Project Portfolio Selection Considering the Fuzzy Chance Constraint of Water Environmental Restoration

The water environment restoration project portfolio (WERP) selection is discussed in this paper. By complying with the analysis of the project's multidimensional property and operation mode, this paper develops the chance constraint and the management constraint of the WERP from the perspectives of public service and enterprise operation. In addition, the multi-objective mixed integer linear programming model is constructed by combining the expectation method and the fuzzy chance constraint programming method. The results demonstrate that: (1) Our proposed method successfully circumvents the occurrence of local objective optimization within a specific confidence interval, thereby achieving a balance between economic and water environment restoration objectives; (2) including fuzzy chance constraints in our proposed method significantly diminishes the risk of exceeding the WERP capacity, thereby ensuring the effectiveness of water environment restoration by adopting a market-based approach. However, further examination of the impact of various sub-projects in WERP is necessary, along with the integration of novel evolutionary algorithms to enhance the efficiency of our model.

Automated summary

This paper discusses the selection of the water environment restoration project portfolio (WERP). By analyzing the project's multidimensional property and operation mode, this paper develops the chance and management constraints of WERP from the perspectives of public service and enterprise operation. Furthermore, a multi-objective mixed integer linear programming model is proposed using the expectation method and fuzzy chance constraint programming method. The results show that our proposed method achieves a balance between economic and water environment restoration objectives and effectively manages the risk of exceeding the WERP capacity through market-based approaches. However, further examination of the impact of sub-projects and integration of evolutionary algorithms are needed to enhance the model's efficiency.