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    Artificial intelligence for production, operations and logistics management in modular construction industry: A systematic literature review
    (Elsevier B.V., 2024) Liu, Q.; Ma, Y.; Chen, L.; Pedrycz, W.; Skibniewski, M.J.; Chen, Z.-S.
    Artificial intelligence (AI) has garnered significant attention within the modular construction industry, emerging as a prominent frontier development trend. A comprehensive and systematic analysis is required to gain a thorough understanding of the existing literature on the use of AI in the management of production, operations, and logistics within the modular construction industry. This review delves into the various aspects of AI implementation in this sector, adopting a critical perspective. The objective of this paper is to analyze the progress, suitability, and research patterns in the field of AI for the management of productions, operations, and logistics within the modular construction industry. First, a concise overview of AI technologies pertaining to the contemporary research on the production, operations and logistics management of the modular construction industry is provided. Second, a bibliometric analysis is performed to provide a comprehensive overview of the existing publications pertaining to this subject matter. Subsequently, this paper presents literature reviews and outlines future directions for each component, specifically AI in the context of production management, operations management, and logistics management within the modular construction industry. The review provides a valuable knowledge base and roadmap to guide future research and development efforts in AI-enhanced modular construction management. © 2024 Elsevier B.V.
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    Construction metaverse: Application framework and adoption barriers
    (Elsevier B.V., 2024) Chen, Z.-S.; Chen, J.-Y.; Chen, Y.-H.; Pedrycz, W.
    This paper addresses the limited research on the metaverse's application in the construction industry. It aims to investigate how the metaverse can empower construction, identify adoption barriers, and determine the most significant barriers. We propose a novel application framework of construction metaverse based on cyber-physical-social systems, identify 17 barriers using the political-economic-social-technological framework, and employ an expert survey and bi-objective optimization to rank the barriers. Results indicate that scalability, lack of policy incentives, and immature business models are the most critical barriers. The findings provide valuable insights for researchers, practitioners, and policymakers in the construction industry, helping to allocate resources effectively and drive metaverse development. The study's importance lies in its potential to guide successful metaverse integration in construction, leading to improved efficiency and innovation. This research inspires future work on specific metaverse applications in construction and interdisciplinary research to understand and overcome the identified barriers. © 2024 Elsevier B.V.
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    Towards a collective opinion generation approach with multiple objectives for evaluating rail transit station accessibility in urban areas
    (Elsevier B.V., 2024) Chen, Z.-S.; Wang, Y.; Chen, Y.-H.; Mardani, A.; Pedrycz, W.; Martínez, L.
    Urban rail transit can alleviate traffic congestion if sufficient ridership is achieved. The accessibility to rail transit stations largely determines public willingness to utilize urban rail transit. The current studies on the accessibility to rail transit stations tend to concentrate solely on individual rail transit lines or stations, neglecting the impact of the external macro-environment. Consequently, the outcomes of these measurements cannot accurately reflect the overall accessibility level of the region, nor do they furnish decision-makers with references for devising plans for rail transit development. This study introduces a novel approach for evaluating the accessibility to urban rail transit stations from a macro-level standpoint, utilizing expert knowledge. The present study conducts an analysis of the impact of political, economic, social, and technological factors on the accessibility to rail transit stations in urban areas. Subsequently, a comprehensive evaluation indicator system is developed based on the aforementioned analysis. Then, experts are summoned to provide their subjective evaluations for every indicator, which are depicted as probability distribution functions. The collective evaluation is derived through the aggregation of individual evaluations utilizing a bi-objective optimization approach that factors in both the collective fairness utility and the confidence level. Finally, the quantile average method is employed to consolidate the collective assessment outcomes of individual indicators, thereby deriving the level of accessibility to rail transit stations in urban areas. We design a small-scale application experiment and attempt to evaluate the accessibility to rail transit stations in Wuchang District, Wuhan with the proposed approach in order to demonstrate the feasibility of the approach. © 2024 The Author(s)