Threshold-Based Value-Driven Method to Support Consensus Reaching in Multicriteria Group Sorting Problems
A Minimum Adjustment Perspective
Bibliographic Data
| ID | 22107785 |
|---|---|
| Authors | Zhuolin Li (0000-0002-0535-0945, Dalian University of Technology), Zhen Zhang (0000-0003-4317-9701, Dalian University of Technology) |
| Year | 2024 |
| Volume | 11 |
| Issue | 1 |
| Pages | 1230-1243 |
| Publication date | 2024-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3251351 |
| OpenAlex | W4323767310 |
| Language | EN |
| Citations received | 3 |
| References cited | 51 |
In multicriteria group decision-making (MCGDM) problems, there exist the situations that alternatives need to be assigned to several predefined ordered categories rather than be ranked from the most preferred to the least preferred, which is called multicriteria group sorting problems. To address multicriteria group sorting problems, it is necessary to implement a consensus reaching process to fully consider the opinion of each decision maker and reduce the conflict among them. To do so, this article proposes a consensus reaching model for multicriteria group sorting problems based on the threshold-based value-driven sorting method from the perspective of minimum adjustment. Specifically, we first define the consensus measure to calculate the agreement degree among decision makers by considering the ordinal information and cardinal information at the same time. On this basis, we construct a minimum adjustment optimization model in terms of the threshold-based value-driven sorting method to assist decision makers in modifying their decision matrices and further promote consensus. Followed by this, an optimization model that aims to minimize the distance between all decision makers and the group with respect to alternatives’ comprehensive values and sorting results is developed to determine the group sorting result for alternatives. Moreover, a numerical application of urban park management, some sensitivity analysis, and simulation experiments are provided to justify the proposed method. Experimental results reveal that the proposed method is effective in promoting consensus
Algorithm · Mathematical optimization · Physics · Sorting · Statistics · Bayesian Modeling and Causal Inference · Computer Science · Fuzzy Systems and Optimization · Mathematics · Multi-Criteria Decision Making · Artificial Intelligence
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| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |