English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

In this study, we propose a multicriteria group decision making (MCGDM)algorithm under uncertainty where data is collected as intervals. The proposedMCGDM algorithm aggregates the data, determines the optimal weights forcriteria and ranks alternatives with no further input. The intervals giveflexibility to experts in assessing alternatives against criteria and providean opportunity to gain maximum information. We also propose a novel method toaggregate expert judgements using cloud models. We introduce an experimentalapproach to check the validity of the aggregation method. After that, we usethe aggregation method for an MCGDM problem. Here, we find the optimal weightsfor each criterion by proposing a bilevel optimisation model. Then, we extendthe technique for order of preference by similarity to ideal solution (TOPSIS)for data based on cloud models to prioritise alternatives. As a result, thealgorithm can gain information from decision makers with different levels ofuncertainty and examine alternatives with no more information fromdecision-makers. The proposed MCGDM algorithm is implemented on a case study ofa cybersecurity problem to illustrate its feasibility and effectiveness. Theresults verify the robustness and validity of the proposed MCGDM usingsensitivity analysis and comparison with other existing algorithms.

Multicriteria Group Decision-Making Under Uncertainty Using Interval Data and Cloud Models