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In contemporary military contexts, the determination of an optimal course of action (COA) in combat operations emerges as a critical challenge. This study delineates a decision support methodology for military applications, employing sophisticated decision analysis techniques. The initial phase entails the identification of pivotal criteria for assessing and ranking COAs, followed by the assignment of weight coefficients to each criterion via the full consistency method (FUCOM). Subsequently, the Einstein weighted arithmetic average operator (EWAA) was utilized for the aggregation of expert opinions, ensuring a consensual evaluation of these criteria and culminating in the final values of their weight coefficients. The ensuing phase focuses on the selection of an optimal COA, incorporating the grey complex proportional assessment (COPRAS-G) method. This method addresses uncertainties and varying criterion values. Expert ratings were again aggregated using the EWAA operator. The findings from this phase are designed to provide military commanders with precise, data-driven guidance for decision-making. To validate and verify the stability of the proposed model, a series of tests were conducted, including a rank reversal test, sensitivity analysis regarding changes in weight coefficients, and a comparative analysis with alternative methods. These assessments uniformly indicated the model's consistency, stability, and validity as a military decision support tool. Emphasizing a high degree of confidence in COA selection, the methodology advocated herein is applicable to decision-making processes in the planning and execution of military operations. The uniform application of professional terms, consistent with the broader context of this research, ensures clarity and coherence in its presentation. The approach outlined in this study stands as a testament to rigorous analytical methodologies in the realm of military strategic planning, offering a robust framework for decision-making under conditions of uncertainty and complexity.
Tešić et al. (Thu,) studied this question.