Projects Portfolio Selection Framework Combining MCDA UTASTAR Method with 0–1 Multi-Objective Programming

Vryzidis, Isaak; Spyridakos, Athanasios; Tsotsolas, Nikos

doi:10.1007/978-3-319-90599-0_6

Cited by 2 publications

(1 citation statement)

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“…Several methods in MCDM used the concept of utility (Ghannadpour et al, 2020), in this regard; UTA and UTASTAR methods can be named (Ehsanifar et al, 2020). UTASTAR is an advanced version of UTA (Vryzidis et al, 2018). On the other hand, the fuzzy UTASTAR (FUTASTAR)…”

Section: First Stagementioning

confidence: 99%

Sustainable project portfolio selection under uncertainty with consider conflicting projects

Ramedani

Mehrabian

Didehkhani

2022

Preprint

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Given the complexity of selecting an industrial project portfolio, portfolio project selection (PPS) has been one of the most crucial concerns of project managers. On the other hand, comprehensive evaluation of projects based on multiple criteria and uncertainty causes the complexity of PPS. Therefore, to improve this problem, this study presents PPS considering a synergy of industrial projects with different criteria related to sustainability, time-dependent, resources, budget, and human resource capability in a two-stage decision under uncertainty. In the first stage, according to the review of historical documents, analysis of checklists, consultants, and specialists the criteria are identified. Then, with a multi-criteria decision-making (MCDM) method, it is considered for scoring with Fuzzy Best-Worst Method (FBWM) and ranking with Fuzzy Vlsekriterijumska Optimizacija IKompromisno Resenje (FVIKOR) method and calculating the sustainable utility of projects. In the second stage, a multi-objective fuzzy-robust-stochastic (FRS) optimization model by maximizing the total profit of the project is a value of the sustainable utilization of the project and minimizing the amount of interruption in project implementation. This model has two challenges including multi-objective and complex, thus to solve the mixed-integer linear programming model, a novel hybrid method according to the Multi-Choice Goal Programming with Utility Function (MCGP-UF) with the Genetics Algorithm (GA) is developed. The outcomes of sensitivity analysis by implementing the model in the real world display that the model and approach presented in varies problems have high flexibility.

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Section: First Stagementioning

confidence: 99%