Multi-criteria Evaluation + Positional Ranking Approach for Candidate Selection in E-voting

The aim of the study is the application of multi-criteria evaluation methods for ranking of candidates in e-voting. Due to the potential to enhance the electoral efficiency in e-voting multiple criteria, such as personality traits, activity and reputation in social media, opinion followers on election area and so on for the selection of qualified personnel can be considered. In this case, the number of criteria excesses in the decision-making stage directed us to the use of a multi-criteria decision making model (MCDM). This paper proposes MCDM for weighted ranking of candidates in e-voting. Criteria for the candidates’ ranking and selection are determined and each voter uses the linguistic scales for the ranking of each candidate. Candidates’ ranking is evaluated according to all criteria. In a numerical study, it is provided the candidates’ evaluation on the base of selected criteria and ranked according to the importance of criteria. To assess the importance of the criteria and to evaluate the suitability of the candidates for each of the criteria, the voters use linguistic variables. In practice, the proposed model can use different evaluation scales for the selection of candidates in e-voting. The proposed model allows selecting a candidate with the competencies based on the criteria set out in the e-voting process and making more effective decisions.

Background: Multi-criteria decision analysis (MCDA) is a decision-making tool that can take into account multidimensional factors and enables comparison of (medical) technologies by combining individual criteria into one overall appraisal. The MCDA approach has slowly gained traction within Health Technology Assessment (HTA) and its elements are gradually being incorporated into HTA across Europe. Several groups of scientists have proposed MCDA approaches targeted toward orphan drugs and rare diseases by including criteria specific to rare diseases. The goal of this article is to provide an overview of the current state of knowledge and latest developments in the field of MCDA in HTA for orphan drugs, to review existing models, their design characteristics, as well as to identify opportunities for further model improvement.Methods: A systematic literature search was conducted in January 2018 using four databases: MEDLINE (Pubmed), EBSCO HOST, EMBASE, and Web of science to find publications related to use of MCDA in the rare disease field (keywords: MCDA/orphan drug/rare disease and synonyms). Identified MCDA models were analyzed, e.g., structure, criteria, scoring, and weighting methodology.Results: Two hundred and eleven publications were identified, of which 29 were included after removal of duplicates. 9 authors developed own MCDA models, 7 of which based on literature reviews intended to identify the most important and relevant decision criteria in the model. In 13 publications (8 models) weights were assigned to criteria based on stakeholder input. The most commonly chosen criteria for creation of the MCDA models were: comparative effectiveness/efficacy, the need for intervention, and disease severity. Some models have overlapping criteria, especially in the treatment cost and effectiveness areas.Conclusions: A range of MCDA models for HTA have been developed, each with a slightly different approach, focus, and complexity, including several that specifically target rare diseases and orphan drug appraisal. Models have slowly progressed over the years based on pilots, stakeholder input, sharing experiences and scientific publications. However, full consensus on model structure, criteria selection and weighting is still lacking. A simplification of the MCDA model approach may increase its acceptance. A multi-stakeholder discussion on fundamental design and implementation strategies for MCDA models would be beneficial to this end.

The complex and controversial task of selecting a dam site in a river basin can be successfully achieved using science-informed multi-criteria decision-making (MCDM) techniques. In this paper, we describe the application of the group fuzzy TOPSIS model for optimal ranking of the case study of Kandoleh dam sites in Kermanshah province, Iran, involving 18 input criteria. In this study, decision-making committee was made up of 20 involved decision makers. The comments of four non-biased, external experts in dam site selection were also used. The triangular fuzzy numbers were used to apply experts’ opinions on the selection criteria. In total, four alternative sites were assessed based on the technical, economic, social and environmental considerations and the data were analyzed using fuzzy TOPSIS MCDM model. Ranking results were compared with multi-criteria decision-making models, including the ELimination and Choice Expressing the REality and simple additive weighting. This logical, open and transparent framework provides a science-informed decision-making approach for complex problems such as optimal dam site selection. Finally, using sensitivity analysis, local studies and group discussions, we demonstrated the multiple benefits of the proposed novel method for a science-informed, open and transparent method for optimal ranking of the dam site candidates.

