## 1 Introduction
Reference [12] first analyzed the SMETP, and Reference [13] proved the SMETP is NP-hard. Since then, studies on SMETPs have focused on meta-heuristic approaches to solve the complexity of local searches. Reference [14] proposed a hybrid meta-heuristic that combined tabu search concepts and genetic algorithms (GAs) to solve the SMETP. Reference [15] proposed a hybrid heuristic that combined local search heuristics with an evolutionary algorithm. Reference [16] proposed an evolutionary approach based on an imperialist competitive algorithm, and Reference [17] proposed an improved bee algorithm that uses GA operators in the global search stage. The present study also uses GA to overcome the complexity of firing sequence combinations in the global search stage. However, while the aforementioned studies assumed that all parameters were certain in the SMETP, the current study proposes an approach to find a robust solution considering parameter uncertainty. Parameter uncertainty analyses can be divided into stochastic programming and robust optimization approaches. Stochastic programming optimizes expected performances using prior knowledge on the probability distribution of uncertain data. Accordingly, accurate information about the probability distribution of uncertain data should be known in stochastic programming, and data values should not change until decision-making has been executed [18]. However, it is difficult to acquire accurate information about uncertain data's probability distribution. RO searches for a solution that is immune to uncertain parameters. A robust solution presents the best worst-case solution; this solution is more conservative than a stochastic programming solution. However, RO approaches do not require information about uncertain data's probability distribution. Uncertain parameters are expressed in two sets: scenario and interval datasets using expert opinions or historical data [19]. The RO approach was first introduced by [PERSON] [20] and was extended by [PERSON] [21; 22]. It is now applied in various domains. However, studies on RO approaches have been extremely limited in military operation fields. Only two studies by [PERSON] [10] and [11] discussed the RO approach. [PERSON] [11] considers target threat level as an uncertain parameter by assuming a problem situation where the enemy unit size cannot be accurately determined. In Reference [11], a deterministic optimization model and an RO model that minimize enemy's firearm threat exposed to the friendly force were developed by distinguishing between when the enemy threat parameter is certain and when it is uncertain. Furthermore, a two-phase approach was developed based on the concept of \"cardinality-constrained uncertainty.\" This approach evaluates the enemy's firearm threat to friendly forces by adjusting the degree of uncertainty when the enemy threat is uncertain. Reference [10] considered target threat level as an uncertain parameter by assuming a problem situation where the quantitative true value of the enemy combat power is unknown. In Reference [10], the combat power of the enemy targets was assumed as the target threat parameter and the model was developed by considering this as an uncertain parameter because it is difficult to determine the quantitative true value of the parameter. References [10] and [11] only consider the single factor (target threat level) out of various factors as an uncertain factor because problem complexity and the robustness issue of solutions are negatively affected as the number of considered uncertain factors increases. However, this study considered two factors at the same time as uncertain parameters, the target threat level and target threat time, but proved that only the uncertain target threat time influenced the solution's robustness. In this paper, an RO approach using the scenario concept was developed.