Application of the DIBR II-fuzzy MABAC model for shooting stance selection
DOI:
https://doi.org/10.31181/jdaic10024112025aKeywords:
Defining Interrelationships Between Ranked criteria II (DIBR II), Multi-Attributive Border Approximation Area Comparison (MABAC), fuzzy number, shooting stancesAbstract
The paper introduces a hybrid multi-criteria decision-making (MCDM) model that combines DIBR II and fuzzy MABAC methods to select an optimal shooting stance. This study focuses on choosing the optimal combat shooting stance during tactical operations, based on expert-defined criteria that affect target engagement effectiveness. The objective is to develop a decision-support model based on MCDM techniques to assist either the shooter in selecting the most effective stance or the commander in issuing an appropriate order. The model aims to reduce the time required to adopt the stance before engagement and lower the risk of poor decisions that could harm operational effectiveness. These methods were combined for several reasons. The DIBR II method is used to assign weights to the criteria because it offers a simple way to calculate them. It can be easily integrated into the ranking process, providing a more objective evaluation and reducing the subjectivity seen in traditional methods. It is also user-friendly for experts solving problems. When uncertainty is low, crisp (real) numbers are used; when uncertainty is higher, fuzzy numbers are applied, which is relevant here. Many factors contribute to uncertainty, including weapon type, shooter training, the external environment, exposure to enemy action, and the area the shooter can see and target. Therefore, using fuzzy numbers, especially simple triangular ones, is justified. The fuzzy MABAC method is then used to identify the most suitable alternative among six possible shooting stances for small-arms combat operations. The MABAC method was chosen because it is a modern approach with a limited number of steps and because it identifies the alternative closest to the ideal solution within the upper or lower approximation boundary. Sensitivity analysis was conducted by varying the criterion with the highest weighting coefficient, and the ranking's validity was confirmed through scenario testing using the Spearman correlation coefficient.
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