Mobile factories promise an increased project efficiency with on-demand production and Just-in-Time delivery of prefabricated elements. However, traditional scheduling methods predominantly focus on either factory or site and neglect the factory mobility, often leading to suboptimal synchronization. To address this gap, this paper introduces a novel reinforcement learning (RL)-based model for optimizing the operational policy of mobile factories in infrastructure projects. The developed model simultaneously schedules on-site and off-site operations, effectively integrating the performance metrics at the project level. Utilizing RL, the factory's production management system continuously learns and adjusts in response to real-time project developments, ensuring optimal decision-making regarding scheduling and resource allocation.@en

More widespread use of industrialized construction (IC) is hampered by the high capital cost of advanced production facilities paired with low profit margins. A novel service-oriented cloud manufacturing (CMfg) model could in theory increase utilization and profitability of distributed production facilities. However, little research has investigated how IC can benefit from the CMfg model. This paper examines opportunities and challenges of applying CMfg for IC. First, an adapted model of CMfg for construction is developed based on a literature review. Second, four possible scenarios for applying this adapted CMfg model are designed. Finally, an evaluation is performed through a survey among 25 practitioners and 12 in-depth interviews with industry experts. The paper assesses the desirability and categorizes the benefits and barriers of such a CMfg platform for IC. The results suggest that CMfg could enhance the design quality, support IC suitability assessment for project developers and lower financial risks for off-site manufacturers.@en

A digital twin (DT) can enhance construction management with comprehensive real-time simulations. However, research rarely considers prefabrication factories, whose processes have a significant impact on cost and duration. It remains unclear how construction DTs can achieve their expected benefits without dynamically interacting with the DTs of manufacturing facilities. To address this, a DT integration model is proposed. It builds upon systems theory and describes integration across the three layers objectives, processes, and data & tools. A theoretical example demonstrates potential benefits of integrated DTs. This work can assist researchers and practitioners who are focusing on DTs in the execution phase.@en

Construction management courses are increasingly teaching digital technologies and automation. The default method to teach construction scheduling remains the critical path method, which suffers from lack of automation, lack of dynamic change processes, and wrong assumptions about resource and spatial availability. New methods to automate construction scheduling, sometimes referred to as generative construction scheduling, have been developed but are seldom taught in construction management courses. This paper describes the design of a new curriculum to teach one new approach to construction scheduling called the Tri-Constraint Method. The proposed five-lesson, flipped-classroom curriculum is designed to illustrate limitations of the critical path method, explain the theoretical and computational foundations of a generative scheduling algorithm, and provide practical experience through implementation with state-of-the-art software. To measure the impact of the curriculum on student learning, the paper conducts a descriptive statistical analysis, a paired t-test, and an examination of qualitative feedback. Students who entered the course with either low or high prior knowledge of construction scheduling showed significant improvement in their understanding of the key concepts and the algorithmic approach behind the generative construction scheduling. Overall, this paper demonstrates how curriculum design in engineering informatics can combine theoretical understanding and practical implementation to understand generative construction scheduling. The dissemination of this and similar teaching curricula can ensure that future engineering practitioners avoid “black box” implementations of automation software in their future careers.

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