From 1820 4 times 4 binary matrices, 253 binary preprocessing matrices are designed for double space-time transmit diversity (DSTTD) systems with two 2 times 2 space-time block code (STBC) encoders. Among them, six matrices yielding the highest average minimum-post-processing SNR are proposed from numerical experimentation under uncorrelated channel conditions. The proposed preprocessing performs a superposition of the first and second (second and first) STBC symbols and a selection of two transmit antennas. Simulation results show that the proposed method provides 1.7 dB (1.8 dB) SNR improvement in uncorrelated (correlated) channel environment at 10-3 bit error rate over the conventional antenna shuffling method.

Decision-making tools have become a prominent methodology in watershed management for many years due to the complexity of environmental systems and requirement for multi-disciplinary expertise. Multi-Criteria Decision Analysis (MCDA) is a systematic methodology, which combines hierarchical structures of a problem and priorities for the alternatives in many fields. This study reviews MCDA applications in pollution risk assessment in the abiotic environments of watersheds for multi-pollutants. Over 80 papers published between 2000 and 2021 are identified in three categories of the Web of Science Core Collection database: “Environmental Sciences”, “Environmental Studies”, and “Water Resources”. The publications are further classified according to different environmental compartments: surface water, groundwater, and soil to investigate MCDA applications in these matrices. Finally, the distribution of the publications according to contaminants and MCDA methods used are also examined. The results reveal that the number of the studies focusing on pollution risk assessment within watersheds has been significantly increasing, especially over the last decade. However, there are still limited MCDA applications linking environmental compartments. Despite several MCDA studies focusing on the vulnerability of watersheds to multiple pollutants, studies related with emerging pollutants are scarce. Moreover, compared to non-point source pollution, studies adopting MCDA to investigate pollutant concentrations coming from point sources are relatively few. According to the overall distributions of MCDA methods, Analytic Hierarchy Process, a commonly found method in the literature that adopts a technique of pairwise comparison to prioritize criteria of prominence, dominates 34% of the publications.

During the early years of their establishment, information service providers (ISPs) were viewed simply as 'suppliers of information'. Nowadays, however, the commercialization of the Internet coupled with the progressive advancements in information technology (IT) have seen the business paradigm evolve into so-called electronic commerce (EC), with the result of an ever-increasing demand from enterprises for new types of information services (IS) to facilitate and coordinate their daily operations. Now, as the role of the ISP is continually changing, and the content of IS becomes ever broader, an effective means of the assessment of ISPs has become a critical issue in any enterprise's decision on the eventual selection of a competent ISP. Hence, the purpose of this paper is to establish a multiple criteria decision-making (MCDM) framework for assessing and evaluating ISPs. Through a review of the literature of previous works on relevant topics, this paper begins by proposing a hierarchy structure for the problems involved in ISP assessment. Based on the hierarchy structure, both additive (analytic hierarchy process) and non-additive (fuzzy measure and fuzzy integral) MCDM methods are used in this study to show the relative importance of the selection criteria and the dimensions of those criteria. Ten real cases are then employed as illustrative alternatives to demonstrate the synthesis decisions under the application of both MCDM methods, so as to show the applicability of the framework. Finally, we find that there is a distinction between the results of alternative rankings produced by the two MCDM methods, thus the ten alternatives are categorized into four groups.

