Agent-Based and Discrete-Event Modeling for Adaptive Engineering Systems Analysis

Abstract

Adaptive engineering systems have become increasingly complex due to autonomous decision-making, dynamic interactions, and uncertain operating environments, making conventional simulation approaches insufficient for comprehensive system analysis. This study proposes a Hybrid Agent-Based Modeling and Discrete-Event Simulation (ABM–DES) framework to integrate intelligent agent behavior with event-driven operational processes for adaptive engineering systems analysis. The proposed methodology consists of problem definition, conceptual modeling, hybrid model development, simulation experiments, and model validation through verification, sensitivity analysis, and robustness assessment. Performance was evaluated using multiple key performance indicators, including throughput, waiting time, resource utilization, response time, service level, operational cost, resilience, and flexibility under various operational scenarios. The results demonstrate that the proposed Hybrid ABM–DES model consistently outperforms traditional analytical methods as well as standalone DES and ABM approaches. Specifically, the hybrid framework achieved 36.8% higher throughput, 32.6% greater resource utilization, 25.7% improvement in service level, while reducing waiting time, response time, and operational cost by 42.7%, 38.3%, and 41.5%, respectively. Dynamic simulations further confirmed superior resilience and faster recovery during disruption events, whereas scenario-based and sensitivity analyses identified arrival rate, service time, resource capacity, and agent adaptation rate as the most influential factors affecting system performance. The primary novelty of this research is the development of a generalized hybrid ABM–DES framework that seamlessly integrates adaptive agent intelligence, discrete-event process dynamics, and multi-scenario decision analysis within a unified simulation architecture. The proposed framework provides a robust and scalable decision-support tool for optimizing complex adaptive engineering systems, including smart manufacturing, intelligent logistics, autonomous transportation, aerospace-systems.