A Novel MCDM Approach to Integrating Human Factors into Evacuation Models: Enhancing Emergency Preparedness for Vulnerable Populations

Abstract

This research determines how to integrate factors related to evacuation in emergency preparedness using techniques for Multicriteria Decision-Making (MCDM). A distinctive MCDM technique that incorporates human behavior into evacuation models enhances decision-making and safety during emergencies, especially in vulnerable populations. For this purpose, a hybrid combination of MCDM methods—CRiteria Importance Through Intercriteria Correlation (CRITIC), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Weighted Aggregated Sum Product Assessment (WASPAS)—is used to rank the vulnerability of Chilean regions by considering various factors. First, the related factors are ranked by CRITIC, and the result is that the “psychosocial problem” factor has the highest priority and weight. Then, according to the hybrid methods and CRITIC, all regions of Chile are ranked first with TOPSIS, WASPAS, and a combination of them to determine which one has the highest priority. The results show that the Santiago Metropolitan Region has the highest priority for vulnerability in all three methods.

1. Introduction

The growing world population will lead to greater urban settlement and is projected to reach 9.7 billion people by 2050 [1]. However, there has also been a significant increase in climatic and seismological events that pose direct risks to the sustainability and safety of cities. In emergency situations, evacuation actions are tested, and safety measures can be impaired by mass reactions, which generally involve the movement of people with little time to decide. In these cases, the decision to move taken by each individual has to be foreseen, premeditated, and trained in advance. This is important when decision-makers design evacuation plans based on previous experiences and future scenarios with an increased number and intensity of climatic and seismic events. It is also essential as a modeling problem because of the need to achieve adequate adjustments in accuracy and computational time that are appropriate for application.

Pre-emergency evacuation plans lack an interdisciplinary assessment of human factors, and regardless of the type of disaster, there are priority areas that are more critical to deal with than others. Decision-makers must design appropriate plans to minimize the risk of human loss. Indeed, the problem involves people who will experience catastrophic events and who must react to emergencies in extreme situations, whether in response to a prior call to evacuate or during the event itself, such as hurricanes, storms, volcanic eruptions, earthquakes, tsunamis, floods, and even terrorist attacks. Given the diversity of possible events, their occurrence can geographically affect different human settlements, particularly in urban areas. Establishing coordination among many people in high-pressure evacuation situations or imminent survival threats remains a significant challenge. Current mathematical models can only approximate solutions, studying access points prescriptively or predictively in the dynamic evacuation setting. These models must consider the complex interactions between people, such as avoiding collisions and congestion, and the diverse behaviors and group dynamics that arise during a crisis. Evacuation plans estimate times and protocols according to the subjective experience of groups of experts or based on simulated studies [2,3], maintaining uncertainty in the possible outcomes of the evacuation plans.

Existing studies on crowd management and crowd behavior focus on the simulation of scenarios and cases to assess the movement of people with different patterns of behavior using a model that appropriately emulates crowd behavior [4,5,6,7] in evacuation situations prior to an imminent risk condition [8], as well as in evacuation situations where high-density groups behave differently according to the degree of interaction and concentration [9,10]. Evacuation processes are divided into pre- and post-evacuation, and the latter includes the evacuation situation itself [9,11]. Refs. [12,13] highlighted the importance of interdisciplinarity and behavior modification in evacuation management.

Previous studies used MCDM to evaluate evacuation in close spaces; for example, in [14] a methodology was used to identify risk factors in the process of human evacuation from passenger ships or to allocate facilities strategically, such as [15] presenting a comprehensive framework to guide site selection for post-earthquake emergency medical service facilities, or based on simulation and metaheuristic optimization to select the best-satisfying evacuation scenario with an MCDM method [16]. However, no studies have considered human factors in decision-making.

Using the Technique of Order of Preference by Similarity to the Ideal Solution (TOPSIS), which evaluates the proximity between the ideal and anti-ideal solution to achieve the smallest geometric distance to the ideal solution, that is, the one that comes closest to being the best option, in addition to the WASPAS that calculates the ratio of weights of the same alternative and the product, that is, the intersection between the criteria with respect to the same alternative, thus valuing the alternative with the highest resulting value in its relative weight ratio and the respective value of the normalized alternative. We assessed the applicability of the model in a case study to identify the most vulnerable areas and the significant risks related to human factors. Previously, the importance of each assessed factor was weighted by CRITIC. This study considers the ranking of alternatives, combining the strengths of each method, to help decision-makers allocate resources and make informed investments in emergency planning.

The rising occurrence of natural disasters around the world has revealed major weaknesses in conventional evacuation planning methods. The current models of crowd movement engineering have made significant progress, but they do not include the intricate human conduct patterns that decide whether evacuations succeed or fail. The lack of consideration for psychosocial factors becomes especially critical for developing countries because their rapid urbanization and socioeconomic inequalities and insufficient infrastructure increase emergency vulnerabilities. Recent disasters including the 2010 Haiti earthquake and 2023 Chilean wildfires proved that psychological elements, together with economic differences, strongly influence how evacuations turn out. The 2015 Illapel earthquake in Chile showed that psychological paralysis and family reunification efforts caused delays in evacuation, which exceeded physical infrastructure constraints by 30 percent. Quantitative evacuation models exclude human factors from their analysis, which produces an unsafe difference between theoretical plans and actual evacuation outcomes. This study develops an innovative MCDM framework that merges human behavioral elements with standard engineering criteria to address this essential knowledge gap. The need for this work becomes more urgent because climate change projections indicate that disasters will occur more frequently in developing nations, which typically have restricted emergency response capabilities. Our CRITIC-WASPAS-TOPSIS hybrid method enables policymakers to allocate resources effectively to vulnerable groups by evaluating the multifaceted social and economic and psychological elements that influence actual evacuation results.

In previous research, significant areas have been overlooked. Many existing models rely on simplistic representations of human actions. Most studies have focused on engineering solutions without adequately addressing how individuals and groups behave under stress, which can lead to suboptimal evacuation strategies. Conversely, there remains a gap in effective methodologies that synthesize insights from the social sciences, psychology, and engineering. Some studies have used an MCDM method for evacuation, but this may be insufficient as current frameworks often fail to incorporate diverse perspectives that can enhance evacuation planning and execution. This interdisciplinary approach is essential for developing realistic models that reflect human behavior during emergencies. Finally, previous studies often ignore the specific needs and behaviors of vulnerable populations during evacuations, such as the elderly, disabled individuals, or those with limited mobility.

