Project portfolio selection and scheduling problem under material supply uncertainty

Integrated decision-making across project portfolio selection, scheduling, and material ordering is essential to avoid suboptimal outcomes, including delays, cost overruns, and missed opportunities. However, existing literature overlooks this integration, as well as the significant impact of uncertainty in material supply on decision-making processes. To address these gaps, we propose a robust methodology for integrating these aspects while accounting for uncertainties in material supply. Initially, we present a deterministic optimization model that integrates key decisions of project portfolio selection, project scheduling, and material ordering to maximize the net present value (NPV). We then enhanced this approach by incorporating various sources of uncertainty in material supply, resulting in a robust model. Given the NP-hard nature of the problem, a modified genetic algorithm was employed to solve it efficiently for larger sizes. Results demonstrate that the modified genetic algorithm enhances computational efficiency while maintaining solution quality compared to exact methods. Specifically, it reduces solution time by over 90% for medium-scale problems with an optimality gap of 1%. Implemented on a road construction project in Australia, sensitivity analysis highlights the pivotal role of supplier capacity in project profitability. A potential 20% increase in capacity correlates with a notable 32% increase in NPV, underlining the importance of considering uncertainty in this parameter. Findings demonstrate that the proposed robust approach ensures feasibility and high-quality solutions across various scenarios, offering decision-makers confidence in unpredictable conditions. This study provides a practical roadmap for integrating decision-making processes and managing uncertainty in project management, enhancing adaptability in dynamic environments.