Leveraging BIM, EPS and constraint programming for automated scheduling: a methodology for structured scheduling data and combinatorial optimization

Purpose This study aims to enhance construction scheduling through a computational methodology that enables structured input preparation, constraint formulation and combinatorial task analysis to improve schedule feasibility, automation and optimization Design/methodology/approach The proposed methodology integrates building information modeling (BIM), enhanced planning and scheduling (EPS), a structured scheduling approach and constraint programming (CP) to enhance computational and combinatorial scheduling. BIM data is automatically extracted and enriched with material quantities, spatial breakdowns and task types, then structured using EPS into labor-hour–based units and spatial zones. These structured inputs feed into a CP model incorporating precedence logic, resource constraints and execution priorities to generate the construction schedule. Moreover, constraint modification and EPS-driven combinatorial analysis enable alternative scheduling and scenario evaluation. Findings The methodology was applied to a multi-section residential project, resulting in a feasible and optimized construction schedule. The CP model optimized resource use and duration based on the objectives while maintaining logical sequencing, with automated BIM-EPS input reducing manual effort. The schedule was automatically generated based on the predefined constraints and consistency was confirmed using Kendall’s Tau-b correlation. Originality/value This study presents a novel integration of BIM, EPS and CP to advance logic-based and computational scheduling. A key contribution of this study is the implementation of advanced scheduling by facilitating constraint formulation through the EPS methodology and enriched BIM data integration. This approach enables schedule feasibility analysis, improves consistency and addresses limitations of constraint- and logic-based methods through structured input and formalized constraints. By automating the extraction and structuring of data for CP, it reduces some manual effort in data preparation and supports optimization though initial setup and predefined constraints.