A Two-Phase Model for Personnel Selection Based on Multi-Type Fuzzy Information

A Weighted Bonferroni-OWA Operator Based Cumulative Belief Degree Approach to Personnel Selection Based on Automated Video Interview Assessment Data

Asynchronous Video Interviewing (AVI) is considered one of the most recent and promising innovations in the recruitment process. Using AVI in combination with AI-based technologies enables recruiters/employers to automate many of the tasks that are typically required for screening, assessing, and selecting candidates. In fact, the automated assessment and selection process is a complex and uncertain problem involving highly subjective, multiple interrelated criteria. In order to address these issues, an effective and practical approach is proposed that is able to transform, weight, combine, and rank automated AVI assessments obtained through AI technologies and machine learning. The suggested approach combines Cumulative Belief Structures with the Weighted Bonferroni-OWA operator, which allows (i) aggregating assessment scores obtained in different forms and scales; (ii) incorporating interrelationships between criteria into the analysis (iii) considering accuracies of the learning algorithms as weights of criteria; and (iv) weighting criteria objectively. The proposed approach ensures a completely data-driven and efficient approach to the personnel selection process. To justify the effectiveness and applicability of the suggested approach, an example case is presented in which the new approach is compared to classical MCDM techniques.

Application of the Choquet Integral: A Case Study on a Personnel Selection Problem

Personnel selection plays a decisive role in human resource management since it determines the input quality of personnel. One approach, fuzzy decision-making methods, has become popular in decision making for personnel selection, considering those methods provide a wide range of tools for dealing with uncertainty. Choquet integral is an aggregation operator, frequently used to unite interrelated information. Choquet integral, with respect to fuzzy, allows consideration of the phenomenon of dependence between criteria. In this paper, personnel selection was performed using the Choquet integral, based on a fuzzy measure. The problem of the evaluation of employees is performed with respect to the personal characteristics of the employees, task performance, employee–employee relationship approaches, and effectiveness of communication.

Fuzzification Technique for Candidate Rating and Selection

The traditional ways of candidate selection and recruitment are prone to subjectivity, imprecision and vagueness. With a view to achieving objective and precise selection and recruitment while keeping up with technological improvement and changes, this paper discusses a fuzzification-based technique for candidate rating and selection. The technique comprises a fuzzy logic component that is an extension of Boolean logic and used for establishing accurate selection process and precise solutions to multi-variable problems. There is a knowledge base component which forms the database of multi-level information and rule base which composes a set of if-then statements for decision making. Its inference engine applies a pre-defined procedure on input from the rule base and fuzzy logic interfaces for final recommendations. The proposed methodology performs pre-defined procedures that are based on some input sets which stores multi-level information derived from several pre-specified scores. Results from the implementation of the proposed technique established its practical function.

Model of Evaluation and Selection of Expert Group Members for Smart Cities, Green Transportation and Mobility: From Safe Times to Pandemic Times

This paper presents the development of technologies to support the decision-making of local government executives and smart city concept managers in selecting and evaluating the competencies of new members for advisory groups for solving problems that are implemented in safe times in individual areas or in crises, such as pandemics. The reason for developing effective urban transformation strategies and for the transparent selection of independent experts (non-politicians) for policymaking, decision-making, and implementation teams is not only the heterogeneity of smart city dimensions together with the necessary complexity and systems approach, but also the nature of the capacities and tools needed for smart city concepts. The innovative hybrid competency assessment model is based on fuzzy logic and a network for neuro-fuzzy assessment. It is a technological model for evaluating the competencies of specialists, taking into account the influence of human factors on the processes of personnel selection and system management. An innovative web platform named “Smart City Concept Personnel Selection” has been designed, which can be adapted to various users of municipalities or regional institutions for the transparent selection of qualified personnel for effective decision-making and the use of public funds during safe times or emergencies, such as the COVID-19 pandemic.