Safety is one of the most critical themes in any large-scale railway construction project. Recognizing the importance of safety in railway engineering, practitioners and researchers have proposed various standards and procedures to ensure safety in construction activities. In this study, we first review four critical research areas of risk warning technologies and emergency response mechanisms in railway construction, namely, (i) risk identification methods of large-scale railway construction projects, (ii) risk management of large-scale railway construction, (iii) emergency response planning and management, and (iv) emergency response and rescue mechanisms. After reviewing the existing studies, we present four corresponding research areas and recommendations on the Sichuan–Tibet Railway construction. This study aims to inject new significant theoretical elements into the decision-making process and construction of this railway project in China.

The low carbon energy transition has attracted worldwide attention to mitigate climate change. Renewable energy (RE) is the key to this transition, with significant developments to date, especially in China. This study systematically reviews the literature on RE development to identify a general context from many studies. The goal is to clarify key questions related to RE development from the current academic community. We first identify the forces driving RE development. Thereafter, we analyze methods for modeling RE developments considering the systematic and multiple complexity characteristics of RE. The study concludes with insights into the target selection and RE development roadmap in China.

The mechanism of risk allocation is designed to protect all stakeholders, and it is vital to project success. Qualitative and quantitative ways of optimizing risk allocation have been well documented in extant literature (e.g., allocation principles, models, and solutions), and the foci of existing research are usually the maximization of rational utility. Few research has focused on partners’ social preferences affecting the output of risk allocation. This study presents a quantitative approach based on modeling alliance member (AM)’s inequity aversion (IA) to analyze risk-sharing arrangements in an alliance project. Fehr and Schmidt’s inequity-aversion model is integrated into modeling partner’s utility. This paper derives results for an alliance leader (AL)’s optimal risk-sharing ratio and AM’s optimal risk-management effort simultaneously. The derivation is based on solving a restrained optimization problem using the conception and methods from Stackelberg game theory. Results show that an AM’s IA significantly affects risk allocation between AL and AM. Specifically, envious preference is positively related to AL’s optimal risk-sharing ratio, whereas guilty preference negatively affects AL’s optimal risk-sharing ratio. These findings will be of interest to academics and practitioners involved in designing alliance negotiations.

Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries. Motivated by the major development strategies and needs of industrial intellectualization in China, this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization, as well as their application to smart industrial engineering. First, this study describes a general methodology for the fusion of data analytics and optimization. Then, it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing. Finally, it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization. The framework uses data analytics to perceive and analyze industrial production and logistics processes. It also demonstrates the intelligent capability of planning, scheduling, operation optimization, and optimal control. Data analytics and system optimization tech-nologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing, resources and materials, energy, and logistics systems, such as high energy consumption, high costs, low energy efficiency, low resource utilization, and serious environmental pollution. The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency. Therefore, industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.

The field of engineering management usually involves evaluation issues, such as program selection, team performance evaluation, technology selection, and supplier evaluation. The traditional self-evaluation data envelopment analysis (DEA) method usually exaggerates the effects of several inputs or outputs of the evaluated decision-making unit (DMU), resulting in unrealistic results. To address this problem, scholars have proposed the cross-efficiency evaluation (CREE) method. Compared with the DEA method, CREE can rank DMUs more completely by using reasonable weights. With the extensive application of this technique, several problems, such as non-unique weights and non-Pareto optimal results, have arisen in CREE methods. Therefore, the improvement of CREE has attracted the attention of many scholars. This paper reviews the theory and applications of CREE, including the non-uniqueness problem, the aggregation of cross-efficiency data, and applications in engineering management. It also discusses the directions for future research on CREE.

The global collaboration and integration of online and offline channels have brought new challenges to the logistics industry. Thus, smart logistics has become a promising solution for handling the increasing complexity and volume of logistics operations. Technologies, such as the Internet of Things, information communication technology, and artificial intelligence, enable more efficient functions into logistics operations. However, they also change the narrative of logistics management. Scholars in the areas of engineering, logistics, transportation, and management are attracted by this revolution. Operations management research on smart logistics mainly concerns the application of underlying technologies, business logic, operation framework, related management system, and optimization problems under specific scenarios. To explore these studies, the related literature has been systematically reviewed in this work. On the basis of the research gaps and the needs of industrial practices, future research directions in this field are also proposed.

