and for member enterprises to schedule their replenishment and delivery under different scenarios. Several counterintuitive findings are obtained regarding the influence of delivery costs inside and outside the industrial park, which will attract attention from managers of SHIP and member firms. This paper is limited to certain assumptions in deriving the optimal decisions. In future research, we will relax the assumption and design other methods to solve the proposed bilevel model. We will also investigate other forms of storage pricing strategies, which will shed further light on SHIP's decision automation.

Urban flood control has become an important issue for both city planners and researchers as flood is one of the most severe natural disasters for large cities. Although many measures have been implemented for urban flood control, the effectiveness and efficiency is greatly hampered by the poor management of physical assets. In urban flood control, managing physical assets is challenging as it involves a complex asset base with the dispersion of asset allocations, management processes, and information systems. To address these challenges, this paper proposes an integrated asset management platform for urban flood control. Cloud technology is adopted to provide whole life cycle management of physical assets by integrating various management processes and information systems. It also enables the cooperative management between sectors. Besides, the concept of smart asset is introduced to realize remote management and real-time data collection from these assets. In the end, a case study is also given to demonstrate the working procedural of managing a typical smart asset through the proposed cloud platform.

Radio Frequency Identification (RFID) technology has been widely used in manufacturing sites for supporting the shopfloor management. Huge amount of RFID-enabled production data has been generated. In order to discover invaluable information and knowledge from the RFID big data, it is necessary to cleanse such dataset since there is large number of noises. This paper uses n-dimensional RFID-Cuboids to establish the data warehouse. A big data cleansing approach is proposed to detect, remove and tidy the RFID-Cuboids so that the reliability and quality of dataset could be ensured before knowledge discovery. Experiments and discussions are carried out for validating the proposed approach. It is observed that the proposed big data cleansing approach outperforms other methods like statistics analysis in terms of finding incomplete and missing cuboids.

This paper studies a vendor-managed-inventory (VMI) supply chain where a manufacturer, as a vendor, procures a type of nondeteriorating raw material to produce a deteriorating product, and distribute it to multiple retailers. The price of the product offered by one retailer is also influenced by the prices offered by other retailers because consumers can choose the product from any of the retailers. This paper is one of the first papers that propose an integrated model to study the influence of pricing and deterioration on the profit of such a VMI system. A hybrid approach combining genetic algorithms and an analytical method is developed for efficiently determining the optimal price of the product of each retailer, the inventory policies of the product and the raw material. Our results of a detailed numerical study show that parameters related to the market and deterioration have significant influences on the profit of the VMI system. However, different from common intuition, we find that an increase in the substitution elasticity of the product among different retailers can bring an increase in the retail prices of the product, while the increase of the market scale can reduce the retail prices.

This special issue explores the state of the art across the RFID technology landscape from hardware, to systems, as well as applications and support for innovative business models.

Radio-frequency identification (RFID) has a great number of unfulfilled prospects. Part of the problem until now has been the value proposition behind the technology-it has been marketed as a replacement technique for the barcode when the reality is that it has far greater capability than simply non-line-of-sight identification, towards decision making in strategic management and reengineered business processes. The vision of the internet of things (IOT) has not eventuated but a world in which every object you can see around you carries the possibility of being connected to the internet is still within the realm of possibility. However incremental innovations may see RFID being sold as a service (much like photocopiers are maintained today) than a suite of technologies within a system that are sold as individual or bundled packaged components. This paper outlines the vision for such a product service system, what kinds of smart applications we are likely to see in the future as a result, and the importance of data management capabilities in planetary-scale systems.

The problem of minimizing makespan on a single batch-processing machine with non-identical job sizes is strongly NP-hard. This paper proposes an ant colony optimization (ACO) algorithm with chaotic control to solve the problem. The metropolis criterion is adopted to select the paths of ants to escape immature convergence. In order to improve the solutions of ACO, a chaotic optimizer is designed and integrated into ACO to reinforce the capacity of global optimization. Batch first fit is introduced to decode the paths into feasible solutions of the problem. In the experiment, the instances of 24 levels are simulated and the results show that the proposed CACO outperforms genetic algorithm and simulated annealing on all the instances.

This paper considers a supply chain that comprises multiple independent and autonomous enterprises (project managers) that seek and select various contractors to complete operations of their project. Both the project managers and contractors jointly determine the schedules of their operations while no single enterprise has complete information of other enterprises. The centralized scheduling approach that can usually obtain good global performance but must share nearly complete information that is difficult or even impractical due to the distributed nature of real-life supply chains. This paper proposes an agent-based supply chain model to support distributed scheduling. A modified contract-net protocol (MCNP) is proposed to enable more information sharing among the enterprises than conventional CNP. Experimental simulation studies are conducted to compare and contrast the performances of the centralized [centralized heuristic (CTR)], conventional CNP, and MNCP approaches. The results show that MCNP outperforms CNP and performs comparably with CTR when project complexity is high in terms of the total supply chain operating cost. Moreover, it is found that although CTR is better than MCNP in terms of global performance, MCNP yields good schedule stability when facing unexpected disturbances.

POPIM (Pragmatic Online Project Information Management) is a prototype Web-based platform for managing collaborative product development projects within an extended enterprise environment. A suite of facilities are provided for geographically dispersed project team members to communicate, share, and collaborate on a project in a common workspace where they enjoy online access to the most up to date project information and maintain a high-level data consistency and accumulate experience and a knowledge-base. In addition to standard project management functionality such as defining work structure breakdowns, determining work schedules, teaming up with specialists, and allocating resources, POPIM incorporates workflow management (including dependency management), and deliverable management (document management if documents are considered as one kind of deliverables). Individual members have their personalized accounts according to their skills and roles/responsibilities in a project. A project team and its members may maintain their own journals/records. More application-specific functions such as product design review and engineering change management can be implicitly performed through online document forms.

Product design review is one of the typical scenarios of collaborative product development. We developed a Web-based prototype framework for supporting collaborative tele-product design review on the Internet. It provides a suite of tools to establish and manage the new product design review process. The paper discusses the issues related to the development and implementation of Web applications in prototype implication. The typical 3-tier architecture is explained to show how the CyberReview components work together to achieve the intended functionality. VRML EAI and Java Applet-Servlet pair technology was included to support the implication.