Fuqiang Zhang

In the context of escalating geopolitical tensions and increasing supply uncertainty, global supply chains are increasingly shifting from the efficiency-oriented just-in-time (JIT) paradigm to a more redundancy-based just-in-case (JIC) strategy. The primary objective of this study is to examine the topological and functional implications of this strategic shift. To achieve this, this study first develops a directed and weighted five-tier supply chain network evolution model that incorporates dynamic compensation mechanisms, including multi-source substitution and internal inventory reserves. The model shows that the JIC strategy alleviates the “rich get richer” effect, leading to more balanced resource allocation. Based on this model, we investigate underload cascading failures triggered by supply shortages and evaluate network robustness using the order fulfillment rate. The results show that the evolved network has obvious dual characteristics of robustness and vulnerability. It can relatively resist small random disturbances, but it is particularly vulnerable to targeted attacks on key enterprises, and the system will fail rapidly due to bidirectional load propagation. In addition, although the JIC strategy improves the resilience to severe disturbances, excessive hoarding costs increase the threshold for the network’s survival and operation, and may also lead to early underload cascading failures under slight shocks. Managerially, this implies that focal enterprises must dynamically optimize, rather than blindly maximize, their strategic reserves to balance resilience with capital lock-up costs. These findings offer a theoretical basis for understanding the trade-off between resilience and cost in strategic supply chain design and provide managerial insights for focal enterprises seeking to build more resilient supply networks under uncertainty. A limitation of this study is its reliance on numerical simulations and future research should empirically validate these mechanisms using real-world industry data.

Social manufacturing (SM) is a task-oriented production paradigm that enables coordinated scheduling across distributed manufacturing resources. However, its operational efficiency is often limited by reliance on centralized management systems. In this context, blockchain technology shows strong potential for decentralized management in SM. However; the corresponding models for early warnings of production progress, especially for task fulfillment and delay risks, remain largely unexplored. To address the limitations, this article first proposes a blockchain-based decentralized production progress early warning system in SM. Furthermore, a corresponding early warning workflow is developed to define the overall interaction logic among participants. Then, a hybrid state machine is implemented within the smart contract to construct a task fulfillment status early warning model. Afterward, a Bayesian network-based model is designed to assess the evolving delay risks of interdependent tasks. Finally, a case study on air-conditioner production is conducted to validate the proposed models. Results show that the prototype system maintains scalable performance, achieving up to 17.56 transactions/s under write-intensive workloads and 52.45 transactions/s under read-intensive workloads, demonstrating more stable scalability than traditional centralized and directed acyclic graph (DAG)-based systems.

Warehouse 4.0 adopts automation, IoT, and big data technologies to establish an intelligent warehousing system for efficient, real-time management of storage, handling, and picking. Addressing challenges like unreasonable storage allocation and inefficient order fulfillment, this paper presents an integrated framework that utilizes swarm intelligence algorithms and collaborative scheduling strategies to optimize inbound/outbound operations. First, for inbound processes, an algorithm-driven storage allocation model is proposed to solve stacker crane scheduling problems. Then, for outbound operations, a “1+N+M” mathematical model is developed, optimized through a three-stage algorithm addressing order picking and distribution scheduling. Finally, a case study of an industrial warehouse validates the proposed methods. The improved mayfly algorithm demonstrates excellent performance, achieving 64.5–74.5% faster convergence and 20.1–24.7% lower fitness values compared to traditional algorithms. The three-stage approach reduces order fulfillment time by 12% and average processing time by 1.8% versus conventional methods. These results confirm the framework’s effectiveness in enhancing warehouse operational efficiency through intelligent automation and optimized resource scheduling.

Nowadays, the focus of flow shops is the adoption of customized demand in the context of service-oriented manufacturing. Since production tasks are often characterized by multi-variety, low volume, and a short lead time, it becomes an indispensable factor to include supporting logistics in practical scheduling decisions to reflect the frequent transport of jobs between resources. Motivated by the above background, a hybrid method based on dual back propagation (BP) neural networks is proposed to meet the real-time scheduling requirements with the aim of integrating production and transport activities. First, according to different resource attributes, the hierarchical structure of a flow shop is divided into three layers, respectively: the operation task layer, the job logistics layer, and the production resource layer. Based on the process logic relationships between intra-layer and inter-layer elements, an operation task–logistics–resource supernetwork model is established. Secondly, a dual BP neural network scheduling algorithm is designed for determining an operations sequence involving the transport time. The neural network 1 is used for the initial classification of operation tasks’ priority; and the neural network 2 is used for the sorting of conflicting tasks in the same priority, which can effectively reduce the amount of computational time and dramatically accelerate the solution speed. Finally, the effectiveness of the proposed method is verified by comparing the completion time and computational time for different examples. The numerical simulation results show that with the increase in problem scale, the solution ability of the traditional method gradually deteriorates, while the dual BP neural network has a stable performance and fast computational time.

