A variable neighborhood search simheuristic for project portfolio selection under uncertainty
A variable neighborhood search simheuristic for project portfolio selection under uncertainty
复制标题
不确定性下项目组合选择的变量邻域搜索模拟启发式
DOI：
10.1007/s10732-018-9367-z
复制
发表时间：
2018
影响因子：
2.7
通讯作者：
Angels Fito
中科院分区：
文献类型：
--
作者：
Javier Panadero;Jana Doering;Renatas Kizys;A. Juan;Angels Fito
由于财政资源有限，公司和政府的决策者面临着选择最佳投资项目组合的任务。随着项目提案库的增加和更多现实的约束条件的考虑，问题变得NP难。因此，元分析已被用于解决大型项目组合选择问题（PPSP）的实例。然而，大多数现有的工作没有考虑到不确定性。本文通过分析PPSP的随机版本来缩小这一差距：目标是最大化反演的预期净现值，同时考虑未来时期的随机现金流和贴现率，以及一组丰富的约束条件，包括允许的最大风险。为了解决这个随机PPSP，模拟优化算法的介绍。我们的方法集成了可变邻域搜索元启发式与蒙特卡洛模拟。一系列的计算实验有助于验证我们的方法，并说明如何解决方案的不确定性水平的增加而变化。
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.