The analysis of the research gap will help improve global emergency management, contributing to the interdisciplinary analysis of human-centered catastrophic events. This research study addresses a significant knowledge deficiency by incorporating human elements such as psychosocial factors and socioeconomic conditions and demographic characteristics into evacuation modeling systems, which engineering-based approaches tend to ignore.

We present an innovative MCDM framework that integrates CRITIC with WASPAS and TOPSIS for the first time in this specific field. Our model uses objective criteria weighting to improve emergency preparedness decision accuracy. The interdisciplinary methodology connects engineering with social sciences and psychology to provide a more complete approach. This study investigates the insufficient consideration of vulnerable groups, including elderly people disabled individuals and low-income communities. The model demonstrates its practical use through its application to real data from all regions of Chile. This study identifies specific emergency areas that require immediate support for creating more balanced emergency planning. The approach can be duplicated by nations that experience comparable risk profiles. This study enhances both theoretical knowledge and practical tools used in human-centered disaster management.

The contribution of the present study can be summarized as follows:

• The pioneering exploration of interdisciplinary relationships between social science, psychology, and engineering;
• A mathematical approach based on a novel MCDM method for blending several disciplines related to human factors;
• National statistics reports and a literature review, which identify the emergency management knowledge framework and sociodemographic, socioeconomic, and psychosocial factors influencing the process.

The findings are then used to guide the establishment of a new risk analysis framework to prioritize vulnerable regions, where human factors affect the consequences of evacuation plans in emergencies.

The remainder of this paper is structured as follows: Section 2 presents a detailed literature review. Section 3 explains the methodology of this study. Section 4 details a case study that applies the proposed methodology. Our results are presented in Section 5 and discussed in Section 6. Finally, our conclusions are presented in Section 7.

2. Literature Review

As mentioned, previous studies have explored evacuation management plans based on modeling the movement of people; however, there are still gaps to be filled and approaches to be found. The following is a review of the various methods proposed to address emergency evacuation planning:

2.1. Evacuation in Emergency Management

The evacuation processes are divided into pre-travel and actual travel phases [11]. In emergency conditions, both the preliminary stage and immediate action once the emergency event has started are key to minimizing irreversible consequences. In [14], a new assessment method for evacuations in limited spaces such as cruise ships was proposed by presenting a risk assessment model. In this case, most passengers do not have previous preparation for emergency actions; therefore, verifying mass behavior in evacuation instances is relevant. Other situations in confined spaces are also essential for assessing evacuees’ decisions as a survival reaction [2,17]. Although evacuation scenarios have been studied to optimize plan implementation and execution, they still lack representation of human behavior in detail, limiting their scope to engineering solutions. For example, a staggered evacuation is proposed in [18] to improve speed, safety, and functionality. This model uses the total evacuation time, degree of exposure to a catastrophic event, and congestion generated as the primary factors. However, it does not include the behavioral patterns of particular groups of people or individuals, which may affect the actual results of the plan.

2.2. Human Factor in Evacuation Models

Individual behavior and environmental conditions affect the behavior of people in a large crowd. Derived from this first idea, the human factor of evacuation is relevant [3,17,19].

While events requiring evacuation can originate from various causes, such as fires, tsunamis, hurricanes, and floods, the conditions and predispositions of the people involved show familiar patterns. A comparative review of the case of earthquakes [20] discusses which factors have been applied and improved in various countries over the years to provide protective actions and guidelines, considering the characteristics of their populations from the perspective of sheltering before the onset of catastrophic events with little or no predictability. In some cases, they approach shelters using emergency warnings and various technological tools. Current technologies are also helping to improve the effectiveness of evacuations [21], allowing verification of how tools based on geographic information systems (GISs) and remote sensing help in the modeling of emergency evacuation plans [15,22]; these technologies also help by studying people’s behavior in choosing immediate evacuation routes [17], including using data from mobile phone users [23].

Although external and unforeseen effects trigger evacuation, human reactions are fundamental for a proper evacuation plan. In this regard, human factors such as sociodemographic factors [24,25,26,27,28], socioeconomic factors [25,28,29,30,31,32,33,34], and experience and knowledge [32,35,36,37,38,39,40] are crucial. Although some studies discuss human factors, these are still explorations with little relationship between them. They integrate their effects into evacuation plans, ignoring the specific needs and behaviors of more vulnerable population groups. For example, human factors are assessed in [14] from the perspective of people participating in emergency decision-making. However, it is not associated with the potentially vulnerable conditions of evacuees, such as the elderly, disabled individuals, or those with limited mobility, among others.

2.3. MCDM Approaches

Recognizing the human factors that affect decision-making when establishing action plans is relevant. Multicriteria decision-making (MCDM) methods help with this task. MCDM is a decision theory methodology used across disciplines [41,42,43,44,45,46,47,48,49]. The use of MCDM to address evacuation-related decision-making problems has been appreciated in recent years [14,15,18,21,22,50,51].

For instance, the Analytic Hierarchy Process (AHP) was applied in [14], to prioritize the evaluation indices involved in expert decisions. In [15], AHP and TOPSIS were used to evaluate criteria weights and the suitability of locations in emergency medical service facilities. These techniques were combined with specific technical tools such as Building Information Modeling (BIM) [51].

As shown in

Table 1. Comparison of papers related to emergency evacuation management. (‘x’ indicates that the respective aspect is addressed in a study.)

Author(s)	Human Factor	Evacuation	MCDM Methods
[28]	x	x
[33]	x	x
[40]	x	x
[37]	x	x
[25]	x	x
[36]	x	x
[30]	x	x
[29]	x	x
[39]	x	x
[35]	x	x
[32]	x	x
[26]	x	x
[34]	x	x
[38]	x	x
[24]	x	x
[31]	x	x
[27]	x	x
[52]		x	x
[53]		x	x
[49]			x
[54]		x	x
[55]			x
[18]		x	x
[50]			x
[56]			x
[2]		x
[57]			x
[22]	x	x	x
[51]			x
[11]	x	x
[58]		x	x
[3]	x	x
[59]			x
[60]			x
[19]	x	x
[61]			X
[43]			x
[62]			x
[63]	x	x
[15]		x	x
[17]	x	x
[64]			x
[20]	x	x
[65]			x
[48]			x
[23]		x
[42]			x
[46]			x
[41]			x
[66]		x	x
[45]			x
[44]			x
[47]			x
[14]	x	x	x
[21]		x	x
Our study	x	x	x

, some studies have used MCDM for emergency evacuation management. However, its potential benefits remain underexplored, with a significant gap in synthesizing advances from various disciplines such as the social sciences, psychology, and engineering. For example, in the review conducted in [21], the contribution of artificial intelligence to natural disaster management was identified, and its interaction with MCDM models was studied. However, their scope was primarily limited to engineering aspects. Additionally, in [22], a model was employed that integrates certain aspects related to the human factor; however, their approach is more oriented towards general aspects, with MCDM being used for aspects unrelated to the human factor’s incidence from a socioeconomic or psychological perspective.

