Multi-project Scheduling with Shared Resource Constraints for Maximizing Net Present Value Using a Tabu Search Heuristic Algorithm

This study investigates the multi-project scheduling problem under shared resource constraints, aiming to maximize net present value (NPV). The practical and theoretical background of the problem is introduced, while the research problem is formulated. An optimization model is established and the characteristics of this model are analyzed, furthermore, the key problem properties are refined. A tabu search (TS) heuristic algorithm is designed for solving the problem, with improvement measures proposed to enhance its efficiency. Finally, numerical experiments are conducted on randomly generated standard instances to verify the effectiveness of the algorithm, while sensitivity analysis of key parameters is performed. The conclusions drawn from the study are as follows: TS algorithm outperforms multistart iteration improvement (MSII) algorithm and random sampling (RS) algorithm, with the performance of the improved TS algorithm being the best. NPV increases with resource intensity and project deadlines, while it decreases with the resource factor. Additionally, NPV shows a monotonic increasing trend with the number of milestones, advance payment ratios, and progress payment ratios, while the discount rate negatively impacts NPV.