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The purpose of this study is to develop a novel general mathematical model to find the optimal product mix of commercial graphite products, which has a complex production process with alternative sub-processes in the graphite mining production process.

The network optimization was adopted to model the complex graphite mining production process through the optimal allocation of raw graphite, byproducts, and saleable products with comparable sub-processes, which has different processing capacities and costs. The model was tested on a selected graphite manufacturing company, and the optimal graphite product mix was determined through the selection of the optimal production process. In addition, sensitivity and scenario analyses were carried out to accommodate uncertainties and to facilitate further managerial decisions.

The selected graphite mining company mines approximately 400 metric tons of raw graphite per month to produce ten types of graphite products. According to the optimum solution obtained, the company should produce only six graphite products to maximize its total profit. In addition, the study demonstrated how to reveal optimum managerial decisions based on optimum solutions.

This study has made a significant contribution to the graphite manufacturing industry by modeling the complex graphite mining production process with a network optimization technique that has yet to be addressed at this level of detail. The sensitivity and scenario analyses support for further managerial decisions.

Hoisting is an essential construction work package, but there is still a high incidence of accidents due to insufficient attention to coping strategies. This study aims to provide decision support to practitioners on safety protocols by developing a multi-stakeholder risk response model and a novel evaluation method.

Firstly, the study summarizes the hoisting risk response strategies system through a literature review and stakeholder theory. Secondly, the study constructed a quantitative theoretical model based on GLS-SEM and questionnaires. Third, the EWM-VA evaluation method was developed to determine the value coefficients of strategies.

The strategic interaction between government and consultants, consultants and builders, and government and builders are in the top three pronounced. Three coping strategies, “Increase funding for lifting equipment and safety devices,” “Improve the quality of safety education and training on lifting construction,” and “Conduct regular emergency rescue drills for lifting accidents,” have the optimal ratio of benefits to costs.

The hoisting risk strategy model from the perspective of multi-interested subjects proposed by the study is based on the global thinking of the project, which reduces the troubles such as the difficulty of pursuing responsibility and the irrational allocation of strategies that were brought by the previously related studies that only considered a single interested subject. In addition, the EWM-VA evaluation method developed in the study also provides new options for evaluating risk strategies and has the potential to be extended to other fields.

Recently, interest in sustainability has grown globally in the heavy vehicle and equipment industry (HVEI). However, this industry's complexity poses a challenge to the implementation of generic sustainable supply chain management (SSCM) practices. This study aims to identify SSCM's barriers, practices and performance (BPP) indicators in the HVEI context.

The results are derived from case studies of four multinational manufacturers. Within-case and cross-case analyses were conducted to categorise the SSCM BPP indicators that are unique to HVEI supply chains.

This study's analysis revealed that supply chain cost implications and a deficient information flow between focal firms and supply chain partners are the key barriers to SSCM in the HVEI. This analysis also revealed a set of policies, programmes and procedures that manufacturers have adopted to address SSCM barriers. The most common SSCM performance indicators included eco-portfolio sales to assess economic performance, health and safety indicators for social sustainability and carbon- and energy-related measures for environmental sustainability.

The insights can help HVEI firms understand and overcome the typical SSCM barriers in their industry and develop, deploy and optimise their SSCM strategies and practices. Managers can use this knowledge to identify appropriate mechanisms with which to accelerate their transition into a sustainable business and effectively measure performance outcomes.

The extant SSCM literature has focused on the light vehicle industry, and it has lacked a concrete examination of HVEI supply chains' sustainability BPP. This study develops a framework that simultaneously analyses SSCM BPP in the HVEI.

The purpose of this study is to identify the causative factors of metro construction safety accidents, analyze the correlation between accidents and causative factors and assist in developing safety management strategies for improving safety performance in the context of the Chinese construction industry.

To achieve these objectives, 13 types and 48 causations were determined based on 274 construction safety accidents in China. Then, 204 cause-and-effect relationships among accidents and causations were identified based on data mining. Next, network theory was employed to develop and analyze the metro construction accident causation network (MCACN).

The topological characteristics of MCACN were obtained, it is both a small-world network and a scale-free network. Controlling critical causative factors can effectively control the occurrence of metro construction accidents. Degree centrality strategy is better than closeness centrality strategy and betweenness centrality strategy.

In practice, it is very difficult to quantitatively identify and determine the importance of different accidents and causative factors. The weights of nodes and edges are failed to be assigned when constructing MCACN.

This study provides a theoretical basis and feasible management reference for construction enterprises in China to control construction risks and reduce safety accidents. More safety resources should be allocated to control critical risks. It is recommended that safety managers implement degree centrality strategy when making safety-related decisions.

This paper establishes the MCACN model based on data mining and network theory, identifies the properties and clarifies the mechanism of metro construction accidents and causations.

Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

This study provides a comprehensive analysis of the transition from Building Information Modelling (BIM) to digital twins (DT) in the construction industry. Specifically, the research explores the current state (themes and trends) and future directions of this emerging research domain.

A multi-stage approach was employed that combines scientometric and systematic review approaches. The scientometric analysis involves quantitative assessment of scientific publications retrieved from the Web of Science database – using software tools like VOSviewer and HistCite. The systematic review involved a rigorous synthesis and evaluation of the existing literature to identify research gaps, themes, clusters and future directions. Clusters obtained from the scientometric analysis of the co-occurrence network were then used as a subject base for a systematic study.

Emergent findings reveal a rapidly growing interest in BIM-DT integration, with over 90% of publications since 2020. The United Kingdom, China and Italy are the leading contributing countries. Five prominent research clusters identified are: (1) Construction 4.0 technologies; (2) smart cities and urban environments; (3) heritage BIM and laser scanning; (4) asset and facility management; and (5) energy and sustainability. The study highlights the potential of BIM-DT integration for enhancing project delivery, asset management and sustainability practices in the built environment. Moreover, the project’s life cycle operation phase has garnered the most attention from researchers in this field compared to other phases.

This unique study is comprehensive in its approach by combining scientometric and systematic methods to provide a quantitative and qualitative evaluation of the BIM-DT research landscape. Unlike previous reviews that focused solely on facility management, this study’s scope covers the entire construction sector. By identifying research gaps, challenges and future directions, this study establishes a solid foundation for researchers exploring this emerging field and envisions the future landscape of BIM-DT integration in the built environment.

This study explores the role of the Internet of Things (IoT) as an enabler for Lean Construction principles and tools in construction projects.

In response to the scarcity of studies about IoT functionalities in construction, a two-round systematic literature review (SLR) was undertaken. The first round aimed to identify IoT functionalities in construction, encompassing an analysis of 288 studies. The second round aimed to analyze their interaction with Lean Construction principles, drawing insights from 43 studies.

The outcome is a comprehensive Lean Construction-IoT matrix featuring 54 interactions. The highest levels of interaction were found in the Lean Construction principle “flow” and the functionality of “data transfer and real-time information sharing”.

The study focuses on the role of IoT as an enabler for Lean Construction. Future work can cover the role of Lean as an enabler for advanced technology implementation in construction.

The Lean Construction-IoT matrix serves as a resource for researchers, practitioners, and decision-makers seeking to enhance Lean Construction by leveraging IoT technology. It also provides various examples of how advanced technology can support waste elimination and value generation in construction projects.