Zamali Tarmudi

New computation approach for fuzzy group decision-making

This paper proposes a new method of using the so-called ‘equilibrium linguistic preference relations’ for group evaluation purposes. The theoretical background is briefly discussed in the early stages; followed by the discussion on the modified linguistic geometric averaging operator to facilitate the proposed computational algorithms. A hypothetical example was employed to demonstrate the suitability and practicability of the proposed method. It was found that the proposed method can reduce the uncertainty of the incomplete initial information that is able to generate more precise and comprehensive results in the evaluation process.

A fuzzy Delphi method to rank alternatives for industry selection

The aim of this study is to determine the ranking of industry alternatives using individual investors’ perceptions. It focuses on the study of portfolio investments based on individual investors’ perceptions on industry selection in unfavorable market conditions. This paper utilises the Fuzzy Delphi Method (FDM) to identify and rank the best performing industry options. The method is equipped with three levels of confidence - Very Optimistic, Neutral, and Very Pessimistic - which are rarely explored using traditional methods. An empirical analysis was carried out using data adopted from a case study in Turkey to demonstrate the feasibility and suitability of the proposed calculations. Based on the results of the calculation, this method, which has clear procedures, shows great potential in dealing with the complexities in assessing investors’ perceptions for the purpose of making industry selection decisions. In addition, it offers investors more confidence in making decisions as it is equipped with three levels of confidence which function as assistance tools in the decision-making process.The aim of this study is to determine the ranking of industry alternatives using individual investors’ perceptions. It focuses on the study of portfolio investments based on individual investors’ perceptions on industry selection in unfavorable market conditions. This paper utilises the Fuzzy Delphi Method (FDM) to identify and rank the best performing industry options. The method is equipped with three levels of confidence - Very Optimistic, Neutral, and Very Pessimistic - which are rarely explored using traditional methods. An empirical analysis was carried out using data adopted from a case study in Turkey to demonstrate the feasibility and suitability of the proposed calculations. Based on the results of the calculation, this method, which has clear procedures, shows great potential in dealing with the complexities in assessing investors’ perceptions for the purpose of making industry selection decisions. In addition, it offers investors more confidence in making decisions as it is equipped with three...

Simple Hierarchy Analysis-Based Linguistic Weights to Effective Teaching Evaluation

Abstract: It has been observed that most of the criteria considered for gauging effective teaching for the lecturers’ evaluation process are naturally imprecise, vague, qualitative, and involve varied input datasets. Besides that, these criteria are usually determined by the universities themselves without considering the perspective of their students. Thus, this study proposes that a lecturer’s evaluation should also be investigated based on the students’ perspectives. The study was equipped with the appropriate linguistic weights to cope with the vagueness of the input datasets using the so-called Simple Hierarchy Analysis (SHA). A numerical example was employed to demonstrate the applicability of the proposed method. Based on the result of the calculation, the method is shown to have great potential in dealing with the complexities in assessing lecturers’ effectiveness in teaching. It offers a versatile judgment, has a clear procedures and is successful in dealing with both perspectives (i.e., students and universities) which are diversified and too ill-defined in terms of input criterion. With these characteristics, the proposed method can assist the universities’ decision makers (DM’s) in conducting the evaluation process in a simple and systematic manner.

Fuzzy simple hierarchy analysis-based linguistic hedges to midsize luxury sporty utility vechicle selection

The aim of this paper is to proposed the Fuzzy Simple Hierarchy Analysis using linguistic weight to deal the ambiguity or vague multi-criteria datasets environment in selection process. It focuses on how the vague multiple datasets evaluated based on fuzzy linguistic approaches. The method was equipped with the linguistic hedge to cope this issue during the evaluation process. The selection of midsize luxury Sporty Utility Vehicles (SUVs) has been adopted to show the applicability and practicality of the proposed method. Based on the analysis results it was found that the method is highly beneficial for the buyers in the sense that it can distinguish clearly for every single judgment made during the selection process. It also offers a unique advantage and successful dealing with the diversified and too ill-defined in terms of input data criterion. Thus, the method can facilitate the potential buyers to make their decision in a simple and systematic manner.

Validating fuzzy decision model using risk and confidence analysis

This paper proposes two validation analyses, namely the risk attitudes and level of confidence for fuzzy multi-criteria decision-making model. Risk attitudes are employed to test the outcomes in dealing with the different decision makers attitude towards risk and uncertainty. Conversely, confidence analysis are used to test how confidence (i.e., pessimistic, moderate, optimistic, etc.) decision makers make a decision which respect to the difference situation and condition. A case study related to the municipal solid waste disposal decision in the state of Selangor and Kuala Lumpur Federal Territory was used to demonstrate the feasibility of the proposed analysis. The result shows that the proposed analysis can improve the relevance and acceptability of the model. Thus, more confident decision and better solution can be derived concurrently in decision-making process.