Train speed profile optimization is an efficient approach to reducing energy consumption in urban rail transit systems. Different from most existing studies that assume deterministic parameters as model inputs, this paper proposes a robust energy-efficient train speed profile optimization approach by considering the uncertainty of train modeling parameters. Specifically, we first construct a scenario-based position–time–speed (PTS) network by considering resistance parameters as discrete scenario-based random variables. Then, a percentile reliability model is proposed to generate a robust train speed profile, by which the scenario-based energy consumption is less than the model objective value at \alpha confidence level. To solve the model efficiently, we present several algorithms to eliminate the infeasible nodes and arcs in the PTS network and propose a model reformulation strategy to transform the original model into an equivalent linear programming model. Lastly, on the basis of our field test data collected in Beijing metro Yizhuang line, a series of experiments are conducted to verify the effectiveness of the model and analyze the influences of parameter uncertainties on the generated train speed profile.

Due to uncertainties in water supply, there is growing demand for water resource management in enterprises. In this study, we evaluated the effects of companies’ water-saving reconstruction projects. We used Hina Advanced Materials Company as a case to construct an investment decision model to (1) calculate the internal and external costs of water resources based on circular economic value analysis theory, and (2) locate the level of water resources circulation. We adopted gray situation decision analysis to identify the typical problems that occur in water resource utilization. Moreover, we demonstrated optimization plans for different potential improvements, thereby providing guidance and references for water resource cost management and the comprehensive optimization of environmental benefits. We concluded that the circulation economic value analysis model can effectively display the flow and amount of value derived from water resource flows, thereby providing guidance and suggestions for optimizing water resource flows.

This study provides a systematic overview of the advent and evolution of reliability systems engineering (RSE) in China, and the latest RSE development, that is, model-based RSE (MBRSE), is emphatically introduced. The establishment of the system architecture and conceptual models of MBRSE is first described. The fundamental theory and methodology of MBRSE are then elaborated, with a V-model as the core of this approach. The development of various MBRSE platforms and the effectiveness of their implementation over the past 30 years are presented. The prospective trends in the development of RSE in China are outlined.

Carbon emission reduction is the only way to alleviate environmental problems, such as global warming. Effective evaluation of carbon performance can help enterprises to carry out energy saving and emission reduction activities to a certain extent and promote sustainable development. This paper constructs a carbon performance evaluation index system that includes the four dimensions of carbon resource (energy) input, cycle, output, and carbon management by incorporating the principles of circular economy and the theory of resource value circulation from the perspective of the flow trajectory of carbon-containing resources in the circulation of enterprises combined with the production characteristics of thermoelectric enterprises. Subsequently, combined with the case study, this paper discusses the scientific and practical nature of the system and provides another way of thinking for carbon performance evaluation of micro-enterprises in other industries. This paper expands the application boundary of matter–element model and supplements the literature of carbon performance, which has certain theoretical and practical significance.

The Cyber–Physical Power System (CPPS) is one of the most critical infrastructure systems in a country because a stable and secure power supply is a key foundation for national and social development. In recent years, resilience has become a major topic in preventing and mitigating the risks caused by large-scale blackouts of CPPSs. Accordingly, the concept and significance of CPPS resilience are at first explained from the engineering perspective in this study. Then, a review of representative quantitative assessment measures of CPPS resilience applied in the existing literature is provided. On the basis of these assessment measures, the optimization methods of CPPS resilience are reviewed from three perspectives, which are mainly focused on the current research, namely, optimizing the recovery sequence of components, identifying and protecting critical nodes, and enhancing the coupling patterns between physical and cyber networks. The recent advances in modeling methods for cascading failures within the CPPS, which is the theoretical foundation for the resilience assessment and optimization research of CPPSs, are also presented. Lastly, the challenges and future research directions for resilience optimizing of CPPSs are discussed.

Today’s supply chain is becoming complex and fragile. Hence, supply chain managers need to create and unlock the value of the smart supply chain. A smart supply chain requires connectivity, visibility, and agility, and it needs be integrated and intelligent. The digital twin (DT) concept satisfies these requirements. Therefore, we propose creating a DT-driven supply chain (DTSC) as an innovative and integrated solution for the smart supply chain. We provide background information to explain the DT concept and to demonstrate the method for building a DTSC by using the DT concept. We discuss three research opportunities in building a DTSC, including supply chain modeling, real-time supply chain optimization, and data usage in supply chain collaboration. Finally, we highlight a motivating case from JD.COM, China’s largest retailer by revenue, in applying the DTSC platform to address supply chain network reconfiguration challenges during the COVID-19 pandemic.