The purpose of this study was to examine the relationship between both the demographic characteristics and the personality type of females in administrative positions in the West Virginia public school systems, and the extent to which those administrators expressed satisfaction in their positions. The total population of 324 female public school administrators in West Virginia were surveyed. Each participant was mailed a survey packet containing a cover letter, a demographics sheet and two questionnaires. The demographics sheet, a Survey of West Virginia Female Public School Administrators, was adapted from previous surveys (Hawthorne, 1985; Hutchens, 1990; Mauter, 1980). The questionnaires used were the Myers-Briggs Type Indicator - Form G (1977), and the Mohrman-Cooke-Mohrman Job Satisfaction Scale (Mohrman, Cooke, Mohrman, Duncan & Zaltman 1977). The total return rate was 173 (53%). The total usable returns were 168 (52%). The data were analyzed using the Statistical Analysis Systems. Analyses of Variance were utilized to test the hypotheses. An alpha level of.05 was used to determine significance. Analyses of the data resulted in the following findings. No statistically significant relationship was found with regard to age, years educational experience, years administrative experience, educational level, ethnicity, marital status, position or personality and job satisfaction. These findings do not support previous findings in the literature. Administrators who had served the fewest years in their current position expressed a higher measure of extrinsic job satisfaction than administrators who had served longer in their current position. Ancillary findings of the study indicated that a statistically significant difference existed in the relationship between the support the female administrator had received from friends, family and colleagues, and the amount of job satisfaction expressed by the female administrator. Apparently the higher the amount of support received by the administrator the greater the degree of job satisfaction she experienced. This study offers little support for the literature on which it was based. The study does, however, reveal a potentially important connection between job satisfaction and the personal and professional relationships of the female administrator.

This study addresses the challenges employers face in screening the large number of resumes received for job positions. We aim to ensure fair evaluation of candidates, reduce bias, and increase the efficiency of the candidate evaluation process by automating the resume screening process. The proposed system uses NLP techniques to extract the relevant competencies from resumes, focusing on the key skills required for specific positions. The competency sets taken for the positions were used. A case study was conducted for 123 job positions. The extracted competencies are matched to predefined skill sets associated with various job positions using Jaccard Similarity. This method provides a similarity score that helps rank candidates by comparing the presence or absence of words in the candidate's resume to the required competencies. This NLP-based system offers significant benefits such as saving time and resources, increasing accuracy in candidate selection, and reducing bias by focusing only on competencies. The system's integration with LinkedIn increases its usefulness by allowing seamless import and analysis of resumes. Overall, this study demonstrates the transformative potential of NLP in optimizing the resume screening process by providing a scalable, efficient, and unbiased solution for large organizations.

The recruitment of permanent Lecturers for higher education is a major challenge for all universities. Given the absence of defined hiring criteria, this article will suggest some and support the recruitment committees at the Ministry of Higher Education in Madagascar. Three methods will be implemented in a decision support system. Each recruitment committee member will assess the importance of each criterion. The Rank Sum weight method is used to determine the weightings of the criteria. The Combined Compromise for Ideal Solution (CoCoFISo) method ranks the candidates. The Minimum of rank (MIRA) method will aggregate the several rankings of candidates. Three fundamental criteria were selected: experience in university teaching, time elapsed since obtaining the teacher's opinion, and number of research publications. These criteria were assessed according to their importance by six members of the recruitment committee. The weights assigned to the criteria vary from one criterion to another and from one decision-maker to another. Applying these weights to the CoCoFISo method resulted in six rankings of the candidates. The rankings of the candidates varied from one decision-maker to another, except for the candidate in first place and the candidate in last place. The MIRA method successfully merged these six rankings into a final ranking for each candidate. The Kendall rank correlation coefficient between the MIRA method and the six rankings obtained using the CoCoFISo method varies between 0.80 and 0.97, thus confirming the validity of the rankings. This new technique will solve the challenge of hiring temporary lecturers as permanent lecturers in Madagascar universities, thanks to the transparency of the process, and will assist the Ministry of Higher Education in its decision-making.

Multi-criteria Decision Making (MCDM) involves evaluating alternatives based on a comprehensive set of conflicting criteria, often requiring the involvement of varied decision makers. This has led to the emergence of stakeholder-based multi-criteria group decision-making (MCGDM) frameworks. However, traditional MCGDM frequently overlooks the interactions and trade-offs among different actors and criteria. The Multi-actor Multi-criteria Analysis (MAMCA), developed in 2000, provides a transparent decision-making process explicitly considering these interrelationships. It allows diverse stakeholder groups to represent their priorities, thereby enhancing their understanding of their own and others’ positions. Over the past two decades, MAMCA has seen a significant rise in popularity and has been widely applied in diverse contexts, proving valuable as both a decision-making and stakeholder engagement tool. However, our analysis of publications on cases in which MAMCA was applied over the years shows that considerable variation exists in the overall process approach, contingent upon the specific goals and context. To address these variations, this paper proposes a modularized MAMCA structure, complemented by systematic application guidelines, to aid future users in navigating the process steps and identifying the most suitable methods for each step. Additionally, future research directions are suggested for potential enhancements to the MAMCA framework by integrating varied methodologies.