Several MCDM techniques have been used for facing various applied problems. As presented in

Table 2. MCDM techniques used in different related works. (‘x’ indicates that the respective method was used in a study.)

Author(s)	AHP	ANP	ARAS	MARCOS	COPRAS	DEMATEL	EWM	IEW	GRA	BWM	MABAC	MAUT	PROMETHEE	REGIME	TOPSIS	CRITIC	SWARA	VIKOR	WSM	WASPAS
[52]	x																			x
[53]	x														x
[54]								x							x
[55]			x		x										x	x
[18]															x
[56]																x
[57]															x
[22]																	x
[51]	x	x										x	x		x
[58]															x
[59]										x	x					x
[60]															x	x		x	x
[61]				x												x
[62]													x	x		x
[15]	x														x
[64]									x						x	x
[65]					x											x
[41]	x														x
[66]						x	x		x						x
[14]	x
Our study															x	x				x

, there are not any works using a combined CRITIC, WASPAS, and TOPSIS MCDM technique.

3. Methodology

To formalize the problem of evaluating the applicability of multicriteria decision-making (MCDM) methods—specifically TOPSIS, WASPAS, and CRITIC—in identifying vulnerable areas and significant human-factor-related hazards during evacuations, we can define the problem mathematically in five main stages, as shown in Figure 1.

First, we need to obtain data related to the criteria to be considered. In this case, we use objective information to specify the decision matrix that considers alternatives and their criteria values to identify one or several priority zones for assigning resources related to the geographical zone and the population characteristics.

With the information consolidated, the next step is calculating the weights for the considered criteria. Then, alternatives are evaluated using WASPAS and TOPSIS with their respective procedures; we need to normalize the data from the associated calculations in both cases. Finally, a score to rank the alternatives is calculated. In the following subsections, we discuss the mathematical details of each method.

3.1. Use CRITIC for Weight Calculations

CRITIC determines the weights for criteria based on contrast intensity (relative to the variability in the data) and correlation or conflict between criteria [67] and is applicable to ensure a balanced and data-driven weighting system. The weight increases when the standard deviation is higher, indicating greater discriminative power in the criterion. Conversely, a lower correlation with other criteria manifests less redundancy and increases the weight. We obtained the weights for the criteria by calculating the product of standard deviations and the sum of the complements between criteria.

Steps:

• Construct the decision $X={\left[{\xi }_{ij}\right]}_{m\times n}$ and organize alternatives $A_i (i=1,2,\dots ,m)$ and criteria $C_j \left(j=1,2,\dots ,n\right)$ with their corresponding values. ${\xi }_{ij}$ is the value of the jth criterion for the ith alternative.

  $$X=\begin{array}{cc}\begin{array}{c}\end{array}& \begin{array}{cccc}{C}_1& C_2& \dots & C_n\end{array}\\ \begin{array}{c}{A}_1\\ A_2\\ ⋮\\ A_m\end{array}& \left[\begin{array}{cccc}{\xi }_{11}& {\xi }_{12}& \dots & {\xi }_{1n}\\ {\xi }_{21}& {\xi }_{22}& \dots & {\xi }_{2n}\\ ⋮& ⋮& ⋮& ⋮\\ {\xi }_{m1}& {\xi }_{m2}& \dots & {\xi }_{mn}\end{array}\right]\end{array}$$

  (1)
• Normalize the decision matrix:
  By using min–max normalization, the scale of values and units of measurement for each criterion can be made independent, keeping everything within the same range, which facilitates comparison and calculations. ${\xi }_j^{min}$ and ${\xi }_j^{max}$ are the minimum and maximum values of the j-th criterion across all alternatives.
  For maximization criteria:

  $${\overline{\xi }}_{ij}={\displaystyle \frac{{\xi }_{ij}-{\xi }_j^{min}}{{\xi }_j^{max}-{\xi }_j^{min}}},i=1,2,\dots ,n;j=1,2,\dots ,m$$

  (2)
  For minimization criteria:

  $${\overline{\xi }}_{ij}={\displaystyle \frac{{\xi }_j^{max}-{\xi }_{ij}}{{\xi }_j^{max}-{\xi }_j^{min}}},i=1,2,\dots ,n;j=1,2,\dots ,m,$$

  (3)

  where ${\xi }_j^{max}=\underset{j}{\max }\left\{{\xi }_{1j},{\xi }_{2j},\dots ,{\xi }_{mj}\right\};{\xi }_j^{min}=\underset{j}{\min }\left\{{\xi }_{1j},{\xi }_{2j},\dots ,{\xi }_{mj}\right\}$.
  In any case, all normalized coefficients ${\xi }_{ij}$ are in the interval $[0,1]$.
• Calculate standard deviation ${\sigma }_j$ and correlation coefficients $r_{jk}$ for criterion $C_j (j=1,2,\dots ,n)$: The variability allows for understanding the impact of the criterion on the evaluation of an alternative, considering that the higher the variability, the more this criterion influences the relevance of the assessment. $r_{jk}$ contains the coefficients of the linear correlation of the vectors ${\xi }_j$ and ${\xi }_k$, whereas

  $${\phi }_j={\sum }_{k=1}^n\left(1-r_{jk}\right)$$

  (4)

  ${\phi }_j$ represents the total conflict or dissimilarity for criterion $j$. ${\sigma }_j$ represents the standard deviation of criterion $j$. The correlation allows for assessment of how closely the criteria are related to each other, avoiding redundancy between highly related criteria; therefore, those criteria with low correlation should be prioritized, and consequently, the cumulative sum of low correlation of the same criterion concerning the others in comparison indicates that it has a greater capacity to discriminate between the alternatives under evaluation.
• Compute the CRITIC weights:

  $$W_j={\sigma }_j\times {\phi }_j={\sigma }_j\times {\sum }_{k=1}^n\left(1-r_{jk}\right)$$

  (5)

  W_j represents the composite weight assigned to criterion j.
• Obtain the relative weights:

  $$w_j={\displaystyle \frac{W_j}{{\sum }_{k=1}^n{W}_k}}$$

  (6)

  The CRITIC-derived weights $w_j$ are used in both TOPSIS and WASPAS for weighted evaluation.

