MCDM model for the selection of network planning techniques in the army for the purposes of performing engineering works when overcoming water obstacles

Overcoming water obstacles is a demanding combat action that requires serious planning under conditions of uncertainty and risk. Choosing a planning method for the implementation of engineering works in the army, as well as how to choose them, has always been a challenge that engineering officers have faced. In this paper, for the selection of the network planning technique, the use of the multi-criteria decision-making (MCDM) model, which contains the methods Logarithm Methodology of Additive Weights (LMAW) and Grey Operational Competitiveness Rating (OCRA), as well as the Einstein weighted arithmetic average (EWAA) operator for aggregating expert opinions, is presented. The LMAW method was used to define the weights of the criteria, while the Grey OCRA method was used to select the optimal planning technique. The Event Chain Methodology (ECM) was identified as the most suitable method for planning the engineering works in question, while the Critical Path Method (CPM) and Precedence Diagramming Method (PDM) are also suitable. In order to check the consistency and validation of the obtained results, a sensitivity analysis to changes in criteria weights and a comparative analysis were performed, where the results were compared with four other MCDM methods in a grey environment. The results of the analyses indicate that the model provides consistent and valid results.

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Publication Details

Journal
Journal of Decision Analytics and Intelligent Computing
Published
2025-06-06
DOI
https://doi.org/10.31181/jdaic10006062025t
Citations
3
Primary Topic
Advanced Data Processing Techniques
Type
article
Field-Weighted Citation Impact
3.64
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article

MCDM model for the selection of network planning techniques in the army for the purposes of performing engineering works when overcoming water obstacles

Boža Miljković, Dragan Pamučar, Duško Tešić, Adis Puška et al.
3 citations
Journal of Decision Analytics and Intelligent Computing
Advanced Data Processing Techniques
3.64
article

MCDM model for the selection of network planning techniques in the army for the purposes of performing engineering works when overcoming water obstacles

Boža Miljković, Dragan Pamučar, Duško Tešić, Adis Puška, Darko Božanić
article en
3 citations

Abstract

Overcoming water obstacles is a demanding combat action that requires serious planning under conditions of uncertainty and risk. Choosing a planning method for the implementation of engineering works in the army, as well as how to choose them, has always been a challenge that engineering officers have faced. In this paper, for the selection of the network planning technique, the use of the multi-criteria decision-making (MCDM) model, which contains the methods Logarithm Methodology of Additive Weights (LMAW) and Grey Operational Competitiveness Rating (OCRA), as well as the Einstein weighted arithmetic average (EWAA) operator for aggregating expert opinions, is presented. The LMAW method was used to define the weights of the criteria, while the Grey OCRA method was used to select the optimal planning technique. The Event Chain Methodology (ECM) was identified as the most suitable method for planning the engineering works in question, while the Critical Path Method (CPM) and Precedence Diagramming Method (PDM) are also suitable. In order to check the consistency and validation of the obtained results, a sensitivity analysis to changes in criteria weights and a comparative analysis were performed, where the results were compared with four other MCDM methods in a grey environment. The results of the analyses indicate that the model provides consistent and valid results.

Journal of Decision Analytics and Intelligent ComputingVol. 5(1)
University of Novi Sad (RS), Institute of Metrology of Bosnia and Herzegovina (BA), University of Defence (RS), Széchenyi István University (HU), Yuan Ze University (TW)
Openalex Percentile: Top 7%
Advanced Data Processing Techniques
3.64
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