Stochastic multi-criteria decision-making: an overview to methods and applications

Çelik, Erkan; Gül, Muhammet; Yücesan, Melih; Mete, Süleyman

doi:10.1186/s43088-019-0005-0

Cited by 15 publications

(6 citation statements)

References 71 publications

(99 reference statements)

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“…This fuzzy set concept reflects uncertainty more robustly and accurately (Mendel et al 2006 ). This fuzzy set extension has been applied to many multi-criteria decision-making (MCDM) problems due to ease of calculation, the necessity of reflecting the uncertain environment in decision-making, and difficulties in obtaining precise data (Celik et al 2019 ). Celik et al’s ( 2015 ) study can be seen in a detailed literature review.…”

Section: Methodsmentioning

confidence: 99%

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COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model

Baskak

Ozbey

Yücesan

et al. 2022

Environ Sci Pollut Res

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The fight against the COVID-19 pandemic, which has affected the whole world in recent years and has had devastating effects on all segments of society, has been one of the most important priorities. The Turkish Standards Institution has determined a checklist to contribute to developing safe and clean environments in higher education institutions in Turkey and to follow-up on infection control measures. However, this study is only a checklist that makes it necessary for decision-makers to make a subjective evaluation during the evaluation process, while the need to develop a more effective, systematic framework that takes into account the importance levels of multiple criteria has emerged. Therefore, this study applies the best-worst method under interval type-2 fuzzy set concept (IT2F-BWM) to determine the importance levels of criteria affecting the “COVID-19 safe campus” evaluation of universities in the context of global pandemic. A three-level hierarchy consisting of three main criteria, 11 sub-criteria, and 58 sub-criteria has been created for this aim. Considering the hierarchy, the most important sub-criterion was determined as periodic disinfection. The high contribution of the interval-valued type-2 fuzzy sets in expressing the uncertainty in the decision-makers’ evaluations and the fact that BWM provides criterion weights with a mathematical optimization model that produces less pairwise comparisons and higher consistency are the main factors in choosing this approach. Simple additive weighting (SAW) has also been injected into the IT2F-BWM to determine the safety level of any university campus regarding COVID-19. Thus, decision-makers will be better prepared for the devastating effects of the pandemic by first improving the factors that are relatively important in the fight against the pandemic. In addition, a threshold value will be determined by considering all criteria, and it will prepare the ground for a road map for campuses. A case study is employed to apply the proposed model, and a comparison study is also presented with the Bayesian BWM to validate the results of the criteria weights. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-22796-1.

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Section: Methodsmentioning

confidence: 99%

“…IT2F-BWM was initially proposed by Wu et al ( 2019 ) and applied to the green supplement selection problem. In supplier selection problems, Celik et al ( 2019 ) found the most suitable supplier IT2F-BWM and TODIM. Hoseini et al ( 2021 ) used BWM in combination with TOPSIS.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model

Baskak

Ozbey

Yücesan

et al. 2022

Environ Sci Pollut Res

Self Cite

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“…Because of its ability to accommodate high levels of ambiguity and uncertainty, SMCDM has gained considerable popularity in addressing MCDM problems in a variety of scenarios. In a detailed review, Celik et al ( 2019 ) found that more than a quarter of the applications of SMCDM were to finance problems. These applications focus on specific issues such as credit scoring, investment project choices, enterprise selection, pension fund selection, bank investment evaluation, and even luxury car selection.…”

Section: Artificial Intelligencementioning

confidence: 99%

Review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence

Černevičienė

Kabašinskas

2022

Front. Artif. Intell.

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The influence of Artificial Intelligence is growing, as is the need to make it as explainable as possible. Explainability is one of the main obstacles that AI faces today on the way to more practical implementation. In practise, companies need to use models that balance interpretability and accuracy to make more effective decisions, especially in the field of finance. The main advantages of the multi-criteria decision-making principle (MCDM) in financial decision-making are the ability to structure complex evaluation tasks that allow for well-founded financial decisions, the application of quantitative and qualitative criteria in the analysis process, the possibility of transparency of evaluation and the introduction of improved, universal and practical academic methods to the financial decision-making process. This article presents a review and classification of multi-criteria decision-making methods that help to achieve the goal of forthcoming research: to create artificial intelligence-based methods that are explainable, transparent, and interpretable for most investment decision-makers.

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“…In order to assess this, a probabilistic approach can be taken, using stochastic (Monte-Carlo) simulation to run a predetermined number of iterations of the MCE workflow, subjecting its criteria to the fluctuations derived from the RMSE. Stochastic processes are sometimes used in flood risk modelling and MCE independently (Celik et al, 2019), but there is no literature which combines stochastic modelling and MCE analysis when evaluating flood risk. This study therefore aims to fill in the existing void in the literature, and hopefully encourage other authors to adopt similar techniques in their own research.…”

Section: Introductionmentioning

confidence: 99%

Application of LiDAR-Derived Data using Multi-Criteria Evaluation (MCE) and Stochastic Modelling; A Flood Risk Analysis of the Mersey River, Nova Scotia

Nieto

Pica²,

Stange³

2022

SURG

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The Maritime province of Nova Scotia has seen coastal flooding become a more frequent phenomenon during the past decades due to the changing climate regime. This has influenced the interest provincial and federal governments have in flood risk modelling, who often incorporate Geographic Information Systems (GIS) as useful tools in their analysis. Incorporating LiDAR-derived digital elevation models (DEMs) in their workflows is the next step in hydrological analysis, as LiDAR-derived DEMs offer high resolution data for the analysis of flood risk without the need to rely on biotic or hydrological data. This study aims to follow this theme in order to model the effects of inland flooding in the low relief landscape of the Mersey River, located in Queen’s County, Nova Scotia, and its effects on the infrastructure built along the river network. The analysis included multi-criteria evaluation (MCE) methods coupled with a stochastic simulation approach in order to determine areas where vulnerability is the most certain. Results indicated that high flood risk is present in urbanized areas within 1 km of the Mersey River at a low degree of uncertainty, making them the best candidates for flood-preventive measures. The accuracy provided by LiDAR-derived DEMs supported a high-quality workflow for the MCE and DEM error analysis, proving their utility for floodplain delineation. The addition of historical and hydrological data to future projects could build on the results presented in this study, adding more to the literature on flood risk modelling along the Mersey River.

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Stochastic multi-criteria decision-making: an overview to methods and applications

Cited by 15 publications

References 71 publications

COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model

COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model

Review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence

Application of LiDAR-Derived Data using Multi-Criteria Evaluation (MCE) and Stochastic Modelling; A Flood Risk Analysis of the Mersey River, Nova Scotia

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