## 3 Results
### Existing Solutions
#### 3.3.2 Passenger Attributes and Strategic Cabin Distribution  
Certain physical characteristics of passengers (such as walking speed and mobility), along with initial cabin location, also need to be considered when optimizing the evacuation of cruise/large passenger ships. [PERSON] and [PERSON] [24] concluded that the passengers with lower walking speed may reach evacuation stations quicker if their cabins are strategically located near these points, while faster-moving passenger with cabins located farther away will arrive at their evacuation stations at the same time or slightly slower compared to the passengers with slower moving speeds. This showcases the significance of having a well-balanced accommodation distribution in order to reduce total evacuation times. The implications of these are far-reaching for the design and operation of cruise ships. The method of assigning cabins based on passenger flow characteristics could help prevent delays and increase evacuation efficiency. Building on this idea, [PERSON] et al. [42] have developed a multi-criteria optimization model for emergency evacuation planning problems to combat common errors in relation to passenger allocation to assembly stations, minimizing counter flow, and ensuring the feasibility of evacuation plans. This model was successfully implemented in a simulation on a large cruise ship with almost 5000 passengers, and it illustrates how personalized evacuation strategies can be applied in practice and their practical benefits.  
Furthermore, the addition of passenger characteristics to complex risk models can also improve decision-making for evacuations. For example, [PERSON] et al. [43]) developed a risk model using Bayesian networks (BNs) are probabilistic graphical models that represent a set of variables and their conditional dependencies via a directed acyclic graph (DAG). They are used widely in various fields, including statistics, machine learning, and artificial intelligence, to model uncertainty in complex systems and infer relationships between variables. In a Bayesian Network, nodes represent random variables, and directed edges represent conditional dependencies between these variables. The structure and the associated conditional probabilities enable BNs to calculate the likelihood of different outcomes given observed evidence, making them a powerful tool for reasoning under uncertainty.) and event tree analysis that incorporates passenger characteristics (gender, age, size, mass, health, and psychological characteristics) to assess risks in the entire emergency process. Its compliance with International Maritime Organization (IMO) and European Maritime Safety Agency (EMSA) guidelines authenticate the model as an efficient tool for optimizing safety using a tactical approach of trying to classify passengers according to their characteristics. Taken together, these points suggest a move towards a change in paradigm regarding evacuation planning. Evacuation strategies should now be tailored to individual needs and characteristics, instead of the previous one-size-fits-all approach. By taking into account passenger characteristics when assigning cabins and evacuation routes, it is possible that it can greatly increase the efficiency onboard during evacuations and reach higher safety levels in emergencies.