Energy development concerns not only the development of renewable energies but also the shift from centralised to clean, decentralised power generation. The development of decentralised energy (DE) is a core part of the energy and economic strategies being adopted around the world that drives the progress toward a highly sustainable future. This paper reviews the concepts, development status, trends, benefits and challenges of DE systems and analyses the existing models and methods for assessing the performance of these systems. A hierarchical decision model for evaluating the performance of DE systems is also constructed based on the framework of multiple criteria decision analysis, which considers the identification, definition and assessment grade of decision criteria. The evidential reasoning approach is applied to aggregate assessment information in a case study of the implementation of an intelligent decision system. Sensitivity and trade-off analyses are also conducted to show how the proposed model can be used to support decision making in DE systems.

Late payment, and indeed no payment, is a rampant and chronic problem that has plagued the global construction industry for too long. Recent development in blockchain technology, particularly its smart contract, seems to provide a new opportunity to improve this old problem. However, this opportunity is largely unexploited. This study aims to develop a blockchain-based smart contract (BBSC) system for smart payment in the construction industry by focusing on the fundamental cycle of payment freezing (sometimes also synonymously called payment guarantees) and disbursement application. Firstly, a BBSC framework, containing three processes of (a) initiation and configuration, (b) payment freezing, and (c) disbursement application, is developed. Next, based on the framework, the system architecture of the BBSC system, containing three layers of (1) Infrastructure as a Service (IaaS), (2) Blockchain as a Service (BaaS), and (3) Software as a Service (SaaS) is proposed and elabora-ted. Finally, based on the system architecture, a BBSC prototype system is developed using a real-life modular construction project as a case study. It was found that the prototype system can improve the certainty and efficiency of the progress payment, thereby enabling smart payment in construction transactions. Without advocating radical changes (e.g., the contractual relationships or the intermediate role of banks in modern construction projects), the prototype can be developed into a real-life BBSC system that can work compatibly with current advancements in the field. Future works are recommended to fine-tune the findings and translate and implement them in real-life applications.

This multiple case study of a contracting firm contributes to understanding the barriers that organizations face during the implementation of building information modeling (BIM) by providing insights into the impact of these barriers across different organizational levels (i.e., from top management to project teams) and by relating these barriers to different degrees of BIM maturity. First, we observe the dominance of barriers related to the motivation, competence, and time capacity of people across all levels of an organization. Second, the cluster of barriers at the middle-management level highlights the important role of this level in reducing these barriers. Third, only those cases with a low level of BIM maturity have struggled with lack of top management support, thereby highlighting the importance of such support in achieving BIM maturity growth. High BIM maturity situations are more prone to externally oriented barriers in attempting to further leverage the benefits of BIM. Our study provides insights on where to focus BIM implementation measures and how to enhance organizational BIM maturity.

E-commerce, new retail, and other changes have highlighted the requirement of high efficiency and accuracy in the logistics service. As an important section in logistics and supply chain management, warehouses need to respond positively to the increasing requirement. The “smart warehouse” system, which is equipped with emerging warehousing technologies, is increasingly attracting the attention of industry and technology giants as an efficient solution for the future of warehouse development. This study provides a holistic view of operations management problems within the context of smart warehouses. We provide a framework to review smart warehouse operations management based on the characteristics of smart warehouses, including the perspectives of information interconnection, equipment automation, process integration, and environmental sustainability. A comprehensive review of relevant literature is then carried out based on the framework with four perspectives. This study could provide future research directions on smart warehouses for academia and industry practitioners.

A smart society is an advanced form of society following agricultural society, industrial society, and information society, with digital data processing system as its main carrier. However, the governance of a smart society still faces many challenges. In view of this problem, first, this research constructs a smart society governance modernization strategy. Second, the innovation mode of a society governance mechanism driven by digital technology is proposed, including the precise intellectual control of a digital twin, the intelligent ubiquitous sensing of the Internet of Things, the empowerment remodeling of a blockchain and the livelihood service of artificial intelligence. Third, this study systematically explores the practice of smart society governance modernization from the aspects of basic information platform construction, evaluation system construction, application demonstration of epidemic prevention and control driven by big data, support of spatial intelligence and artificial intelligence technology for people’s livelihood, smart campus, public resources, and data governance application demonstration to provide theoretical guidance for promoting digital technology innovation in the process of the governance of a smart society.

Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.