Personnel selection is a challenging problem for any organization. The success of a project is determined by the human resources that handle the project. To make better personnel selections, researchers have adopted multi-criteria decision-making (MCDM) approaches. Among these, fuzzy-based MCDM methods are most frequently used, as they handle vagueness and imprecision better. Intuitionistic fuzzy set (IFS) is a popular MCDM context which provides degree of membership and non-membership for preference elicitation. In this work, we propose a novel decision-making framework that consists of two stages. In the first stage, a new extension to the popular VIKOR method is presented under IFS context. The positive and negative ideal solutions are determined, and VIKOR parameters are calculated using transformation procedure. The proposed method combines the strength of both interval-valued fuzzy set and IFS that is more effective in handling vagueness with a simple formulation setup. In the second stage, a personnel selection problem is used to validate the proposed framework. Finally, the superiority and weakness of the proposed framework are discussed by comparison with other methods.

This paper proposes an interval hesitant fuzzy multi-criteria decision-making model based on improved projection measure, aiming to solve the non-standardization problem in traditional methods. By improving the projection measure and ensuring that its value is in the interval [0,1], the stability and accuracy of the decision results are improved. Compared with the existing methods, this method shows stronger robustness in multi-criteria decision-making, especially in complex decision-making scenarios. The effectiveness of the model is verified by example analysis, which provides a new theoretical tool for multi-criteria comprehensive decision-making.

In manufacturing firms, there is a critical need for proper maintenance of manufacturing facilities. The maintenance process enhances customer satisfaction and reliability of the products, and increases the profit of the manufacturer. Therefore, a proper maintenance policy selection is a critical issue for manufacturers, as an inefficient maintenance policy affects not only the direct cost of the firm but also the other aspects. In the present study, maintenance policy selection at the level of the firm rather than the equipment level is shown, and for selection various criteria have been identified. The presented work not only provides the best alternatives but also provides an alternative ranking, which facilitates decision-makers in choosing alternatives according to their constraints. These selection criteria are different in nature, as some give a crisp value, whereas others are defined in linguistic terms. To select the appropriate maintenance policy, a distance-based fuzzy multicriteria decision-making (MCDM) approach has been employed. The proposed method provides the means for integrating the economic figure of merit with the strategic performance variables. The MCDM approach is efficient in incorporating data, in the form of linguistic variables, triangular fuzzy numbers, and crisp numbers, into the evaluation process of maintenance policy alternatives. A comprehensive example illustrates the application of the distance-based fuzzy MCDM approach.

We compare the Condorcet Efficiencies of the plurality rule, the negative plurality rule, and the Borda rule to examine what might be gained by using a voting rule that requires candidate rankings. Previous analyses have considered (1) voting situations for which the three rules determine different candidates as winners (and the Condorcet Efficiencies might actually differ across the three rules) as well as (2) refined measures of Group Mutual Coherence that assess the presence of underlying models of rational behavior that governs voter preferences. In this paper, we present the final step of this particular line of analysis by analyzing (1) and (2) simultaneously. We conclude that there is a considerable benefit on the basis of Condorcet Efficiency from asking voters to submit candidate rankings in three-candidate elections.

<p>Personnel selection determines the input quality of personnel, therefore, plays a decisive role in human resource management. Personnel selection problem has been studied extensively. Selecting the best personnel among many alternatives is a multi-criteria decision making (MCDM) problem. The necessity of dealing with uncertainty in real world problems has been a long-term research challenge that has originated different methodologies and theories. Fuzzy decision making along with their extensions have provided a wide range of tools that are able to deal with uncertainty in different types of problems. Fuzzy decision making methods have become increasingly popular in decision making for personnel selection. Various decision making approaches have been proposed to solve the problem. This paper presents a comprehensive literature review of the applying Fuzzy decision making techniques in personnel selection problem.</p>