Simulation-based approach for solving stochastic network resource-constrained project scheduling problems
This paper aims to propose a discrete-event simulation framework for solving the Stochastic Network-based Resource-Constrained Project Scheduling Problem (SN-RCPSP), which involves uncertainty in both activity duration and occurrence, and allows for feedback loops and limited renewable resources. A novel simulation methodology is implemented using Enterprise Dynamics software. The model integrates probabilistic branching, stochastic durations and resource constraints in an Agent-Based Modeling architecture. The approach is validated using benchmark cases, including statistical and analytical cross-validation against known methods. The proposed model achieves comparable or superior accuracy to existing simulation and analytical methods with significantly fewer replications. It also allows for performance evaluation under different resource availability scenarios and provides managerial insights into optimal resource allocation. This study models only one type of renewable resource and assumes fixed execution modes for activities. Learning effects and cost structures are not included, which can be explored in future research. The simulation model offers a robust tool for practitioners in project planning, especially in uncertain and dynamic environments like pharmaceutical approvals or R&D projects. It allows managers to assess risks and resource impacts before execution. This paper contributes a comprehensive and flexible simulation-based methodology for SN-RCPSPs, validated via empirical, theoretical and scenario-based analyses. It extends classical models by incorporating resource constraints and supports performance-driven decision-making in stochastic project environments.