System health management, which aims to ensure the safe and efficient operation of systems by reducing uncertain risks and cascading failures during their lifetime, is proposed for complex transportation systems and other critical infrastructures, especially under the background of the New Infrastructure Projects launched in China. Previous studies proposed numerous approaches to evaluate or improve traffic reliability or efficiency. Nevertheless, most existing studies neglected the core failure mechanism (i.e., spatio–temporal propagation of traffic congestion). In this article, we review existing studies on traffic reliability management and propose a health management framework covering the entire traffic congestion lifetime, from emergence, evolution to dissipation, based on the study of core failure modes with percolation theory. Aiming to be “reliable, invulnerable, resilient, potential, and active”, our proposed traffic health management framework includes modeling, evaluation, diagnosis, and improvement. Our proposed framework may shed light on traffic management for megacities and urban agglomerations around the world. This new approach may offer innovative insights for systems science and engineering in future intelligent infrastructure management.

With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads, how to maintain the stable, reliable, and efficient operation of the power system has become a challenging issue requiring investigation. One of the feasible solutions is deploying the energy storage system (ESS) to integrate with the energy system to stabilize it. However, considering the costs and the input/output characteristics of ESS, both the initial configuration process and the actual operation process require efficient management. This study presents a comprehensive review of managing ESS from the perspectives of planning, operation, and business model. First of all, in terms of planning and configuration, it is investigated from capacity planning, location planning, as well as capacity and location combined planning. This process is generally the first step in deploying ESS. Then, it explores operation management of ESS from the perspectives of state assessment and operation optimization. The so-called state assessment refers to the assessment of three aspects: The state of charge (SOC), the state of health (SOH), and the remaining useful life (RUL). The operation optimization includes ESS operation strategy optimization and joint operation optimization. Finally, it discusses the business models of ESS. Traditional business models involve ancillary services and load transfer, while emerging business models include electric vehicle (EV) as energy storage and shared energy storage.

Given the complexity of power grids, the failure of any component may cause large-scale economic losses. Consequently, the quick recovery of power grids after disasters has become a new research direction. Considering the severity of power grid disasters, an improved power grid resilience measure and its corresponding importance measures are proposed. The recovery priority of failed components after a disaster is determined according to the influence of the failed components on the power grid resilience. Finally, based on the data from the 2019 Power Yearbook of each city in Shandong Province, China, the power grid resilience after a disaster is analyzed for two situations, namely, partial components failure and failure of all components. Result shows that the recovery priorities of components with different importance measures vary. The resilience evaluations under different repair conditions prove the feasibility of the proposed method.

This study combines multi-regional input–output (MRIO) model with linear programming (LP) model to explore economic structure adjustment strategies for the reduction of carbon dioxide (CO₂) emissions. A particular feature of this study is the identification of the optimal regulation sequence of final products in various regions to reduce CO₂ emissions with the minimum loss in gross domestic product (GDP). By using China’s MRIO tables 2017 with 28 regions and 42 economic sectors, results show that reduction in final demand leads to simultaneous reductions in GDP and CO₂ emissions. Nevertheless, certain demand side regulation strategy can be adopted to lower CO₂ emissions at the smallest loss of economic growth. Several key final products, such as metallurgy, nonmetal, metal, and chemical products, should first be regulated to reduce CO₂ emissions at the minimum loss in GDP. Most of these key products concentrate in the coastal developed regions in China. The proposed MRIO–LP model considers the inter-relationship among various sectors and regions, and can aid policy makers in designing effective policy for industrial structure adjustment at the regional level to achieve the national environmental and economic targets.

A project is a specific effort to create a unique product, so it is a favorable place for knowledge creation and development. Knowledge can be transferred inside and outside projects and their parent project-based organizations, thus affecting project performance and organizational competitiveness. However, the current research on the elements and outcomes of knowledge transfer (KT) in the project environment lacks completeness and clarity, and that on the different levels of KT is fragmented. This study aims to conduct comprehensive research to determine and link the elements and outcomes of KT in the project environment. The authors systematically analyzed the relevant literature from 2000 to 2021, which showed an increasing publication trend. They divided KT in the project environment into three levels according to the transfer scenario: Intra-project, cross-project, and cross-organizational KT. Five-dimensional transfer elements and two-dimensional transfer outcomes were then identified and analyzed from previous literature. Lastly, the relationships between the transfer elements and outcomes were gathered to create a comprehensive model. Importantly, the knowledge gap in the current literature was highlighted, and future research directions were put forward. This study builds a theoretical framework linking transfer elements to outcomes that can serve as a basis for scholars and practitioners to develop effective strategies for KT in the project environment.

