Extension of MEREC-CRADIS methods with double normalization-case study selection of electric cars

Puška, Adis; Božanić, Darko; Mastilo, Zoran; Pamučar, Dragan

doi:10.1007/s00500-023-08054-7

Cited by 22 publications

(16 citation statements)

References 77 publications

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“…To validate the results obtained using the entropy-AROMAN framework, a comparison was made with other the MCDM models, such as the multi-attributive border approximation area comparison (MABAC) [ 56 ], proximity indexed value (PIV) [ 57 ], and compromise ranking of alternatives from distance to ideal solution (CRADIS) [ 58 ] methods.…”

Section: Resultsmentioning

confidence: 99%

A Multicriteria-Based Comparison of Electric Vehicles Using q-Rung Orthopair Fuzzy Numbers

Biswas

Sanyal

Božanić

et al. 2023

Entropy

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The subject of this research is the evaluation of electric cars and the choice of car that best meets the set research criteria. To this end, the criteria weights were determined using the entropy method with two-step normalization and a full consistency check. In addition, the entropy method was extended further with q-rung orthopair fuzzy (qROF) information and Einstein aggregation for carrying out decision making under uncertainty with imprecise information. Sustainable transportation was selected as the area of application. The current work compared a set of 20 leading EVs in India using the proposed decision-making model. The comparison was designed to cover two aspects: technical attributes and user opinions. For the ranking of the EVs, a recently developed multicriteria decision-making (MCDM) model, the alternative ranking order method with two-step normalization (AROMAN), was used. The present work is a novel hybridization of the entropy method, full consistency method (FUCOM), and AROMAN in an uncertain environment. The results show that the electricity consumption criterion (w = 0.0944) received the greatest weight, while the best ranked alternative was A7. The results also show robustness and stability, as revealed through a comparison with the other MCDM models and a sensitivity analysis. The present work is different from the past studies, as it provides a robust hybrid decision-making model that uses both objective and subjective information.

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Section: Resultsmentioning

confidence: 99%

A Multicriteria-Based Comparison of Electric Vehicles Using q-Rung Orthopair Fuzzy Numbers

Biswas

Sanyal

Božanić

et al. 2023

Entropy

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“…In their paper, they selected the electric car that best met the set goals using Promethee II (protracted method that requires a priori knowledge of the criteria weights II). Puška et al [36] revealed the possibility of introducing electric vehicles to reduce the impact on the environment. They selected electric vehicles in order to achieve this goal, using the DNMEREC (Double Normalization Method based on the Removal Effects of Criteria) and DNCRADIS (Double Normalization Compromise Ranking of Alternatives from Distance to Ideal Solution) methods.…”

Section: Application Of Multi-criteria Methods In the Selection Of El...mentioning

confidence: 99%

Multi-Criteria Selection of Electric Delivery Vehicles Using Fuzzy–Rough Methods

Wang,

Xu,

Puška

et al. 2023

Sustainability

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Urban logistics implementation causes environmental pollution; therefore, it is necessary to consider the impact on the environment when carrying out such logistics. Electric vehicles are alternative vehicles that reduce the impact on the environment. For this reason, this study investigated which electric vehicle has the best indicators for urban logistics. An innovative approach when selecting such vehicles is the application of a fuzzy–rough method based on expert decision making, whereby the decision-making process is adapted to the decision makers. In this case, two methods of multi-criteria decision making (MCDM) were used: SWARA (stepwise weight assessment ratio analysis) and MARCOS (measurement alternatives and ranking according to compromise solution). By applying the fuzzy–rough approach, uncertainty is included when making a decision, and it is possible to use linguistic values. The results obtained by the fuzzy–rough SWARA method showed that the range and price of electric vehicles have the greatest influence on the selection of an electric delivery vehicle. The results of applying the fuzzy–rough MARCOS method indicated that the Kangoo E-Tech Electric vehicle has the best characteristics according to experts’ estimates. These results were confirmed by validation and the application of sensitivity analysis. In urban logistics, the selection of an electric delivery vehicle helps to reduce the impact on the environment. By applying the fuzzy–rough approach, the decision-making problem is adjusted to the preferences of the decision makers who play a major role in purchasing a vehicle.

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“…However, there are some methods that use double normalization. Puska et al (2023) declare that the ranking of alternatives is more stable when implementing methods with double normalization than methods with one more time normalization. By this fact, researchers of this article make a decision to use a method for ranking with this property.…”

Section: Dncradismentioning

confidence: 99%

Investigating the Effects of Covid-19 on Tourism in the G7 Countries

Komasi,

Nemati,

Hashemkhani Zolfani

et al. 2024

Technological and Economic Development of Economy

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Natural and human-made crises can significantly impact the development of countries’ tourism industries. The susceptibility of countries to these crises depends on their policies, planning, and management in facing diverse challenges. This article aims to investigate the effects of the COVID-19 pandemic on the tourism industry in G7 countries by comparing rankings and positions on indices in 2016 and 2020. Data collected from the RANking COMparison (RANCOM), Proximity Indexed Value (PIV), and Double Normalization Compromise Ranking of Alternatives from Distance to Ideal Solution (DNCRADIS) models have been utilized for data analysis. The research findings indicate noticeable differences in using different models, as the rankings and positions of G7 countries for the years 2016 and 2020, except for two countries, the United States and France, have been different. The research results demonstrate that the COVID-19 crisis had significant impacts on the tourism industries of G7 countries. Countries like the United States, France, and the United Kingdom appear as leading nations in the tourism industry, while Japan and Canada faced challenges, and Germany and Italy experienced changes in their positions. Based on these results, officials and planners in the tourism industry of G7 countries can make appropriate decisions for the development and improvement of tourism under similar crisis conditions. Moreover, these findings can serve as a valuable guide for other countries in managing similar crises in the tourism industry.

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Extension of MEREC-CRADIS methods with double normalization-case study selection of electric cars

Cited by 22 publications

References 77 publications

A Multicriteria-Based Comparison of Electric Vehicles Using q-Rung Orthopair Fuzzy Numbers

A Multicriteria-Based Comparison of Electric Vehicles Using q-Rung Orthopair Fuzzy Numbers

Multi-Criteria Selection of Electric Delivery Vehicles Using Fuzzy–Rough Methods

Investigating the Effects of Covid-19 on Tourism in the G7 Countries

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