An extended picture fuzzy multicriteria group decision analysis with different weights: A case study of COVID-19 vaccine allocation.

The high contagion rates of COVID-19 and the limited amounts of vaccines forced public health authorities to develop vaccinations strategies for minimizing mortality, avoiding the collapse of health care infrastructure, and reducing their negative impacts to societies and economies. We propose a Multi Criteria Group Decision Making for prioritizing a set of COVID-19 vaccination alternatives, under a picture fuzzy environment, where the weights for Decisions Experts (DE) and criteria are unknown. A panel of six DEs assess six criteria for prioritizing four groups for vaccination. The weights for DE and criteria are handled in the form of fuzzy sets. Three types of weights are calculated: subjective, objective, and mixture weights. According to our results, three out of the six criteria hold 60% of the strategic importance: 1) allocation and distribution, 2) COVID-19 strains and 3) capabilities and infrastructures. However, persons with comorbidities became the group with the highest priority, followed by essential workers, women, and adults older than 40 years. Governments, decision makers, and policy makers can find rigorous scientific evidence for articulating effective vaccinations campaigns from this work, and contribute to minimize undesired outputs, such as high mortality rates or collapse of hospitals.

Assessment of green development level performance in G20 countries: A dynamic evaluation framework.

To achieve sustainable development goals and foster future prosperity globally, the promotion of green development is essential. Nevertheless, persistent regional disparities in green development among the G20 nations stem from differing economic and environmental conditions in dynamic contexts. In light of these conditions, it is essential for governments to evaluate their country's current green development performance in comparison with higher-performing nations and to learn from their experiences. This study introduces a new dynamic evaluation framework that integrates the importance of evaluation indicators using the dynamic entropy weighting method, a Dynamic Technique for Order Preference by Similarity to Ideal Solution method, and principal component analysis. This framework serves as a tool for G20 countries to assess their green development levels between 2015 and 2021. A key contribution of this study is the facilitation of the G20's ability to implement relevant reform measures and monitor green development progress in a dynamic environment. This framework can serve as a model for similar evaluations in other regions globally. The study's main findings indicate that G20 countries with high levels of green development possess strong economic foundations and prioritise the balanced development of their economies, societies, environments, and innovation.

Purchasing decisions on date palm fruits: A quantitative analysis of the Khalas cultivar.

This study examines the attributes of date palm fruits that influence consumer purchasing decisions and measures the attributes' relative importance weights for understanding consumption patterns relative to the cultivation areas. A case study was conducted for a selected date fruit, Khalas, which is cultivated in Saudi Arabia and ranked first in the world in exported dates. Our empirical investigation is based on utilizing a proposed quantitative analysis that integrated the entropy weighting method and binary logit models. With this survey design, 486 questionnaires were collected. Analysis results revealed a ranking list of preferred attributes, with size, mellowness, price, and color being the most valued. However, this ranking list fluctuates when different cultivated types of Khalas dates are available. The results also showed that consumption patterns may change in terms of preference index and shopping location. The paper concludes with a discussion of managerial implications, limitations, and future research directions.

Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns.

The pandemic caused by the spread of the SARS-CoV-2 virus forced governments around the world to impose lockdowns, which mostly involved restricting non-essential activities. Once the rate of infection is manageable, governments must implement strategies that reverse the negative effects of the lockdowns. A decision support system based on fuzzy theory and multi-criteria decision analysis principles is proposed to investigate the importance of a set of key indicators for post-COVID-19 reopening strategies. This system yields more reliable results because it considers the hesitation and experience of decision makers. By including 16 indicators that are utilized by international organizations for comparing, ranking, or investigating countries, our results suggest that governments and policy makers should focus their efforts on reducing violence, crime and unemployment. The provided methodology illustrates the suitability of decision science tools for tackling complex and unstructured problems, such as the COVID-19 pandemic. Governments, policy makers and stakeholders might find in this work scientific-based guidelines that facilitate complex decision-making processes.

Correction to: Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns.

[This corrects the article DOI: 10.1007/s11135-021-01129-3.].