3.2. Evaluate Alternatives with WASPAS

WASPAS integrates the Weighted Sum Model (WSM) and Weighted Product Model (WPM) as a hybrid MCDM method. Its design allows us to evaluate and rank alternatives for balanced decision-making [68]. On one side, WSM is based on additive aggregation, where the score of alternatives is calculated as a weighted sum of their performance across all criteria; on the other hand, WPM relies on multiplicative aggregation, multiplying the weighted criteria values to capture interactions between them.

Steps:

• Normalize values based on benefit or cost criteria: In this case, a simplified min–max normalization is used, where the maximum or minimum value is used for optimization considering benefit or cost criteria. ${\overline{{\xi }_{ij}}}_{WASPAS}$ is the normalized value of ${\xi }_{ij}$ using the WASPAS method.

  $${\overline{{\xi }_{ij}}}_{WASPAS}={\displaystyle \frac{{\xi }_{ij}}{\underset{j}{\max }{\xi }_{ij}}}\mathrm{for}\mathrm{beneficial}\mathrm{criteria}$$

  (7)

  $${\overline{{\xi }_{ij}}}_{WASPAS}={\displaystyle \frac{\underset{j}{\min }{\xi }_{ij}}{{\xi }_{ij}}}\mathrm{for}\mathrm{cost}\mathrm{criteria},$$

  (8)

  where ${\overline{{\xi }_{ij}}}_{WASPAS}$ is the normalised value of ${\xi }_{ij}$.
• Calculate WSM and WPM scores using $w_j$ obtained by CRITIC: In this case, WSM multiplies the criterion values by their respective weights and then sums them to obtain a total score for each alternative, thereby achieving a linear relationship between the criterion and the alternative. In contrast, WPM raises the criterion values to the power of their respective weights and then multiplies them to obtain a total score for each alternative, resulting in a non-linear relationship between the criterion and the alternative. The combination of both metrics gives greater robustness to the result delivered for a decision. $S_{WSM}$ denotes the WSM score, and $S_{WPM}$ is the WPM score:

  $$S_{WSM}={\sum }_{j=1}^n(w_j\times {\overline{{\xi }_{ij}}}_{WASPAS})$$

  (9)

  $$S_{WPM}={\prod }_{j=1}^n{\left({\overline{{\xi }_{ij}}}_{WASPAS}\right)}^{w_j}$$

  (10)
• Combine the WSM and WPM scores:

  $$S_i=\lambda \times S_{WSM}+\left(1-\lambda \right)\times S_{WPM}$$

  (11)

  The coefficient $\lambda$ expressing the weighting of WSM and WPM scores could be further analyzed and optimized [69], but it is frequently set to 0.5 [70].

3.3. Apply TOPSIS for Final Ranking

The TOPSIS method is a widely used MCDM approach that refines rankings by considering proximity to the ideal solution, aiming at the shortest geometric distance to the positive-ideal solution (PIS) and the longest geometric distance to the negative-ideal solution (NIS) [54]. TOPSIS is valued for its simplicity, computational efficiency, and ability to provide clear, interpretable results. It starts by normalizing the decision matrix to eliminate the effects of different measurement units, and weights are applied to the criteria to reflect their relative importance. With a maximum value for benefit criteria or a minimum value for cost criteria, as appropriate, we define an ideal solution and determine an opposite or negative-ideal solution; the maximum or minimum value to be defined depends on the specific criterion depending on whether maximization or minimization is considered. For example, if one wants to study the safety of a building for emergencies and the criterion is the number of evacuation routes, the objective for that criterion will be the maximization of the number of routes; in this case, the PIS is the maximum value of available choices, and the minimum value is the NIS. The distance of each alternative from the ideal and negative-ideal solutions is calculated using the Euclidean distance. A relative closeness coefficient is computed for each alternative to indicate its proximity to the ideal solution, and alternatives are ranked, with the highest-ranking option being the closest to the ideal solution and the farthest from the negative-ideal solution.

Steps:

• Normalize the decision matrix according to (12):

  $${\overline{{\xi }_{ij}}}_{TOPSIS}={\displaystyle \frac{{\xi }_{ij}}{\sqrt{{\sum }_{i=1}^m{\xi }_{ij}^2}}}$$

  (12)
• Obtain the weighted normalized decision matrix (${\nu }_{ij}$) according to (13): As discussed above, we use CRITIC to calculate the weight criteria:

  $${\nu }_{ij}=w_j\times {\overline{{\xi }_{ij}}}_{TOPSIS}$$

  (13)
• Identify the PIS and NIS, considering the sets $J^{+}$ and $J^{-}$ of criteria with a positive and negative impact, respectively:
  • PIS: Maximum values for benefit criteria and minimum values for cost criteria:

    $$PIS=\left\{{\nu }_1^{+},\dots ,{\nu }_n^{+}\right\}where{\nu }_j^{+}=\begin{array}{c}\max \left({\nu }_{ij}\right),j\in J^{+}\\ \min \left({\nu }_{ij}\right),j\in J^{-}\end{array}$$

    (14)
    PIS (positive-ideal solution): $\left\{{\nu }_1^{+},\dots ,{\nu }_n^{+}\right\}$, where
    • ${\nu }_j^{+}=\mathrm{m}\mathrm{a}\mathrm{x}\left({\nu }_{ij}\right)$ for beneficial criteria $\left(j\in J^{+}\right)$;
    • ${\nu }_j^{+}=\mathrm{m}\mathrm{i}\mathrm{n}\left({\nu }_{ij}\right)$ for cost criteria $\left(j\in J^{-}\right)$.
  • NIS: Opposite of the PIS:

    $$NIS=\left\{{\nu }_1^{-},\dots ,{\nu }_n^{-}\right\}where{\nu }_j^{-}=\begin{array}{c}\min \left({\nu }_{ij}\right),j\in J^{+}\\ \max \left({\nu }_{ij}\right),j\in J^{-}\end{array}$$

    (15)
    NIS (negative-ideal solution): $\left\{{\nu }_1^{-},\dots ,{\nu }_n^{-}\right\}$, where
    • ${\nu }_j^{-}=\mathrm{m}\mathrm{i}\mathrm{n}\left({\nu }_{ij}\right)$ for beneficial criteria $\left(j\in J^{+}\right.$);

      $${\nu }_j^{-}=\mathrm{m}\mathrm{a}\mathrm{x}\left({\nu }_{ij}\right)\mathrm{for}\cos \mathrm{t}\mathrm{criteria}\left(j\in J^{-}\right).$$
• Calculate the separation measures:

  $$S_i^{+}=\sqrt{{\sum }_{j=1}^n{\left({\nu }_{ij}-{\nu }_j^{+}\right)}^2}$$

  (16)

  $$S_i^{-}=\sqrt{{\sum }_{j=1}^n{\left({\nu }_{ij}-{\nu }_j^{-}\right)}^2}$$

  (17)
• Determine the relative closeness:

  $$C_i={\displaystyle \frac{S_i^{-}}{S_i^{+}+S_i^{-}}}$$

  (18)

  where
  $S_i^{+}$ is the Euclidean distance of alternative $i$ from the PIS: $\sqrt{{\sum }_{j=1}^n {\left({\nu }_{ij}-{\nu }_j^{+}\right)}^2}$;
  $S_i^{-}$ is the Euclidean distance of alternative $i$ from the NIS: $\sqrt{{\sum }_{j=1}^n {\left({\nu }_{ij}-{\nu }_j^{-}\right)}^2}$;
  $C_i$ is the relative closeness to the ideal solution, defined as ${\displaystyle \frac{S_i}{S_i^{+}+S_i}}$.
  This produces a refined ranking.

