Construction workforce allocation represents a critical operational variable in construction logistics management, directly influencing project flow efficiency, schedule reliability, and overall performance. In complex construction systems, bottlenecks emerge as dynamic constraints within interconnected activity networks, disrupting labor flow and amplifying delay propagation. Despite its operational importance, bottleneck identification in construction scheduling remains largely heuristic and experience-driven. This study proposes a workforce bottleneck analytics framework derived from operational combinatorial modeling to quantitatively capture interactions among activity sequencing, workforce allocation, and schedule dynamics. The analytical framework translates construction scheduling into measurable operational patterns reflecting workforce allocation behavior, task interdependency, and schedule deterioration mechanisms. Multiple workforce allocation scenarios are subsequently generated and analyzed to identify recurring resource-deficient activities associated with unfavorable scheduling outcomes. The proposed framework is validated through a real-world case study of a three-story urban building project in Pekanbaru, Indonesia. Results demonstrate that the integration of analytical modeling and workforce bottleneck occurrence analysis enables systematic, data-driven bottleneck identification and reveals recurring operational patterns embedded within construction scheduling systems. The findings indicate that schedule degradation is strongly influenced by recurring workforce allocation deficiencies rather than solely by critical path logic. More importantly, the study provides empirical evidence that construction scheduling contains observable and repeatable operational structures that can be systematically extracted from project data and transformed into operational knowledge. The approach transforms conventional schedule tracking into an operational intelligence framework that supports proactive workforce management and provides a foundation for future artificial intelligence and advanced analytics applications in construction project control.

Keywords

Construction scheduling; workforce allocation; workforce bottleneck analytics; operational intelligence; recurring operational patterns; project performance.