## 2 Literature Review
For the other assessment, a framework based on mul-criteria decision making (MCDM) is provided that integrates spherical fuzzy Analytical Hierarchical Process (SF-AHP) and grey Complex Proportional Assessment (G-COPRAS), in which spherical fuzzy sets and grey numbers are used to express the ambiguous linguistic evaluation statements of experts as presented in [33]. In [34], [PERSON] et al. develop a scenario-based stochastic optimization model to choose optimal policies for the integrated deployment of local urban relief teams in the MCIs. Factors such as the deployment of local relief teams in an urban area with several affected sites, allocation of casualties to treatment centers and assignment of medical teams to casualty treatment centers and triage groups are simultaneously determined. [PERSON] et al., in [35], integrate the Gravitational Search-based Back Propagation Neural Network prediction model along with a qSOFA medical score to generate an accurate prediction of casualty's health condition deteriorated and thus enable the classification of casualties into risk profiles. [PERSON] and [PERSON] [36] consider optimally dislocated ambulances to prioritize patients during MCIs as a Markov decision process model with two classes of ambulances. In [37], [PERSON] et al. designed casualty collection point (CCP) locations and the efficient allocation of limited emergency medical service (EMS) resources to transport casualties quickly to appropriate hospitals and increase the survival rate of casualties. A hybridsimulation-optimization approach to optimize the CCP location and the EMS resource allocation problem over mixed binary and integer feasible domains for the minimization of the expected complete delivery time of all casualties from the disaster region to hospitals is presented. A novel two-stage sequential algorithm, namely a combination of optimal computing budget allocation-based rapid-screening algorithm and an adaptive particle global and hyper box local search (ORSA-APGHLS), is developed. [PERSON] et al. presented in [38], combining particle swarm optimization and the genetic algorithm in solving distributed scheduling problems and applied them to problems such as multi-agent task planning. After a literature review on the subject, the most important factors influencing the number of deaths and permanent disability of people injured in the MCI are:  
1. A number of casualties who will survive, in emergency medicine, i.e., the so-called \"avoidable deaths\" [11; 13]. 2. Post-accident status of the injured is described by the following values [13; 39]: respiratory rate, pulse, state of consciousness and systolic arterial pressure. The post-accident condition can be supplemented [40] by introducing age, sex and the characteristics of capillary recurrence. 3. The expected value of survival of the injured in the MCI depends on the post-accident status of the injured person [13; 17; 41]. In [16], the author conducted research on the change in the probability of survival of the injured over time due to the group of medical segregation to which these persons were assigned. In [16], the author described the survival time of people due to belonging to a segregating group with an appropriate logistic function. In [11], the author took into account the expected value of the casualty's survival due to the post-accident condition. 4. Availability and quality of transport for injured persons are characterized respectively by the total number and types of means of transport [11]. An important factor is the information on the availability of means of transport at a given moment [12; 16]. 5. Availability of hospital emergency department (HED) resources and the effectiveness of treatment of individual post-traumatic conditions. In [42; 43], an analysis was made of the appointment of specific personnel to treat a specific medical case--the post-accident state of the injured. In [13], the number of operating rooms was taken into account when determining the target HED.