Specialty contractors, generally known as subcontractors, perform the majority of the work on commercial construction projects. They face significant challenges in allocating their resources across multiple concurrent projects (O'Brien and Fischer 2000). Despite their central nature to project performance, relatively little research has been conducted to formally model their operations. Without such modeling, there is little basis from which to measure or improve performance. This thesis attempts to address the limited literature by providing a formal information model for subcontractor resource management. There are three main contributions: First, adding to the general literature on subcontractor management as a precursor to formal modeling. Second, an information model for managing subcontractor resources. This model is expressed in UML (Unified Modeling Language) and implemented in code. Third, as part of the information model, an extension to the Process Connectors architecture (Siddiqui et al 2008) to add resource constraints to distributed planning coordination. Model development and validation is performed through case studies with subcontractors and general contractors. Collectively, the contributions of this research provide a practical basis for describing and representing subcontractor resources that can improve practice and provide a basis for future prescriptive research. 1.1 Rationale: Multi-Project Resource Allocation of Subcontractors In construction projects, it is common for the majority of work to be performed by subcontractors. Therefore, it is critical to understand subcontractors' issues, concerns, and how they coordinate with general contractors. Existing literature, case st...... half of the article ......cation processes. The model is as generalizable as possible but perhaps slightly unsuitable for all subcontractors due to the nature of their business and their corporate strategies. This research aims to support descriptive modeling. The developed model and implementation software represent resource constraints and support human decision makers. What-if analysis and incorporation of local resource constraints are the specific focus of this research. This research does not provide an optimization or resource allocation algorithm. Optimization techniques for solving resource constraint problems have been found to be computationally impractical for most large real-life projects due to the enormous number of variables and constraints (Abeyasinghe, Greenwood et al. 2001). The focus is on descriptive modeling that supports human-directed scenario analysis.