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

TL;DR

This paper introduces a hybrid model combining System Dynamics and Agent-Based Modeling to predict R&D project progress in the oil and gas sector.

Contribution

The novel contribution is a multilevel AB–SD hybrid framework for R&D project management, capturing uncertainties like team size and task scheduling.

Findings

Parallel task execution reduced rework duration by 88% compared to sequential execution.

Optimal team size for task completion was four to five members, balancing efficiency and communication overhead.

The model aligns with empirical observations on R&D project dynamics and resource allocation.

Abstract

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.

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