Evaluation of Human Resources in Transportation Companies Using Multi-Criteria Model for Ranking Alternatives by Defining Relations between Ideal and Anti-Ideal Alternative (RADERIA)

Vladimir Jakovljevic, Mališa Zizovic, Dragan Pamucar, Željko Stević and Miloljub Albijanic
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Vladimir Jakovljevic: School of Mathematics and Statistics, Faculty of Science, University of Sydney, Carslaw Building, Eastern Ave, Camperdown, NSW 2006, Australia
Mališa Zizovic: Faculty of Technical Sciences in Cacak, University of Kragujevac, Svetog Save 65, 32102 Cacak, Serbia
Dragan Pamucar: Department of Logistics, Military Academy, University of Defence in Belgrade, Pavla Jurisica Sturma 33, 11000 Belgrade, Serbia
Željko Stević: Faculty of Transport and Traffic Engineering, University of East Sarajevo, Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina
Miloljub Albijanic: FEFA Faculty, Metropolitan University, 11000 Belgrade, Serbia

Mathematics, 2021, vol. 9, issue 9, 1-25

Abstract: Multi-criteria decision-making methods (MCDM) represent a very powerful tool for making decisions in different areas. Making a rational and reliable decision, while respecting different factors, is a challenging and difficult task; MCDM models have a great impact on achieving this goal. In this paper, a new MCDM technique is presented—ranking alternatives by defining relations between the ideal and anti-ideal alternative (RADERIA), which was tested for the evaluation of human resources (HR) in a transportation company. The RADERIA model has three key advantages that recommend it for future use: (1) the RADERIA model has a new approach for data normalization that enables defining the normalization interval according to the judgments of a decision-maker; (2) an adaptive model for data normalization of the RADERIA model allows tough conversion into various forms of decreasing functions (linear, quadratic equation, etc.); and (3) the resistance of the RADERIA model to the rank reversal problem. Furthermore, in many simulations, the RADERIA method has shown stability when processing a larger number of datasets. This was also confirmed by a case study with 36 alternatives, as considered in this paper. The results and verification of the proposed new method were acquired through a comprehensive verification of the complexity of the results. The complexity of the results was executed through (1) comparison with four other multi-criteria methods, (2) checking the resistance of the RADERIA model to the rank reversal problem, and (3) the analysis of the impact of changes in the measurement scale on the ranking results.

Keywords: multi-criteria analysis; RADERIA; LBWA; rank reversal; human resources; transportation company (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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