A Grey Mathematics Approach for Evolutionary Multi-objective Metaheuristic of Project Portfolio Selection

Balderas, Fausto; Fernández, Eduardo; Gómez-Santillán, Claudia; Cruz-Reyes, Laura; Rangel-Valdez, Nelson; Morales-Rodríguez, María Lucila

doi:10.1007/978-3-319-71008-2_27

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Balderas, Fernandez, Gomez, and Cruz-Reyes (2017) presented a TOPSIS-Grey approach to handle project portfolio problem. Balderas et al (2018) also applied the grey mathematical approach to address project portfolio optimization. Zhao, Wu, and Wen (2018) applied grey entropy to discuss the evaluation of green construction projects.…”

Section: Grey Theorymentioning

confidence: 99%

Project Portfolio Selection Problems: A Review of Models, Uncertainty Approaches, Solution Techniques, and Case Studies

Mohagheghi

Mousavi

Antuchevičienė

et al. 2019

Technological and Economic Development of Economy

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Project portfolio selection has been the focus of many scholars in the last two decades. The number of studies on the strategic process has significantly increased over the past decade. Despite this increasing trend, previous studies have not been yet critically evaluated. This paper, therefore, aims to presents a comprehensive review of project portfolio selection and optimization studies focusing on the evaluation criteria, selection approach, solution approach, uncertainty modeling, and applications. This study reviews more than 140 papers on project portfolio selection research topic to identify the gaps and to present future trends. The findings show that not only the financial criteria but also social and environmental aspects of project portfolios have been focused by researchers in project portfolio selection in recent years. In addition, meta-heuristics and heuristics approach to finding the solution of mathematical models have been the critical research by scholars. Expert systems, artificial intelligence, and big data science have not been considered in project portfolio selection in the previous studies. In future, researchers can investigate the role of sustainability, resiliency, foreign investment, and exchange rates in project portfolio selection studies, and they can focus on artificial intelligence environments using big data and fuzzy stochastic optimization techniques.

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Section: Grey Theorymentioning

confidence: 99%

Project Portfolio Selection Problems: A Review of Models, Uncertainty Approaches, Solution Techniques, and Case Studies

Mohagheghi

Mousavi

Antuchevičienė

et al. 2019

Technological and Economic Development of Economy

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Multicriteria decision support for project portfolio selection with the FITradeoff method

Marques

Frej

Almeida

2022

Omega

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Optimization of renewable energy project portfolio selection using hybrid AIS-AFS algorithm in an international case study

Goli

2024

Sci Rep

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In the coming decade, as restrictions on fossil fuel usage become more stringent, investment in renewable energy projects presents an increasingly appealing opportunity. Evaluating investment attractiveness involves considering both profitability and investment risk. This study proposes a multi-objective mathematical model for identifying the optimal Renewable Energy Project Portfolio (REPP), aiming to maximize net present value while minimizing investment risk. The key innovation of this model is its incorporation of project lifetime and workforce employment considerations to discern the best REPP. To optimize the objective functions of this mathematical model, a hybrid meta-heuristic algorithm combining Artificial Immune System (AIS) and Artificial Fish Swarm (AFS) algorithms is introduced. Genuine data from a varied spectrum of renewable energy projects spanning 20 countries has been meticulously collected. The proposed model is optimized using this dataset, considering portfolio sizes of 3, 5, 10, and 15. The numerical results indicate that, at a specific investment risk threshold, the proposed hybrid algorithm outperforms both AIS and AFS in terms of profitability. Furthermore, the assessment of the geographical distribution of selected projects reveals a deliberate effort to avoid concentration in any specific region, demonstrating a commitment to identifying optimal investment opportunities globally. This research advances the understanding of renewable energy project portfolio optimization, providing valuable insights for investors, policymakers, and sustainable development practitioners.

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A Grey Mathematics Approach for Evolutionary Multi-objective Metaheuristic of Project Portfolio Selection

Cited by 3 publications

References 5 publications

Project Portfolio Selection Problems: A Review of Models, Uncertainty Approaches, Solution Techniques, and Case Studies

Project Portfolio Selection Problems: A Review of Models, Uncertainty Approaches, Solution Techniques, and Case Studies

Multicriteria decision support for project portfolio selection with the FITradeoff method

Optimization of renewable energy project portfolio selection using hybrid AIS-AFS algorithm in an international case study

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