Linear solution schemes for Mean-SemiVariance Project portfolio selection problems: An application in the oil and gas industry

Sefair, Jorge A.; Méndez, Carlos Y.; Babat, Onur; Medaglia, Andrés L.; Zuluaga, Luis F.

doi:10.1016/j.omega.2016.05.007

Cited by 40 publications

(18 citation statements)

References 32 publications

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“…Integer Linear Programming Carlsson et al (2007), Bas (2012), Zhu and Wang (2012), Li et al (2014), Qin et al (2014), Jingmei and Peng (2015), Wu, Xu, Ke, Chen, and Sun (2018), Lifshits and Avdoshin (2016) Fuzzy programming Riddell and Wallace (2007), Gutjahr et al (2008), In this section, modeling approaches were reviewed. Various models have been developed to attend to PPS (e.g., Sefair et al, 2017;Schaeffer & Cruz-Reyes, 2016;Li, Wang, Yan, & Zhao, 2018). Such approaches limit the ability of top managers in the process of PPS.…”

Section: Scoring Methodsmentioning

confidence: 99%

“…To better illustrate the exact approaches, Table 14 is presented. Hassanzadeh et al (2014b) Robust augmented weighted Tchebycheff programs Hall et al (2015), Sefair et al (2017) Benders decomposition Roland et al (2016) Cutting-plane approach Y. Liu and Y. K. Liu (2017) The equivalent analytical expressions of credibility constraints…”

Section: Exactmentioning

confidence: 99%

“…,Naderi (2013),Li et al (2015),Gang et al (2015),Ghodoosi, Maftahi, & Yousefi (2016),Sefair et al (2017), Schaeffer and Cruz-Reyes (2016), Li et al (2018) Mixed integer Programming Hu et al (2008), Gutjahr et al (2010), Urli and Terrien (2010), Carazo et al (2010), Fernandez et al (2013), Rabbani et al (2013), Hassanzadeh et al (2014), Perez and Gomez (2014), Gang et al (2015), Roland, Figueira, and De Smet (2016), Wu et al (2018), Mohagheghi et al (2016), Lifshits and Avdoshin (2016) Multi objective programming Gutjahr et al (2008), Guo, Liang, Zhu, and Hu (2008), Gutjahr and Reiter (2010), Urli and Terrien (2010), Yu et al (2012), Hall et al (2015), Carazo (2015), Jingmei and Peng (2015), Ghodoosi et al (2016), Li et al (2018) Non-linear Programming Gurgur (2009), Gutjahr and Reiter (2010), Gutjahr and Froeschl (2013), Mavrotas and Pechak (2013a), Mavrotas and Pechak (2013b), Mavrotas and Pechak (2013), Yang et al (2015) Stochastic Programming Zhang et al (2011), Mohagheghi et al (2015b), Mohagheghi et al, (2017a), Li et al (2018) Semi-variance, mean variance models Sheng and Chen (2011), Sefair et al (2017) Quadratic programming…”

mentioning

confidence: 99%

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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: Scoring Methodsmentioning

confidence: 99%

Section: Exactmentioning

confidence: 99%

mentioning

confidence: 99%

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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

View full text Add to dashboard Cite

show abstract

“…Theoretically, it is much more realistic than the mean-variance approach, since the portfolio managers are in fact only concerned with the left-side of the return distribution. Paquin et al (2016) investigated this downside risk, and its impact on the expected profitability of a firm, and Sefair et al (2016) used semi-variance as a risk measure in their optimisation in the oil and gas industry.…”

Section: Downside Riskmentioning

confidence: 99%

“…Chang et al (2000) Liagkouras & Metaxiotis, (2014) and Saborido et al (2016) considered the cardinality constraints for their optimisation. Sefair et al (2016) optimised a portfolio of non-divisible projects with semi-variance as a risk measure. Portfolio managers must consider both maximising the expected return and minimising the risk (Woodside-Oriakhi et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

The Impact Made on Project Portfolio Optimisation by the Selection of Various Risk Measures

Yousefi

Yakhchali

Šaparauskas

et al. 2018

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This study addresses the effect of selecting an appropriate risk measure and the impact of this choice on the efficient frontier of the project portfolio of an organisation. The appropriate choice of a firm's project portfolio has a great impact on the organisational success. Each portfolio manager selects the best projects with different criteria and consistent with firm's strategic objectives. We used the Markowitz efficient frontier method to select the best projects of the organisation. The choice of proper measures impacts on this decision and can change the organisation's portfolio. The standard deviation was applied, and the relevant optimisation was made for this purpose. Then, the semi-standard deviation was used to differentiate between favourable and unfavourable opportunities. Afterwards, Value at Risk and Expected Shortfall were applied as appropriate risk measures to make a better estimate of the tail risks. All these risk measures were used to select the best possible projects. Managers should select the appropriate risk measures according to their objectives, estimation of their project distribution, and characteristics of the projects. This research studied the best measures consistent with construction projects and the effect of changes in these measures.

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Fuzzy Random Based Mean Variance Model for Agricultural Production Planning

Othman

Arbaiy

Lah

et al. 2019

Advances in Intelligent Systems and Computing

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Linear solution schemes for Mean-SemiVariance Project portfolio selection problems: An application in the oil and gas industry

Cited by 40 publications

References 32 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

The Impact Made on Project Portfolio Optimisation by the Selection of Various Risk Measures

Fuzzy Random Based Mean Variance Model for Agricultural Production Planning

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