An Agent-Based Modeling Dynamic Hybrid Model for Project Management in Research and Development

This paper presents a hybrid approach to predict the evolution of technological maturity of R&D projects, using the context of the oil and gas (O&G) sector as an example. Integrating System Dynamics (SD) and Agent-based Modeling (ABM) enables the proposed multilevel framework to capture uncertainties inherent to R&D projects, including work effort, team size, and project duration, all of which influence technological progress. Although AB–SD hybrid models are well established in other fields, their application in R&D contexts remains limited. The AB–SD model combines system-level feedback structures governing work phases, rework cycles, and project duration with the explicit representation of decentralized agents (e.g., team members, tasks, and controllers) whose interactions drive emergent project dynamics. A base-case scenario was developed to analyze the structural dynamics of early-stage innovation projects, simulating 15 parallel tasks over 156 weeks. In a comparative scenario with sequential task execution, the model showed an 88% reduction in rework duration relative to the base case. The second scenario evaluated mixed parallel–sequential task structures under varying team sizes. In parallel configuration, simulation results indicated that increasing team size reduced overall project duration and improved task completion rates, with optimal performance achieved for teams of four to five members. These outcomes are consistent with empirical observations in R&D project management, where moderate team expansion enhances coordination efficiency without incurring communication overhead. However, as widely recognized in empirical studies, a substantial increase in team size does not necessarily translate into higher completion rates, as excessive team growth often introduces communication complexity and management delays. Overall, the model outputs and the proposed modeling framework are well aligned with expert understanding in the field, confirming their validity as a quantitative tool for analyzing resource allocation, task scheduling efficiency, and technology maturity progression.