## 2 Literature Review  
The work on improving the organization and management of emergency medical services in the case of mass incidents is mainly concerned with the problem of optimal dislocation of emergency system units to handle mass incidents, as well as research related to the quality of prioritization of the handling of casualties in a mass casualty incident. [PERSON], in his article [13], makes a comparison of metaheuristic methods, especially hyperheuristic methods (a combination of basic heuristic methods) and a pilot method for the problem of efficient prioritization of medical service. Statistical tests are conducted on the two most urgent medical service priority groups to which casualties are allocated based on the START system. The test variables are the number of operating rooms, the number of injured persons and the probability of survival of these persons based on a Weibull distribution, which is often used when the probability of survival varies over time. The test results represent the percentages of all injured persons who received adequate medical attention. According to the author, the four-class START system used in practice needs to be improved, and the results of the study confirm the validity of the hyper-heuristic method in conjunction with the pilot method. [PERSON] et al., in their article [14], formulate the problem of optimizing the allocation of emergency medical services forces and resources in mass casualty incident and then compare three methods for solving this problem. They use the D'Hondt method, the greedy algorithm and the simulated annealing algorithm. Parameters such as the individual's waiting times at the scene and the waiting times in the ED for rescue treatment are characteristic of this model. In addition, the injured persons in the worst condition are considered first. In addition, the authors of the model develop functions mapping the severity of selected injury types to the passage of time. In [15], a model is developed to optimize the allocation of injured people to hospitals, taking into account the optimal number of patients that can be efficiently served within a given time limit. The model is implemented within a system called ISCRAM (Information Systems for Crisis Response and Management). In [4], authors develop a discrete-event simulation policy model by evaluating realistic scenarios. The model performs scheduling and outcomes at operative and online levels. Based on a variety of such situations, the authors derive general policy implications at both the macro (e.g., strategic rescue policy) and micro (e.g., operative and online scheduling strategies at the emergency site) levels. [PERSON] et al., in their paper [16], address the problem of prioritizing the medical handling of casualties in a mass casualty incident. The aim is to investigate the impact of not taking into account the current state of availability of forces and resources of the local emergency medical system in the process of determining medical service priority groups. The authors develop a model based on a modification of the START system, which takes into account only two classes of clinical-transport priority: immediate and delayed. This is because they find that, in practice, these two groups, in particular, are crucial in analyzing the problem of medical transport and service. The model takes into account the time-varying probability of survival of casualties depending on the priority group to which they belong. The availability of transport means at a given time, and the total number of casualties are also taken into account. In the model, the arrival times of the means of transport at the scene of the incident are determined using Poisson distribution. The survival times of people due to their belonging to one of the two priority groups are described by a logistic function. In [17], [PERSON] et al. develop a model based on the Fixed Job Scheduling Problem (FJSP) algorithm, a modified job scheduling problem--a general sequencing problem with parallel machines.