Studies in Informatics and Control – ICI Bucharest
Edmundas Kazimieras ZAVADSKAS1*, Željko STEVIĆ2, Zenonas TURSKIS3, Milovan TOMAŠEVIĆ4
1 Vilnius Gediminas Technical University, Institute of Sustainable Construction,
Saulėtekio al. 11, LT-10223 Vilnius,Lithuania,
edmundas.zavadskas@vgtu.lt (*Corresponding author)
2 University of East Sarajevo, Faculty of Transport and Traffic Engineering,
Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina,
zeljkostevic88@yahoo.com or zeljko.stevic@sf.ues.rs.ba
3 Research Institute of Smart Building Technologies, Faculty of Civil Engineering,
Vilnius Gediminas Technical University, Lithuania,
zenonas.turskis@vgtu.lt
4 Faculty of Information Studies in Novo Mesto, Slovenia,
milovan.tomasevic@fis.unm.si
ABSTRACT: Multi-Criteria Decision-Making (MCDM) methods have a significant influence on decision making in a variety of strategic fields, including science, business, and real-life studies. These methods also effectively support researchers in solving the emerging issues that may be encountered during their research activity. This work introduces a new Evaluation method based on the Distance from the Average Solution in the Minkowski space (EDAS-M). The main contribution of this study is the EDAS-M based MCDM model for the evaluation of an autonomous vehicle. Besides, the CRITIC (Criteria Importance Through Intercriteria Correlation) was used to determine objective criteria weights. The EDAS-M method provides a modified extension of the conventional Evaluation method based on the Distance from the Average Solution (EDAS) method. Seven different MADM methods are used to compare problem-solving results. Namely, the EDAS, WASPAS (Weighted Aggregated Sum Product ASsessment), SAW (Simple Additive Weighting), ARAS (Additive Ratio ASsessment), TOPSIS (Technique for Order Preference by Similarity Ideal Solution), TOPSIS-M (TOPSIS Minkowski space) and MABAC (Multi-Attributive Border Approximation Area Comparison) techniques validate the stability of the results obtained by using the new method above mentioned. Sensitivity analysis reflects the dynamics of the influence of dynamic matrices. It showed a high correlation of positions with all applied approaches. This correlation has also been maintained in a dynamic environment.
KEYWORDS: EDAS, Minkowski space, EDAS-M, MCDM, Autonomous Vehicle, CRITIC.
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CITE THIS PAPER AS:
Edmundas Kazimieras ZAVADSKAS, Željko STEVIĆ, Zenonas TURSKIS, Milovan TOMAŠEVIĆ, A Novel Extended EDAS in Minkowski Space (EDAS-M) Method for Evaluating Autonomous Vehicles, Studies in Informatics and Control, ISSN 1220-1766, vol. 28(3), pp. 255-264, 2019. https://doi.org/10.24846/v28i3y201902