Beyond Multicriteria Ranking Problems: The Case of PROMETHEE

2019 ◽  
pp. 95-114 ◽  
Author(s):  
Yves De Smet
Keyword(s):  
Ranking Problems

ADDITIVE-VETO MODELS FOR CHOICE AND RANKING MULTICRITERIA DECISION PROBLEMS

2013 ◽  
Vol 30 (06) ◽  
pp. 1350026 ◽  
Author(s):  
ADIEL TEIXEIRA DE ALMEIDA
Keyword(s):  
High Performance ◽  
Additive Model ◽  
Additive Models ◽  
Numerical Application ◽  
Multicriteria Decision ◽  
Decision Methods ◽  
Overall Evaluation ◽  
Low Performance ◽  
Ranking Problems ◽  
Multicriteria Decision Problems

Using additive models for aggregation of criteria is an important procedure in many multicriteria decision methods. This compensatory approach, which scores the alternatives straightforwardly, may have significant drawbacks. For instance, the Decision Maker (DM) may prefer not to select alternatives which have a very low performance in whatever criterion. In contrast, such an alternative may have the best overall evaluation, since the additive model may compensate this low performance in one of the criteria as a result of high performance in other criteria. Thus, additive-veto models are proposed with a view to considering the possibility of vetoing alternatives in such situations, particularly for choice and ranking problems. A numerical application illustrates the use of such models, with a detailed discussion related to real practical problems. Moreover, the results obtained from a numerical simulation show that it is not so rare for a veto of the best alternative to occur in the additive model. This is of considerable relevance depending on the DM's preference structure.

scholarly journals Incorporation of the Method of Ranking the Hazards of Abandoned Mine Entries into a Rule-Based Expert System

Minerals ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 600 ◽  
Author(s):  
Mhlongo ◽  
Amponsah-Dacosta ◽  
Kadyamatimba
Keyword(s):  
Expert System ◽  
Limpopo Province ◽  
Abandoned Mines ◽  
Abandoned Mine ◽  
Production Rules ◽  
Web Based ◽  
Rule Based ◽  
Ranking Problems ◽  
The Web

The work of quantifying the problems of abandoned mines is the first step towards the rehabilitation of these mines. As the result, in all countries that have many abandoned mines, researchers and different organizations have been making efforts to develop decision-making tools, methods, and techniques for rehabilitation of abandoned mines. This paper describes the work conducted to incorporate the method for ranking the problems of abandoned mine entries into a rule-based expert system. This is done using the web-based expert system platform provided by expert system (ES)-Builder Shell. The ES is tested by applying it to the case study of the problems of abandoned mine entries in the areas of Giyani and Musina, Limpopo Province of South Africa. This paper gives details of the procedure followed in creating the production rules of the ES for ranking problems of abandoned mine entries (ES-RAME), its attributes, and the results of its application to the selected case study. The use of the ES-RAME is found to be important for setting the objectives and priorities of the rehabilitation of abandoned mine entries. In addition, the incorporation of the ranking method into the expert system ensured that the procedure of the tanking method is clearly communicated and preserved as the rules of the ES. The expert system also has the advantages of being consistent in its guidance, and it gives the user an opportunity to go through the ranking process of the system using any possible fictitious information; this gives the user a feel for the ranking process and the data required when using the ES-RAME.

Discovering Consensus Preferences Visually Based on Gower Plots

2018 ◽  
Vol 17 (03) ◽  
pp. 741-761
Author(s):  
Li-Ching Ma
Keyword(s):  
Group Consensus ◽  
Visual Aids ◽  
Ranking List ◽  
Consensus Sequences ◽  
Group Ranking ◽  
Data Mining Approach ◽  
Search Processes ◽  
Ranking Problems ◽  
Visually Based ◽  
Made In

Group-ranking problems are widely encountered decision problems which combine personal preferences to form an integrated group priority; however, providing support to solve group-ranking problems is difficult because each person has his/her own viewpoint regarding how such decisions should be made. In addition, many researchers have shown that visual aids are useful in helping users comprehend decision backgrounds. Therefore, determining how to support the group-ranking process and providing visual aids is an important issue. This study proposes a novel graphical approach to discover group consensus sequences. First, a counting-based data mining approach is constructed to discover a consensus preference matrix. Second, an ordinal Gower plot can be drawn whereby group consensus sequences can be directly observed. Unlike previous methods, the proposed approach can discover group consensus sequences without involving tedious candidate generation and exhaustive search processes, derive a total ranking list, as well as provide visual aids to users.

