Amirkabir University of Technology AUT Journal of Civil Engineering 2588-2899 9 3 2025 11 01 Optimizing Dynamic Scheduling in Construction with BIM: A Framework for Budget-Constrained Resource Management 233 252 5870 10.22060/ajce.2025.23350.5873 EN Ali Akbar Shirzadi Javid School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran 0000-0002-6105-5991 Shahrzad Omrani School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran Sahar Falegari School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran Journal Article 2024 07 16 Project scheduling is a fundamental part of construction management, as it controls activity timing, costs, and resource allocation. Despite the available tools for planning a project, such an important role still relies heavily on the scheduler's experience and goes through many trial-and-error situations during the project. This research develops a new framework for time and resource allocation optimization in a project to further facilitate project planning. The framework also attempts to gather, store, and process all of the project’s data in order to achieve an accurate estimation. Building Information Modeling (BIM) was used to store the necessary data, and after defining the constraints, the model was transferred to Simphony.NET via a Visual Basic (VB.NET) data-exchange module that queried and exported task dependencies, resource limitations, and budget constraints stored in an MS Access database. The transfer mechanism preserved the relational data schema (foreign keys linking tasks, resources, and costs), thereby ensuring interoperability and preventing data loss.  Finally, the ant colony algorithm was used for optimization. The outcome was compared to a real-life case study, and the reliability of the algorithm was validated. Results show that compared to the actual project duration of 108 days and the contractor’s initial planned duration of 90 days, our model predicted 97 days. This reduced the time estimation error from 16% (initial vs. actual) to 10% (model vs. actual). Furthermore, relative to the actual project outcome, the optimized schedule achieved an 18% improvement in project duration and a 13% reduction in total cost. https://ajce.aut.ac.ir/article_5870_1f74a54f39b3123ad272ca0a06e7463f.pdf