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Chigangacha, S, Henjewele, C, Ibrahim, H A and Bui, N (2025) AI-Powered Auto-Scheduling of Construction Projects for Improved Efficiency. In: Thomson, C (Ed.) and Neilson, C J (Ed.), Proceedings 41st Annual ARCOM Conference, 1-3 September 2025, Abertay University, Dundee, UK. Association of Researchers in Construction Management, 89-98.

  • Type: Conference Proceedings
  • Keywords: algorithm; artificial intelligence; auto-scheduling; construction project management; systematic literature review; technology integration
  • ISBN/ISSN: 978-0-9955463-9-4
  • URL: http://www.arcom.ac.uk/-docs/proceedings/2a2ab3abc53e7dca18a95b2e2d13509b.pdf
  • Abstract:
    This systematic literature review explores the integration of artificial intelligence (AI) with auto-scheduling in construction projects. (The gap). The study assesses the current practice of AI applications in scheduling, emphasising their potential to enhance decision-making and efficiency. By analysing 31 carefully selected relevant papers, we identify key trends, opportunities, challenges and requirements associated with these technologies. This semi-systematic literature review identified nine key technologies mapped across four primary functions: (1) resource allocation, (2) schedule optimisation, (3) schedule development and (4) decision making. This illustrates AI’s effectiveness in scheduling. Our findings indicate that AI can significantly improve scheduling accuracy, reduce project delays, and optimise resource allocation. However, challenges categorised into technical, organisational, financial, security, and skills-related barriers, include issues with data quality, difficulties in system integration, resistance to stakeholder acceptance, financial limitations, and a lack of skilled professionals must be resolved. This is the first paper to map the nine key technologies to enable AI applications in construction scheduling, laying the groundwork for future research and implementation strategies.