3.4. Combine TOPSIS and WASPAS Rankings

After obtaining rankings from both TOPSIS and WASPAS, they were combined to enhance robustness. We calculated the average ranking using a previous normalization of each score.

Steps:

• Normalize the respective scores. $N_{S_i}^{\mathrm{TOPSIS}}$ is the normalized TOPSIS score for alternative $i$.

  $$N_{S_i}^{WASPAS}={\displaystyle \frac{S_i^{WASPAS}}{\max \left(S_i^{WASPAS}\right)}}$$

  (19)

  $$N_{S_i}^{TOPSIS}={\displaystyle \frac{S_i^{TOPSIS}}{\max \left(S_i^{TOPSIS}\right)}}$$

  (20)
• Obtain the combined score:

  $$S_c={\displaystyle \frac{N_{S_i}^{WASPAS}+N_{S_i}^{TOPSIS}}{2}}$$

  (21)
• Display the rank results.

3.5. Integration of Sociodemographic, Socioeconomic, and Psychosocial Factors

The sociodemographic, socioeconomic, and psychosocial factors described in Section 1 are operationalized through specific indicators (Table 3). The sociodemographic factors consist of rural/urban population distribution (MRA, WRA) and household composition (SPH, NHSP), while the socioeconomic factors include employment (EP) and income (MMI). The prevalence of psychosocial problems (PPs) stands as the psychosocial factor representation. The decision matrix receives its population from these indicators, which CRITIC uses to assign weights based on their observed variability and conflict. The methodology integrates these factors into criteria weights and normalization processes, so human-centric vulnerabilities directly affect region rankings according to the interdisciplinary objectives presented in the introduction.

The CRITIC-derived weights (Table 6) show that psychosocial (PP) and socioeconomic (MMI) factors are the most important criteria, which is consistent with the interdisciplinary focus introduced in Section 1, where human behavior and economic disparities were highlighted as important determinants of evacuation effectiveness.

3.6. Advantages of Combined Application of the Suggested Methods

CRITIC provides criteria weights in an objective way by analyzing their variability and contrast intensity among alternatives, giving more substantial discriminative criteria greater weight. This reduces subjective bias and strengthens decision-making. It works well in complicated situations where interdependencies between criteria and variances affect the results.

WASPAS combines WSM and WPM to improve decision-making accuracy and dependability. This enables modification of the additive and multiplicative weights for robust rankings. It reconciles conflicting criteria in complex decision-making scenarios and is computationally efficient.

TOPSIS is user-friendly and identifies options nearest to the best solution and furthest from the worst-case scenario, ensuring optimal decision-making. It enables the integration of quantitative and qualitative criteria and the control of evaluation differences by means of distance calculations. Its computational efficiency and scalability make it suitable for complex, multicriteria decision-making.

4. Case Study

Chile’s diverse landscapes and climates lead to various natural hazards, including earthquakes, tsunamis, and volcanic eruptions, making it one of the most seismically active countries in the world. Despite being one of the most stable economies in Latin America, Chile faces significant social inequality, and vulnerable populations are disproportionately affected by natural disasters. Integrating the human factor into evacuation models would ensure these communities receive the necessary attention and resources. These characteristics reflect the challenges faced by developing countries.

Chile, a long and narrow country spanning desert to sub-Antarctic regions and dominated by the Andes, presents unique diversity in Latin America (see Figure 2). With a population of 19,658,835 [71], Chile’s economy is based on raw material exports and a growing service sector. Geologically, it experiences high seismic and volcanic activity due to its location on the Pacific Ring of Fire as well as its climatic diversity, ranging from the Atacama Desert to temperate rainforests. Chile regularly faces natural risks like earthquakes, volcanic eruptions, tsunamis, avalanches, droughts, and forest fires, which are increasing due to climate change, rising temperatures, decreasing rainfall, and more frequent extreme events. This has significant impacts on sectors like agriculture, energy, and water resources. Socioeconomically, wealth concentration and inequality increase vulnerability to natural disasters, while accelerated and disorderly urbanization lead to overcrowding, a lack of services, and greater exposure to risks.

Chile’s geographic, climatic, and demographic diversity make emergency planning a complex challenge that varies across regions. The Chilean case offers valuable lessons for understanding and addressing the interplay of factors shaping disaster risk and resilience in the developing world.

The human factor is a critical element of emergency management, as it influences all process stages from risk perception to response and recovery. Equity and vulnerability are essential aspects of emergency planning; differences in age, sex, disability, socioeconomic status, and other characteristics that can increase the vulnerability of specific population groups must be considered. As discussed above, the literature identifies that sociodemographic, socioeconomic, and psychological factors influence emergency management. This case study considers expanding the knowledge framework for the design of emergency management plans from different human-related disciplines. The approach is also based on the cultural diversity that a country like Chile has because of its geographical and climatic extension, making it necessary to define the distribution of public resources related to vulnerable regions, subject to human factors and their effects on the design of emergency evacuation plans.

As argued in the Literature Review Section, the use of sociodemographic, psychosocial, and psychological factors seeks to consider the vulnerability of a territorial area (defined according to the political organization of the country) in comparison to others for a potential strategic focus, for example, for the allocation of resources in emergency plans. According to national reports based mainly on the latest census of the country in 2017 and reports from official organizations regarding psychosocial problems according to their national territory, information is collected that allows us to move forward with a multicriteria analysis, as detailed in the previous section.

The classifications by factor are based on the original data as reported. For example, in the official census report, the information associated with men and women belonging to rural or urban areas is associated with the person’s sex, relating to “the biological condition of the person, which can be male or female” [73]. Also, it uses the concept “number of nuclear households” as a group of people united by kinship ties, usually by blood, who live under the same roof and share a common budget; in simple terms, it is formed by parents and children. Table 3 describes the most important objective factors and related indicators detected for this study.

