An stochastic multiattribute acceptability analysis‐based method for the multiattribute project portfolio selection problem with rank‐level information

Abstract

In many cases of practical multiattribute project portfolio selection problems, it is hard to obtain accurate measurements of attributes and precise preference information. Even after a long and costly information gathering, the attribute measurements and the preference information can still be uncertain or inaccurate. Considerable cost saving will be obtained if the selection of an optimal project portfolio can be done using rank‐level information based on some or all the attributes, without knowing the preference information. In this paper, we propose a stochastic multiattribute acceptability analysis‐based method that can deal with mixed rank and cardinal attribute measurements and uses little or no weight information. In the proposed stochastic multiattribute acceptability analysis‐based method, the decision makers need not to express their preferences explicitly or implicitly, so it is particularly useful when no weight information is available at all. A numerical example involving selection of photovoltaic plants in an industrial province in Eastern China is provided to demonstrate the proposed method.

A methodology for strategy-oriented project portfolio selection taking dynamic synergy into considerations

“…Therefore, Projects A, C and E should be removed from the candidate project list, which only Projects B, D, F, G, H, I and J are suitable for Company A to choose five projects to formulate 21 project portfolios, i.e., C 7 5 =2 ¼ 21. Step 2: Once those projects that exceed available resources have been removed from the candidate project list, the multiproject assessment [46] needs to be applied by considering resources required for remaining projects. Screening project analysis can be applied to remove those remaining projects that exceed the total resource of Company A. LetP ¼ P 1 ; P 2 :::P i :::P n ð Þ denote the set of i candidate projects, where i is the number of projects.…”

A Two-Stage Mathematical Programming Model for Distributed Photovoltaic Project Portfolio Selection With Incomplete Preference Information

“…More importantly, from the third and fifth columns of Table 1, we can find that many studies used the fuzzy sets to describe the uncertainty of PV project investment but the preference information was complete. Song et al (2019) used the stochastic multicriteria acceptability analysis (SMAA) method to deal with incomplete preference information when selecting a project portfolio. However, as the weighted averaging operator is used to aggregate evaluations in their paper, the degree of compensation between attributes cannot be changed.…”

Efficiency evaluation and ranking of supply chains based on stochastic multicriteria acceptability analysis and data envelopment analysis

“…On the one hand, more different data types are considered to extend SMAA, such as pseudo-criteria (Hokkanen et al, 1998), ordinal criteria (Lahdelma et al, 2003), and multivariate Gaussian criteria (Hwang and Yoon, 1981), PROMETHEE (Mareschal et al, 1984), evidential reasoning (Yang and Singh, 1994), and TODIM Lima, 1991, 1992), have been combined with SMAA for stochastic multicriteria decision making (Okul et al, 2014;Corrente et al, 2014;Zhang et al, 2017Zhang et al, , 2019. In addition to model developments, there are also interesting applications area based on SMAA, for example, the interval cross-efficiencies aggregation problem (Yang et al, 2012), the ABC inventory classification problem (Li et al, 2017), and project portfolio optimization problems (Yang et al, 2015;Song et al, 2019). introduced SMAA into DEA and proposed an SMAA-D method, which extended the DEA analysis to handle uncertain or imprecise criteria measurements and partial preference information using stochastic distributions.…”