권오정 교수

Abstract In this paper, we consider a new weapon‐target allocation problem with the objective of minimizing the overall firing cost. The problem is formulated as a nonlinear integer programming model, but it can be transformed into a linear integer programming model. We present a branch‐and‐price algorithm for the problem employing the disaggregated formulation, which has exponentially many columns denoting the feasible allocations of weapon systems to each target. A greedy‐style heuristic is us

Drones have become integral in modern warfare, and their evolution towards greater autonomy is inevitable. This study explores the trajectory towards intelligent, minimally human-dependent military drones, detailing the necessary technological advancements. We simulate drone reconnaissance operations to identify and analyze emergent challenges. This study delves into various technologies critical for enhancing drone intelligence, with a focus on reinforcement learning based on object detection,

In this paper, we consider a new weapon–target allocation problem with the objective of minimizing the overall firing cost. The problem is formulated as a nonlinear integer programming model. We applied Lagrangian relaxation and a branch-and-bound method to the problem after transforming the nonlinear constraints into linear ones. An efficient primal heuristic is developed to find a feasible solution to the problem to facilitate the procedure. In the branch-and-bound method, three different bran

With rapid urbanization, the importance of urban warfare is increasing, and it is also required to reflect the characteristics of cities in wargame models. However, in the military's wargame models, the urbanization factor was calculated and used without theoretical basis. In this study, we investigate techniques for estimating the urbanization factor using Fractal dimension theory. The urbanization factor we propose can suggest a logical and valid representative value when used in conjunction w