Table 3. Survey of considered indictors.

Factors	Indicator	Abbreviation	Definition
Sociodemographic	MEN—RURAL AREA	MRA	Number of men in rural areas
	WOMEN—RURAL AREA	WRA	Number of women in rural areas
	MEN—URBAN AREA	MUA	Number of men in urban area
	WOMEN—URBAN AREA	WUA	Number of women in urban area
	0 TO 4	P-0_4	Number of inhabitants in the region between 0 and 4 years, inclusive
	5 TO 9	P-5_9	Number of inhabitants in the region aged 5–9 years, inclusive
	10 TO 19	P-10_19	Number of inhabitants in the region between 10 and 19 years old, inclusive
	20 TO 59	P-20_59	Number of inhabitants aged 59
	60 OR MORE	P-60_m	Number of single-person households
	SINGLE-PERSON HOUSEHOLD	SPH	Number of single-parent households
	NUCLEAR HOUSEHOLD—SINGLE-PARENT	NHSP	Number of single-person households
	NUCLEAR HOUSEHOLD—COUPLE WITH CHILDREN	NHCCH	Number of single-person households
	NUCLEAR HOUSEHOLD—COUPLE WITHOUT CHILDREN OR DAUGHTERS	NHCNOCH	Number of nuclear households and households, not including other relatives of the head of the household
	COMPOSITE HOUSEHOLD	CH	Number of nuclear households, including other relatives of the head of household
	EXTENDED HOUSEHOLD	EH	Number of non-nuclear households, but with other relatives or non-relatives of the head of the household
	NON-CORE HOUSEHOLD	NCH	Number of adults with physical, mental, sensory, or intellectual limitations, which, in interaction with various barriers, may hinder their full and effective participation in society
	ADULTS WITH DISABILITIES	AWD	Number of adults who, due to physical, mental, or sensory limitations, require the help of others to perform basic activities of daily living
	DEPENDENT ADULT	DA	Number of persons according to their legal or social status with respect to marriage/partnership
	MARITAL STATUS	MS	Number of persons who have carried out some economic activity during a data capture period
Socioeconomic	EMPLOYED POPULATION	EP	Value that divides an income distribution into two equal parts
	MEDIAN MONTHLY INCOME	MMI	Number of persons reporting some difficulty with their emotional, psychological, and social well-being
Psychological	PSYCHOSOCIAL PROBLEMS	PPs	Percentage of the population with psychosocial problems

Table 3. Survey of considered indictors.

Factors	Indicator	Abbreviation	Definition
Sociodemographic	MEN—RURAL AREA	MRA	Number of men in rural areas
	WOMEN—RURAL AREA	WRA	Number of women in rural areas
	MEN—URBAN AREA	MUA	Number of men in urban area
	WOMEN—URBAN AREA	WUA	Number of women in urban area
	0 TO 4	P-0_4	Number of inhabitants in the region between 0 and 4 years, inclusive
	5 TO 9	P-5_9	Number of inhabitants in the region aged 5–9 years, inclusive
	10 TO 19	P-10_19	Number of inhabitants in the region between 10 and 19 years old, inclusive
	20 TO 59	P-20_59	Number of inhabitants aged 59
	60 OR MORE	P-60_m	Number of single-person households
	SINGLE-PERSON HOUSEHOLD	SPH	Number of single-parent households
	NUCLEAR HOUSEHOLD—SINGLE-PARENT	NHSP	Number of single-person households
	NUCLEAR HOUSEHOLD—COUPLE WITH CHILDREN	NHCCH	Number of single-person households
	NUCLEAR HOUSEHOLD—COUPLE WITHOUT CHILDREN OR DAUGHTERS	NHCNOCH	Number of nuclear households and households, not including other relatives of the head of the household
	COMPOSITE HOUSEHOLD	CH	Number of nuclear households, including other relatives of the head of household
	EXTENDED HOUSEHOLD	EH	Number of non-nuclear households, but with other relatives or non-relatives of the head of the household
	NON-CORE HOUSEHOLD	NCH	Number of adults with physical, mental, sensory, or intellectual limitations, which, in interaction with various barriers, may hinder their full and effective participation in society
	ADULTS WITH DISABILITIES	AWD	Number of adults who, due to physical, mental, or sensory limitations, require the help of others to perform basic activities of daily living
	DEPENDENT ADULT	DA	Number of persons according to their legal or social status with respect to marriage/partnership
	MARITAL STATUS	MS	Number of persons who have carried out some economic activity during a data capture period
Socioeconomic	EMPLOYED POPULATION	EP	Value that divides an income distribution into two equal parts
	MEDIAN MONTHLY INCOME	MMI	Number of persons reporting some difficulty with their emotional, psychological, and social well-being
Psychological	PSYCHOSOCIAL PROBLEMS	PPs	Percentage of the population with psychosocial problems

The information collected, based on their respective indicators, is detailed in

Table 4. Matrix with data for each region and indicator.

