Research on Timing Sequence Update Strategy Decision of Project Portfolio Based on Coupling Benefits in Strategic Period

The Chinese government's substantial investment in water restoration has created numerous lucrative opportunities for commercial environmental restoration enterprises. Accordingly, this research study seeks to address the primary challenge faced by enterprise managers: selecting projects that fulfill both strategic imperatives and maximize economic returns. To tackle this issue, we segmented the overarching strategic period into multiple phases and studied the project portfolio from a holistic strategic period perspective. We introduced a decision model for the dynamic, sequential updating of the portfolio throughout the strategic period, emphasizing the combined benefits at each phase. This model guides the strategic selection of projects at any decision-making stage to optimize the benefits across the entire strategic duration. The multi-agent Nash Q-learning algorithm was employed for portfolio construction and updating strategies. This approach yields an optimal project portfolio for each phase of the strategy. Unlike traditional methods that predominantly focus on cumulative returns and find it challenging to accommodate strategic shifts, our proposed technique prioritizes intertwined strategic returns. It promotes project choices in tune with strategic contexts and supports ongoing adjustments to project strategies, providing invaluable guidance for decision makers. A comparison of our proposed method with other optimization strategies validated its superior performance. Furthermore, the case study described in this study confirms that our method promotes project choices in tune with strategic contexts and supports ongoing adjustments to project strategies, providing invaluable guidance for decision makers.