## 2 Literature Review
The model defines jobs as affected persons, operations as tasks and machines as emergency response units. Five objective functions are considered, such as (1) the expected value of the number of deaths among injured persons, (2) the total projected time required to transport individuals to hospitals, (3) the quality of medical service offered by the assigned hospital to individuals, (4) the total time between a specific action taken by rescuers and (5) the current total time taken by rescuers to perform actions. The model takes into account factors such as the projected average time of the assigned medical task and the change in the probability of survival of casualties with time. A characteristic feature of the model is the stochastic approach to the change in the probability of survival of casualties in the danger zone at the scene before the time of the start of medical rescue operations. Approximate methods--the Variable Neighborhood Search (VNS) metaheuristics and its deterministic variant Variable Neighborhood Descent (VND)--are used to solve the problem. A resource-constrained triage model called SAVE (Severity-Adjusted Victim Evacuation) is proposed by [PERSON] and [PERSON] in [11]. The model takes into account the state of the injured in the process of assigning transport means and hospital places after an MCI. The objective of the optimization problem considered is to maximize the number of people still alive while not overwhelming any single hospital. This problem is formulated as a mixed-integer program. [PERSON] et al. [18] present a numerical solution which uses the deterministic optimal control theory for determination by [PERSON]'s minimum principle of the optimal service rate according to the priority classes. This queueing system aims to minimize the expected value of the square differences between the number of servers and the number of patients and the cost of serving these patients over a determined time period. The optimization of the allocation of forces and resources (rescue teams) to earthquake hazard zones is presented in [19]. The objective function is to maximize the number of living persons. The affected persons are assigned to two categories of people. The first is people who urgently need medical assistance, and the second is people whose assistance can be deferred. The danger zones are divided into three types, i.e., the most endangered zone, the vulnerable zone and the less endangered zone. Based on the danger zones, the expected probability of survival is assumed. The utility principle of achieving the maximum number of survivors is applied. Integer non-linear programming is used to solve the optimization problem. In [20], Markov Chain is used for establishing a function from treated time to probability of death. A flexible job shop scheduling model and a genetic algorithm are designed. In [21], a multi-criteria optimization problem is solved in which three objective functions are defined, expressed as: (1) effectiveness--the rate of survival or well-being of those affected by a mass casualty event, (2) efficiency--expressed in terms of the rate of human suffering caused by the delay of humanitarian assistance and (3) the efficiency of providing humanitarian assistance. The optimization problem is solved by the weighted sum method of the criteria. In [22], the integer (mixed) linear programming is proposed for optimal dislocation and redislocation of ambulances. Lagrangian Dual Decomposition (LDD) is used to partition the set of ambulance allocations. [PERSON] et al., in [23], design the SIMEDIS (Simulation for the Assessment and Optimization of Medical Disaster Management) simulation model, which consists of 3 interacting components: the victim creation model, the victim monitoring model and the medical response model. SIMEDIS, characterized by the interaction between the two models, is provided by a time trigger or medical intervention.