Aggregating the results of benevolent and aggressive models by the CRITIC method for ranking of decision-making units: A case study on seven biomass fuel briquettes generated from agricultural waste

2021 ◽  
pp. 79-92
Author(s):  
Narong Wichapa ◽  
Porntep Khokhajaikiat ◽  
Kumpanat Chaiphet
Keyword(s):  
Decision Making ◽  
Agricultural Waste ◽  
Ranking Method ◽  
Data Envelopment ◽  
Decision Matrix ◽  
Ranking Models ◽  
Biomass Materials ◽  
Ranking Problems ◽  
Decision Making Units

The ranking of decision-making units (DMUs) is one of the main issues in data envelopment analysis (DEA). Hence, many different ranking models have been proposed. However, each of these ranking models may produce different ranking results for similar problems. Therefore, it is wise to try different ranking models and aggregate the results of each ranking model that provides more reliable results in solving the ranking problems. In this paper, a novel ranking method (Aggregating the results of aggressive and benevolent models) based on the CRITIC method is proposed. To prove the applicability of the proposed ranking method, it is examined in three numerical examples, six nursing homes, fourteen international passenger airlines and seven biomass materials for processing into fuel briquettes. First, benevolent and aggressive models were used to calculate the efficiency rating for each DMU. As a result, the decision matrix was generated. In the decision matrix, the results of benevolent and aggressive models were viewed as criteria and DMUs were viewed as alternatives. Then, the weights of each criterion were generated by the CRITIC method. Finally, each DMU was ranked. In a comparative analysis, the proposed method can lead to achieving a more reliable decision than the method which is based on a stand-alone method.

On the Borda-Kendall Consensus Method for Priority Ranking Problems

1982 ◽  
Vol 28 (6) ◽  
pp. 621-637 ◽  
Author(s):  
Wade D. Cook ◽  
Lawrence M. Seiford
Keyword(s):  
Consensus Method ◽  
Priority Ranking ◽  
Ranking Problems

Accurate and Interpretable Factorization Machines

2019 ◽  
Vol 33 ◽  
pp. 4139-4146 ◽  
Author(s):  
Liang Lan ◽  
Yu Geng
Keyword(s):  
Scoring System ◽  
Time Complexity ◽  
Feature Representation ◽  
Feature Mapping ◽  
Feature Interactions ◽  
Proposed Model ◽  
Novel Method ◽  
Ranking Problems ◽  
New Feature ◽  
Low Dimensional

Factorization Machines (FMs), a general predictor that can efficiently model high-order feature interactions, have been widely used for regression, classification and ranking problems. However, despite many successful applications of FMs, there are two main limitations of FMs: (1) FMs consider feature interactions among input features by using only polynomial expansion which fail to capture complex nonlinear patterns in data. (2) Existing FMs do not provide interpretable prediction to users. In this paper, we present a novel method named Subspace Encoding Factorization Machines (SEFM) to overcome these two limitations by using non-parametric subspace feature mapping. Due to the high sparsity of new feature representation, our proposed method achieves the same time complexity as the standard FMs but can capture more complex nonlinear patterns. Moreover, since the prediction score of our proposed model for a sample is a sum of contribution scores of the bins and grid cells that this sample lies in low-dimensional subspaces, it works similar like a scoring system which only involves data binning and score addition. Therefore, our proposed method naturally provides interpretable prediction. Our experimental results demonstrate that our proposed method efficiently provides accurate and interpretable prediction.

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