With limited financial resources, decision-makers in firms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash flows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases.

中文翻译：

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不确定条件下项目组合选择的可变邻域搜索模拟

由于财政资源有限，企业和政府的决策者面临选择最佳投资项目组合的任务。随着项目建议库的增加和考虑到更现实的限制，问题变得越来越棘手。因此，元启发法已用于解决项目组合选择问题（PPSP）的大型实例。但是，大多数现有作品并未考虑不确定性。本文通过分析PPSP的随机版本为缩小这一差距做出了贡献：目标是最大化反转的预期净现值，同时考虑未来时期的随机现金流量和折现率以及一系列丰富的约束包括允许的最大风险。为了解决这种随机PPSP，引入了一种仿真优化算法。我们的方法将可变邻域搜索元启发式方法与蒙特卡洛模拟相结合。一系列计算实验有助于验证我们的方法，并说明解决方案如何随着不确定性水平的提高而变化。