One of the most significant threats faced by ships is anti-ship missiles. Nowadays, these missiles, equipped with diverse guidance systems, can locate their trajectory and attack the ship. Consequently, ships need to utilize their weapons to attempt to neutralize these threats. This article aims to develop dynamic assignment algorithms to assign a ship’s defensive soft-kill weapons to a set of incoming missiles, to minimize the average damage inflicted on the ship. To this end, initially, a binary linear programming model is developed to solve the static weapon- target assignment problem. Subsequently, a simulation–optimization algorithm and a reinforcement learning- based approach, grounded in the value iteration algorithm, are developed to solve the dynamic weapon-target assignment problem. To compare and evaluate the performance of the developed solution methods, we employ a set of randomly generated test instances. Computational results indicate that the reinforcement learning approach, due to its inherent foresight, outperforms the simulation–optimization approach in reducing the inflicted damages. However, in terms of CPU run time, the simulation–optimization approach is more efficient.

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