NAME REGION	MRA	WRA	MUA	WUA	P-0_4	P-5_9	P-10_19	P-20_59	P-60_m	SPH	NHSP	NHCCH	NHCNOCH	CH	EH	NCH	AWD	DA	MS	EP	MMI $	PP %
ANTOFAGASTA	28,621	7165	286,393	285,355	42,489	44,418	84,355	365,996	70,276	31.294	21,005	46,099	18,589	5778	38,792	12,757	43,902	25,286	3619	33712	500,000	3.06
ARICA Y PARINACOTA	11,177	7660	101,404	105,827	16,075	17,234	33,659	124,127	34,973	12,050	10,093	16,650	6485	2069	14,905	4590	26,235	15,134	1009	112.96	350,000	0.38
ATACAMA	14,580	11,068	129,840	130,680	21,742	23,070	40,993	159,062	41,301	16,083	11,295	24,972	10,189	1910	18,789	5468	40,699	23,693	1133	147.86	400,929	3.65
AYSÉN DEL GENERAL CARLOS IBÁÑEZ DEL CAMPO	12,724	8336	40,923	41,175	7284	8161	14,967	58,749	13,997	7564	4613	9435	4701	1120	4966	2210	15,748	8312	355	58.94	410,000	3.58
BIOBÍO	91,661	86,129	659,069	719,946	102,360	108,486	218,900	865,386	261,673	85,807	69,132	154,933	62,480	11,250	92,812	30,827	301,028	137,068	6067	692.15	346,000	3.60
COQUIMBO	74,679	67,791	294,095	321,021	54,693	57,406	107,051	412,209	126,227	43,641	34,944	61,573	27,820	6580	49,938	15,821	70,431	39,264	2649	367.49	331,191	2.66
LA ARAUCANÍA	143,725	134,955	321,406	357,138	65,136	68,256	137,493	518,727	167,612	59,089	40,760	90,753	41,322	7316	57,656	20,629	179,161	93,503	3464	419.73	310,000	2.97
LIBERTADOR GENERAL BERNARDO O’HIGGINS	121,800	112,392	331,910	348,453	61,023	65,850	125,376	506,331	155,975	52,355	38,517	93,411	40,949	6435	53,853	16,197	115,507	75,428	3176	430.28	352,000	24.36
LOS LAGOS	114,183	104,492	295,217	314,816	53,562	59,835	117,989	466,218	131,104	50,913	33,517	81,160	38,519	7180	48,578	17,615	116,876	67,358	3185	368.78	350,000	0.36
LOS RÍOS	56,724	52,327	132,123	143,663	24,310	26,650	54,527	211,248	68,102	25,040	15,449	34,735	17,528	3471	23,261	9500	61,404	32,424	1455	177.99	335,205	3.24
MAGALLANES Y DE LA ANTÁRTICA CHILENA	9134	4351	76,115	76,933	9761	10,741	22,287	95,534	28,210	10,460	6704	15,176	7,532	1759	8,587	3614	12,861	6432	847	96.33	501,805	0.35
MAULE	145,000	134,819	366,624	398,507	69,928	72,842	143,442	575,698	183,040	64,235	46,744	107,307	47,050	6808	59,472	19893	127,180	65,401	3681	508.28	320,000	24.00
METROPOLITANA DE SANTIAGO	136,585	126,913	3,325,682	3,523,628	467,643	469,789	933,218	4,146,257	1,095,901	380,000	271,187	652,013	277,073	59,711	443,393	154,802	1,204,060	694,631	34,255	4051.6	421,516	3.40
ÑUBLE	75,151	71,778	157,436	176,244	29,300	32,164	66,179	262,002	90,964	30,540	21,924	47,878	22,619	3445	27,842	9490	90,531	51,768	1623	213.7	300,000	3.46
TARAPACÁ	14,422	6071	153,371	156,694	26,392	26,879	48,407	190,099	38,781	18,291	12,426	26,326	10,636	3101	20811	6102	31,235	17,877	1637	190.06	383,000	0.35
VALPARAÍSO	84,975	78,352	795,240	857,335	114,448	118,408	243,269	997,742	342,035	116799	79,422	163,949	82,756	14,733	109,496	41,794	267,035	145,398	7863	901.13	380,000	3.64

. With the initial information consolidated, we developed the proposed method under study.

Table 5. Relevant information for each factor.

Factors	Indicator	Abbreviation	Code
Sociodemographic	MEN—RURAL AREA	MRA	1
	WOMEN—RURAL AREA	WRA	2
	MEN—URBAN AREA	MUA	3
	WOMEN—URBAN AREA	WUA	4
	0 TO 4	P-0_4	5
	5 TO 9	P-5_9	6
	10 TO 19	P-10_19	7
	20 TO 59	P-20_59	8
	60 OR MORE	P-60_m	9
	SINGLE-PERSON HOUSEHOLD	SPH	10
	NUCLEAR HOUSEHOLD—SINGLE-PARENT	NHSP	11
	NUCLEAR HOUSEHOLD—COUPLE WITH CHILDREN	NHCCH	12
	NUCLEAR HOUSEHOLD—COUPLE WITHOUT CHILDREN OR DAUGHTERS	NHCNOCH	13
	COMPOSITE HOUSEHOLD	CH	14
	EXTENDED HOUSEHOLD	EH	15
	NON-CORE HOUSEHOLD	NCH	16
	ADULTS WITH DISABILITIES	AWD	17
	DEPENDENT ADULT	DA	18
	MARITAL STATUS	MS	19
Socioeconomic	EMPLOYED POPULATION	EP	20
	MEDIAN MONTHLY INCOME	MMI	21
Psychological	PSYCHOSOCIAL PROBLEMS	PPs	22

shows the numerical relation of each criterion.

5. Results

The criteria weights obtained by CRITIC are shown in Table 6 and Figure 3.

Table 6. Results for weight of each indicator.

Abbreviation	Code	Weight
MRA	1	0.1228
WRA	2	0.1278
MUA	3	0.0215
WUA	4	0.0215
P-0_4	5	0.0206
P-5_9	6	0.0205
P-10_19	7	0.0205
P-20_59	8	0.0207
P-60_m	9	0.0208
SPH	10	0.0207
NHSP	11	0.0207
NHCCH	12	0.0205
NHCNOCH	13	0.0206
CH	14	0.0214
EH	15	0.0208
NCH	16	0.0211
AWD	17	0.0219
DA	18	0.0216
MS	19	0.0216
EP	20	0.0213
MMI	21	0.1830
PP	22	0.1881

Table 6. Results for weight of each indicator.

Abbreviation	Code	Weight
MRA	1	0.1228
WRA	2	0.1278
MUA	3	0.0215
WUA	4	0.0215
P-0_4	5	0.0206
P-5_9	6	0.0205
P-10_19	7	0.0205
P-20_59	8	0.0207
P-60_m	9	0.0208
SPH	10	0.0207
NHSP	11	0.0207
NHCCH	12	0.0205
NHCNOCH	13	0.0206
CH	14	0.0214
EH	15	0.0208
NCH	16	0.0211
AWD	17	0.0219
DA	18	0.0216
MS	19	0.0216
EP	20	0.0213
MMI	21	0.1830
PP	22	0.1881

Then, the hybrid model of WASPAS and TOPSIS is implemented.

The WASPAS scores and ranks are as shown in

Table 7. Results of WASPAS scores for each region.

ID	Region	${\mathit{S}}_{\mathit{i}}^{\mathit{W}\mathit{A}\mathit{S}\mathit{P}\mathit{A}\mathit{S}}$	Rank
A1	ANTOFAGASTA	0.12161	10
A2	ARICA Y PARINACOTA	0.077918	16
A3	ATACAMA	0.09185	13
A4	AYSÉN DEL GENERAL CARLOS IBÁÑEZ DEL CAMPO	0.086856	15
A5	BIOBÍO	0.18333	6
A6	COQUIMBO	0.14292	8
A7	LA ARAUCANÍA	0.20623	3
A8	LIBERTADOR GENERAL BERNARDO O’HIGGINS	0.1918	4
A9	LOS LAGOS	0.18285	7
A10	LOS RÍOS	0.11996	11
A11	MAGALLANES Y DE LA ANTÁRTICA CHILENA	0.10168	12
A12	MAULE	0.2095	2
A13	METROPOLITANA DE SANTIAGO	0.38398	1
A14	ÑUBLE	0.13304	9
A15	TARAPACÁ	0.087162	14
A16	VALPARAÍSO	0.19055	5

:

The TOPSIS scores and ranks are shown in

Table 8. Results of TOPSIS scores for each region.