Under the background of the fourth industrial revolution driven by the new generation information technology and artificial intelligence, human–robot collaboration has become an important part of smart manufacturing. The new “human–robot–environment” relationship conducts industrial robots to collaborate with workers to adapt to environmental changes harmoniously. How to determine a reasonable human–robot interaction operations allocation strategy is the primary problem, by comprehensively considering the workers’ flexibility and industrial robots’ automation. In this paper, a human–robot collaborative operation framework based on CNC (Computer Number Control) machine tool was proposed, which divided into three stages: pre-machining, machining and post-machining. Then, an action-based granularity decomposition method was used to construct the human–robot interaction hierarchical model. Further, a collaboration effectiveness-based operations allocation function was established through normalizing the time, cost, efficiency, accuracy and complexity of human–robot interaction. Finally, a simulated annealing algorithm was adopted to solve preferable collaboration scheme; a case was used to verify the feasibility and effectiveness of the proposed method. It is expected that this study can provide useful guidance for human–robot interaction operations allocation on CNC machine tools.

One-dimensional (1D) organic–inorganic hybrid lead halides with unique core–shell quantum wire structures and splendid photoluminescence properties have been considered one of the most promising high-efficiency broadband emitters. However, studies on the broadband emissions in 1D purely face-shared lead iodide hybrids are still rare so far. Herein, we report on a new 1D lead iodide hybrid, (2cepyH)PbI₃ (2cepy = 1-(2-chloroethyl)pyrrolidine), characterized with face-sharing PbI₆ octahedral chains. Upon UV photoexcitation, this material shows broadband yellow emissions originating from the self-trapped excitons associated with distorted Pb–I lattices on account of the strong exciton–phonon coupling, as proved by variable-temperature emission spectra. Moreover, experimental and calculated results reveal that (2cepyH)PbI₃ is an indirect bandgap semiconductor, the band structures of which are governed by inorganic parts. Our work represents the first broadband emitter based on a 1D face-shared lead iodide hybrid and opens a new way to obtain the novel broadband emission materials.

In the context of service-oriented manufacturing, warehouse product service system (WPSS) has been proposed through integrating smart warehouse and ‘centralized purchase + shared storage + JIT distribution’ services for various enterprises in an Industrial Park. The influence mechanism of enterprises‘ uncertain demand on profit decision of WPSS was deduced. Firstly, a mathematical model was established by turning enterprises’ uncertain demand into service cost. Its maximum profit was taken as the object, considering the storage income, penalty income for delayed storage, and distribution cost. Then, an improved interval number optimization method with midpoint and width was adopted to optimize the possibility of maximum profit; its possibility was used to quantify the decision makers’ tolerance for uncertain demand. Finally, a case study was presented to verify the feasibility of the proposed method. It is expected that the maximum profit under enterprises’ uncertain demand can provide decision support for resources configuration and operation scheduling of WPSS.

Industry 4.0 focuses on the realization of smart manufacturing from shop floors to factories and to the whole supply chain. As a key technology of smart manufacturing, cyber-physical system has been widely discussed in the aspects of system design, data collection and processing, and cyber-physical synchronization. In a smart shop floor, manufacturing resources with intelligence and autonomy are abstracted as cyber-physical system units. They can communicate with each other autonomously to make optimal production decisions according to the real-time status of the shop floor. In this article, an autonomous collaboration network comprised of cyber-physical system–based smart manufacturing resources is modeled by using complex network theory. The collaboration activities among them are further analyzed, from which the information of key cyber-physical system units and key collaboration relationships are excavated. A demonstrative case is studied to verify the feasibility of the proposed model. From the case, it can be seen that (1) autonomous collaboration network has a small-world feature; (2) cyber-physical system units with bigger degrees and the collaborative relationships with bigger tightness are more important; (3) the workload of cyber-physical system units needs to be balanced because some cyber-physical system units have exceeded their capacities; and (4) cyber-physical system units with larger collaboration clustering coefficients will attract other nodes to form communities centered by them. Based on these results, the autonomous production control and management of smart shop floor will become more accurate, efficient, and balanced.

Facing fierce market competitions and fast technological transformation, manufacturing enterprises eagerly aspire to cut down investments in non-core businesses with low return, such as logistics, warehousing, and machine maintenance. In the context of service-oriented manufacturing (SOM), these non-core businesses are expected to be undertaken by third-party service providers in a centralized “product + service” manner. Supported by SOM principles, public warehousing product service system (PW-PSS) is defined as an integration of public warehouses and related warehousing services (e.g. raw material purchasing, work-in-progress storing/sorting/monitoring, and finished products packaging/distribution) for different manufacturing enterprises in an Industrial Park (IP). PW-PSS especially benefits for small-and medium-sized enterprises (SMEs) who are suffering from a heavy investment and management burden of self-built warehouses. To well operate the PW-PSS, third-party service providers must improve the service satisfaction degree of customers (i.e. manufacturing enterprises) to attract their continuous cooperation. This paper proposes a service satisfaction evaluation (SSE) method considering customer preferences to ensure the PW-PSS solutions more reliable and reasonable. First, the standard impact loss method is adopted to determine the initial weights of SSE indexes, and then Kano model is used to adjust the above weights based on the consideration of customer preferences. Second, according to whether an SSE index is profit-oriented or cost-oriented, a positive or negative distance is obtained based on a distance average solution method. Finally, an example is given to verify the proposed method. It is expected that SSE can provide a decision-making basis for SMEs in an IP to choose optimal PW-PSS solutions.