Reliability-redundancy allocation, preventive maintenance, and spare parts logistics are crucial for achieving system reliability and availability goal. Existing methods often concentrate on specific scopes of the system’s lifetime. This paper proposes a joint redundancy-maintenance-inventory allocation model that simultaneously optimizes redundant component, replacement time, spares stocking, and repair capacity. Under reliability and availability criteria, our objective is to minimize the system’s lifetime cost, including design, manufacturing, and operational phases. We develop a unified system availability model based on ten performance drivers, serving as the foundation for the establishment of the lifetime-based resource allocation model. Superimposed renewal theory is employed to estimate spare part demand from proactive and corrective replacements. A bisection algorithm, enhanced by neighborhood exploration, solves the complex mixed-integer, nonlinear optimization problem. The numerical experiments show that component redundancy is preferred and necessary if one of the following situations occurs: extremely high system availability is required, the fleet size is small, the system reliability is immature, the inventory holding is too costly, or the hands-on replacement time is prolonged. The joint allocation model also reveals that there exists no monotonic relation between spares stocking level and system availability.
In the steelmaking industry, enhancing production cost-effectiveness and operational efficiency requires the integration of intelligent systems to support production activities. Thus, effectively integrating various production modules is crucial to enable collaborative operations throughout the entire production chain, reducing management costs and complexities. This paper proposes, for the first time, the integration of Vision-Language Model (VLM) and Large Language Model (LLM) technologies in the steel manufacturing domain, creating a novel steelmaking process management system. The system facilitates data collection, analysis, visualization, and intelligent dialogue for the steelmaking process. The VLM module provides textual descriptions for slab defect detection, while LLM technology supports the analysis of production data and intelligent question-answering. The feasibility, superiority, and effectiveness of the system are demonstrated through production data and comparative experiments. The system has significantly lowered costs and enhanced operational understanding, marking a critical step toward intelligent and cost-effective management in the steelmaking domain.
The joint optimization of production, maintenance, and quality control has shown effectiveness in reducing long-term operational costs in production systems. However, existing studies often assume that changes in the mean value of product quality characteristics in a deteriorating system follow a specific distribution while keeping variance constant. To address this limitation, we propose an innovative method based on the continuous ranking probability score (CRPS). This method enables the simultaneous detection of changes in mean and variance in nonconformities, thus removing the assumption of a specific distribution for quality characteristics. Our approach focuses on developing optimal strategies for production, maintenance, and quality control to minimize cost per unit of time. Additionally, we employ a stochastic model to optimize the production time allocated to the inventory buffer, resulting in significant cost reductions. The effectiveness of our proposed joint optimization method is demonstrated through comprehensive numerical experiments, sensitivity analysis, and a comparative study. The results show that our method can achieve cost reductions compared to several other related methods, highlighting its practical applicability for manufacturing companies aiming to reduce costs.
There is notable variability in carbon emission reduction efforts across different provinces in China, underscoring the need for effective strategies to implement carbon emission allowance auctions. These auctions, as opposed to free allocations, could be more aligned with the principle of “polluter pays.” Focusing on three diverse regions — Ningxia, Beijing, and Zhejiang — this study employs a system dynamics simulation model to explore markets for carbon emissions and green certificates trading. The aim is to determine the optimal timing and appropriate policy intensities for auction introduction. Key findings include: (1) Optimal auction strategies differ among the provinces, recommending immediate implementation in Beijing, followed by Ningxia and Zhejiang. (2) In Ningxia, there’s a potential for a 6.20% increase in GDP alongside a 21.59% reduction in carbon emissions, suggesting a feasible harmony between environmental and economic objectives. (3) Market-related policy variables, such as total carbon allowances and Renewable Portfolio Standards, significantly influence the optimal auction strategies but have minimal effect on carbon auction prices.
Power grids play a crucial role in connecting electricity suppliers and consumers. They facilitate efficient power transmission and energy management, significantly contributing to the transition toward low-carbon practices across both upstream and downstream sectors. Effectively managing carbon reduction in the power industry is essential for enhancing carbon reduction efficiency and achieving dual-carbon goals. Recent studies have focused on the outcomes of carbon reduction efforts rather than the management process. However, when power grids prioritize the process of carbon reduction in their management, they are more likely to achieve better results. To address this gap, we propose an evaluation model for managing carbon reduction activities in power grids, comprising the carbon management efficiency (CME) module based on the maturity model and the carbon reduction efficiency (CRE) module based on the entropy method. The CME module provides a scorecard corresponding to a detailed and continuous evaluation model for carbon management processes to calculate its performance. Simultaneously, the CRE module relates carbon reduction results to the development direction of the government and power grid, allowing for effective adjustments and updates based on actual situations. The evaluation model was applied to provincial power grids within the China Central Power Grid. The results reveal that despite some fluctuations in carbon reduction performance, provincial power grids within the China Central Power Grid have made continuous progress in carbon reduction efforts. According to the synergy model, there is evidence suggesting that power grids are steadily improving their carbon reduction performance, and a more organized approach would lead to a greater degree of synergy. The evaluation model applies to power grids, and its framework can be extended to other industries, providing a theoretical reference for evaluating their carbon reduction efforts.