Promoting the growth of the lithium battery sector has been a critical aspect of China’s energy policy in terms of achieving carbon neutrality. However, despite significant support on research and development (R&D) investments that have resulted in increasing size, the sector seems to be falling behind in technological areas. To guide future policies and understand proper ways of promoting R&D efficiency, we looked into the lithium battery industry of China. Specifically, data envelopment analysis (DEA) was used as the primary approach based on evidence from 22 listed lithium battery enterprises. The performance of the five leading players was compared with that of the industry as a whole. Results revealed little indication of a meaningful improvement in R&D efficiency throughout our sample from 2010 to 2019. However, during this period, a significant increase in R&D expenditure was witnessed. This finding was supported, as the results showed that the average technical efficiency of the 22 enterprises was 0.442, whereas the average pure technical efficiency was at 0.503, thus suggesting that they were suffering from decreasing returns to scale (DRS). In contrast, the performance of the five leading players seemed superior because their average efficiency scores were higher than the industry’s average. Moreover, they were experiencing increasing scale efficiency (IRS). We draw on these findings to suggest to policymakers that supporting technologically intensive sectors should be more than simply increasing investment scale; rather, it should also encompass assisting businesses in developing efficient managerial processes for R&D.

The purpose of this paper is to delineate the research challenges of human–machine collaboration in risky decision-making. Technological advances in machine intelligence have enabled a growing number of applications in human–machine collaborative decision-making. Therefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilities. In risky decision-making, a human decision-maker is vulnerable to cognitive biases when judging the possible outcomes of a risky event, whereas a machine decision-maker cannot handle new and dynamic contexts with incomplete information well. We first summarize features of risky decision-making and possible biases of human decision-makers therein. Then, we argue the necessity and urgency of advancing human–machine collaboration in risky decision-making. Afterward, we review the literature on human–machine collaboration in a general decision context, from the perspectives of human–machine organization, relationship, and collaboration. Lastly, we propose challenges of enhancing human–machine communication and teamwork in risky decision-making, followed by future research avenues.

Wind and solar powers will gradually become dominant energies toward carbon neutrality. Large-scale renewable energies, with strong stochasticity, high volatility, and unadjustable features, have great impacts on the safe operation of power system. Thus, an advanced hydropower energy system serving multiple energies is required to respond to volatility, with expanding role from a “stable energy supplier” to a “flexible efficiency regulator”. Future research and application can be considered from three aspects: 1) system expansion (e.g., the construction of large-scale hydropower/renewable energy bases in China, the construction of transnational hydropower energy internet, and the functional transformation of traditional hydropower reservoirs and generating units); 2) efficiency promotion (e.g., advanced intelligent forecasting, multi-objective operation, and risk management methods); and 3) supporting measures (e.g., market reform, benefit compensation and policy mechanism, technical standards, and laws and regulations).

The diffusion of new energy vehicles (NEVs), such as battery electric vehicles (BEVs) and fuel cell vehicles (FCVs), is critical to the transportation sector’s deep decarbonization. The cost of energy chains is an important factor in the diffusion of NEVs. Although researchers have addressed the technological learning effect of NEVs and the life cycle emissions associated with the diffusion of NEVs, little work has been conducted to analyze the life cycle costs of different energy chains associated with different NEVs in consideration of technological learning potential. Thus, relevant information on investment remains insufficient to promote the deployment of NEVs. This study proposes a systematic framework that includes various (competing or coordinated) energy chains of NEVs formed with different technologies of power generation and transmission, hydrogen production and transportation, power-to-liquid fuel, and fuel transportation. The levelized costs of three typical carbon-neutral energy chains are investigated using the life cycle cost model and considering the technological learning effect. Results show that the current well-to-pump levelized costs of the energy chains in China for BEVs, FCVs, and internal combustion engine vehicles (ICEVs) are approximately 3.60, 4.31, and 2.21 yuan/GJ, respectively, and the well-to-wheel levelized costs are 4.50, 6.15, and 7.51 yuan/GJ, respectively. These costs primarily include raw material costs, and they vary greatly for BEVs and FCVs from resource and consumer costs. In consideration of the technological learning effect, the energy chains’ well-to-wheel levelized costs are expected to decrease by 24.82% for BEVs, 27.12% for FCVs, and 19.25% for ICEVs by 2060. This work also summarizes policy recommendations on developing energy chains to promote the diffusion of NEVs in China.