A benefit-to-cost ratio based approach for portfolio selection under multiple criteria with incomplete preference information

计算机科学 文件夹 选择(遗传算法) 线性规划 运筹学 偏爱 功能(生物学) 数学优化 多准则决策分析 过程(计算) 数学 经济 人工智能 算法 统计 操作系统 金融经济学 生物 进化生物学
作者
Eduarda Asfora Frej,Petr Ekel,Adiel Teixeira de Almeida
出处
期刊:Information Sciences [Elsevier BV]
卷期号:545: 487-498 被引量:58
标识
DOI:10.1016/j.ins.2020.08.119
摘要

Benefit-to-cost ratio (BCR) measuring is a useful approach for selecting portfolios of projects when they are evaluated applying multiple and conflicting criteria. A multiattribute value function can be used to measure the benefit of each project, which allows one to evaluate the BCRs, rank projects, and select them according to the available budget. However, here, there is a significant difficulty associated with the inaccuracy in the values of criteria scaling constants, which may not be exactly known by decision makers (DMs). Considering this, the present work is directed at overcoming this difficulty by developing a BCR-based model for selecting portfolios under incomplete information about criteria scaling constants. During the elicitation process, DMs answer questions on preferences by considering tradeoffs amongst criteria. The provided information is converted into inequalities forming a space of criteria weights. These inequalities serve as constraints for linear programming models, which are processed to find dominance relations between projects, considering their BCRs. The process is supported by developed computing tools. The formation of a portfolio of research and development projects, which are to be executed by a Brazilian electric energy utility, is presented to illustrate the paper results and their practical applicability.
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