The development of shale oil is of considerable strategic importance, particularly concerning national security implications. Effective management is vital to maximize both efficiency and socio-economic benefits. This process necessitates addressing four critical relationships: balancing local and global factors, reconciling universality with particularity, integrating inheritance with innovation, and resolving primary and secondary contradictions. These relationships pose several management challenges that must be overcome to develop a robust management model for shale oil extraction. This paper uses the Gulong shale oil in the Daqing oilfield as a case study to examine the implications and specific manifestations of these relationships. To address the limitations of traditional management models, which often overly emphasize local factors, particularity, innovation, and secondary contradictions, we have developed the “Integrated Dialectical Four-Domain Coupling Management Model.” This model incorporates systems engineering theory into management strategies. Key strategies include the global deployment of experimental zone construction, systematic geological and engineering integration, combining historical practices with innovative approaches, phase analysis, and contradiction coordination. These strategies have significantly advanced the development of Gulong shale oil, demonstrating positive on-site results. The innovative management processes detailed in this paper provide valuable insights applicable to similar reservoirs and other large-scale engineering management projects.
The integration of Blockchain Technology (BT) with Digital Twins (DTs) is becoming increasingly recognized as an effective strategy to enhance trust, interoperability, and data privacy in virtual spaces such as the metaverse. Although there is a significant body of research at the intersection of BT and DTs, a thorough review of the field has not yet been conducted. This study performs a systematic literature review on BT and DTs, using the CiteSpace analytic tool to evaluate the content and bibliometric information. The review covers 976 publications, identifying the significant effects of BT on DTs and the integration challenges. Key themes emerging from keyword analysis include augmented reality, smart cities, smart manufacturing, cybersecurity, lifecycle management, Ethereum, smart grids, additive manufacturing, blockchain technology, and digitalization. Based on this analysis, the study proposes a development framework for BT-enhanced DTs that includes supporting technologies and applications, main applications, advantages and functionalities, primary contexts of application, and overarching goals and principles. Additionally, an examination of bibliometric data reveals three developmental phases in cross-sectional research on BT and DTs: technology development, technology use, and technology deployment. These phases highlight the research field’s evolution and provide valuable direction for future studies on BT-enhanced DTs.
In recent decades, interest in project-based learning within organizational learning has grown significantly. This study synthesizes principles that facilitate learning at the project level. Through a cross-case analysis of the Gaasperdammer Tunnel project in the Netherlands and the Hong Kong-Zhuhai-Macao Bridge in China, and validation via focus group discussions, we have identified five key principles: Owner Commitment, Social Environment Approach, Collaboration Vision, Value Orientation, and Open Mindset. These principles highlight the mindsets that guide the behavior and thinking of project practitioners beyond prescriptive processes and routines. Our research enhances the understanding of how project participants can learn from their involvement in unique, complex projects and improve their capabilities for future endeavors. We emphasize the critical role of learning in the development of project capabilities and suggest it be a focal point in future research on infrastructure development projects.
Roadmapping is a well-established technique in the context of innovation and strategy, with the potential to support organizations address the complex transformative challenges facing humanity in the 21st century. This is enabled by its systems-based architecture and visual form of roadmaps, supporting communication and reduction of information asymmetries in complex sociotechnical systems. This paper focuses on an adaptation of the roadmapping method to support strategic planning for roadmapping systems in organizations, addressing implementation challenges. This represents a novel application of roadmapping to business processes and systems, demonstrating the flexibility of the roadmapping approach. A workshop template (‘R2’) and process for supporting the roadmapping of roadmapping systems is presented, developed and refined through a series of six industrial cases, and illustrated with an application example in the additive manufacturing sector.
Understanding the influencing factors and the evolving trends of the Water-Sediment Regulation System (WSRS) is vital for the protection and management of the Yellow River. Past studies on WSRS have been limited in focus and have not fully addressed the complete engineering control system of the basin. This study takes a holistic view, treating sediment management in the Yellow River as a dynamic and ever-evolving complex system. It merges concepts from system science, information theory, and dissipative structure with practical efforts in sediment engineering control. The key findings of this study are as follows: between 1990 and 2019, the average Yellow River Sediment Regulation Index (YSRI) was 55.99, with the lowest being 50.26 in 1990 and the highest being 61.48 in 2019; the result indicates that the WSRS activity decreased, yet it fluctuated, gradually approaching the critical threshold of a dissipative structure.
Numerous maintenance strategies have been proposed in the literature related to reliability. This paper focuses on the utilization of reliability importance measures to optimize maintenance strategies. We analyze maintenance strategies based on importance measures and identify areas lacking sufficient research. The paper presents principles and formulas for advanced importance measures within the context of optimizing maintenance strategies. Additionally, it classifies methods of maintenance strategy optimization according to importance measures and outlines the roles of these measures in various maintenance strategies. Finally, it discusses potential challenges that optimization of maintenance strategies based on importance measures may encounter with future technologies.
This paper examines sustainable supply strategies for essential and strategic resources in China, addressing both domestic requirements and global supply uncertainties. In the context of intense global competition for resources and substantial internal demand, China’s significant role as a major consumer and global supplier is pivotal in the dynamics of the global supply chain. This study highlights China’s dependence on imports for essential resources and the critical need for resilient supply chains to enhance national security and promote environmental sustainability. By referencing international experiences and accounting for China’s specific circumstances, this study proposes strategic initiatives, including updating the strategic resource catalog, imposing export controls on key minerals, promoting resource conservation, and enhancing global cooperation. These strategies aim to reduce external dependencies and support global resource sustainability. The proposed framework can help policymakers ensure long-term resource security and manage resources more effectively in complex global landscapes.
