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Precast concrete construction enhances project efficiency, sustainability, and durability by leveraging a controlled production environment that ensures high-quality outputs. Still, the sequential nature of off-site production, encompassing casting, curing, and storage of the precast elements, is subject to significant uncertainties, including supply chain variability and environmental factors affecting curing times. Dynamic adjustments to the production schedule are essential for aligning with real-time changes, necessitating a robust framework for real-time data acquisition and analysis. Dynamic Scheduling (DS) is a responsive and adaptive approach that accommodates real-time changes, optimizes the production flow, and minimizes downtime or delays. However, the DS approach demands real-time data to quickly and efficiently respond to unforeseen challenges, which requires acquiring and analyzing production-relevant data throughout the production process. The Digital Twin (DT) emerges in Industry 4.0 (I4.0) as a bridge between physical operations and digital capabilities, enabling a seamless flow of information, for which the Asset Administration Shell (AAS) is a reference implementation. This study introduces a DS framework to optimize precast element production, utilizing a DT for real-time data aggregation across the production system. The framework implements a Semantic DT based on the AAS and the Linked Data approach. It employs Resource Description Framework (RDF) serialization of the AAS and an ontological representation of the production system for data integration. The framework leverages a simulation-based scheduler, which exchanges data with the DT in a Service-oriented Architecture (SoA) using SPARQL, a language for querying and updating graph databases. The approach is evaluated through a proof of concept, demonstrating effective uncertainty management in a dynamic production environment.
Kosse et al. (Sat,) studied this question.