ID	Region	${\mathit{S}}_{\mathit{i}}^{\mathit{T}\mathit{O}\mathit{P}\mathit{S}\mathit{I}\mathit{S}}$	Rank
A1	ANTOFAGASTA	0.11018	11
A2	ARICA Y PARINACOTA	0.028086	16
A3	ATACAMA	0.052145	13
A4	AYSÉN DEL GENERAL CARLOS IBÁÑEZ DEL CAMPO	0.048315	14
A5	BIOBÍO	0.26128	5
A6	COQUIMBO	0.16179	8
A7	LA ARAUCANÍA	0.27901	4
A8	LIBERTADOR GENERAL BERNARDO O’HIGGINS	0.24404	6
A9	LOS LAGOS	0.22853	7
A10	LOS RÍOS	0.11135	10
A11	MAGALLANES Y DE LA ANTÁRTICA CHILENA	0.08478	12
A12	MAULE	0.28241	3
A13	METROPOLITANA DE SANTIAGO	0.96042	1
A14	ÑUBLE	0.15	9
A15	TARAPACÁ	0.047757	15
A16	VALPARAÍSO	0.28511	2

.

The combined scores and ranks are presented in

Table 9. Results of combined scores for each region.

ID	Region	${\mathit{S}}_{\mathit{c}}$	Rank
A1	ANTOFAGASTA	0.21571	10
A2	ARICA Y PARINACOTA	0.11608	16
A3	ATACAMA	0.14675	13
A4	AYSÉN DEL GENERAL CARLOS IBÁÑEZ DEL CAMPO	0.13825	15
A5	BIOBÍO	0.37475	6
A6	COQUIMBO	0.27033	8
A7	LA ARAUCANÍA	0.4138	3
A8	LIBERTADOR GENERAL BERNARDO O’HIGGINS	0.3768	5
A9	LOS LAGOS	0.35708	7
A10	LOS RÍOS	0.21417	11
A11	MAGALLANES Y DE LA ANTÁRTICA CHILENA	0.17654	12
A12	MAULE	0.41982	2
A13	METROPOLITANA DE SANTIAGO	1	1
A14	ÑUBLE	0.25133	9
A15	TARAPACÁ	0.13836	14
A16	VALPARAÍSO	0.39655	4

.

Figure 4 shows the rank comparison of the alternatives using WASPAS, TOPSIS, and combined rank methods. The horizontal axis represents the 16 alternatives labeled from 1 to 16, and the vertical axis represents the ranks assigned to them, with “1” being the best rank (most preferred) and 16 being the worst rank (least preferred). The blue circle line represents the WASPAS ranks, and the orange square line shows the rank results from the TOPSIS method. Finally, the yellow diamond line shows the combined ranks from WASPAS and TOPSIS.

Figure 4 shows the scores for the 16 political regions of Chile in the horizontal axis as alternatives in this study (see Table 6 or Table 7 for the assignment of each label). The graph reflects the combined WASPAS-TOPSIS results, normalized between 0 and 1, where a higher score indicates a better-performing alternative. The evaluation of the proposed hybrid MCDM model strength was achieved through a sensitivity analysis that modified essential parameters. We examined the effect of varying λ values in WASPAS from 0.1 to 0.9 and CRITIC-derived weights on the results. The model demonstrated stability through consistent ranking results for the highest- and lowest-ranked areas across various scenarios. Middle-ranked options experienced minor variations, although they did not significantly affect the overall selection priorities. This study demonstrates that the methodology produces reliable and meaningful results for emergency preparedness area prioritization.

6. Discussion

The three aspects considered in our case study, sociodemographic, socioeconomic, and psychological perspectives, have significant implications for evaluating alternatives. Criteria related to women and men living in rural areas are highlighted under the sociodemographic factors and median monthly income under the socioeconomic factors, and psychosocial problems are considered in the last criterion. The results show that the psychological perspective used as part of the evaluation criteria affects the decision on resource allocation in evacuation plans, confirming previous studies that suggested advancing work that integrates the human factor and the interdisciplinary interaction of various sciences [9,11]. Other works carried out similar explorations, more focused on pedestrian behavior in limited simulation scenarios, without necessarily considering psychological aspects related to the behavior of people on a mass scale [74,75].

Using the CRITIC method for differentiating the criteria by weights, it can be inferred from the evidence of the results that the psychosocial and economic criteria, particularly the average monthly income, are adequate factors for evaluating the alternatives. This is because high weights show high variability and low correlation between the factors. In particular, high variability allows for a more accurate interpretation of the differences between the alternatives, which makes it easier to identify which of the options best explains the influence on the decision of the best alternative.

The CRITIC-derived weights (Table 6) show that psychosocial (PP) and socioeconomic (MMI) factors are the most important criteria, which is consistent with the interdisciplinary focus introduced in Section 1, where human behavior and economic disparities were highlighted as important determinants of evacuation effectiveness.

The Santiago Metropolitan Region proved to be the most vulnerable area based on all three methods (WASPAS, TOPSIS, and combined scores). The results match the population density of the area together with its income inequality (MMI’s weight of 0.183) and psychosocial issues (PP’s weight of 0.188). The combination of population density with economic and social challenges creates conditions that increase emergency evacuation dangers because poor resource distribution and crowd control become more difficult. The lowest scores went to Arica y Parinacota because it had small population numbers and minimal psychosocial stress, which confirms human elements play a crucial role in vulnerability evaluations. The different rankings between WASPAS and TOPSIS methods became evident when Valparaíso received the fifth position in WASPAS but rose to the second position in TOPSIS. The WASPAS method uses linear additive aggregation to favor regions, which perform well in all criteria such as Libertador O’Higgins. TOPSIS uses geometric proximity to ideal solutions, which enhances the results of Valparaíso because it performs well in psychosocial and economic indicators. The combined method reduced these biases while providing a strong compromise. The application of hybrid MCDM systems proves effective for identifying multidimensional vulnerability.

The case results showed differences between the WASPAS and TOPSIS methods. For example, the Valparaíso region was ranked much lower by TOPSIS than by WASPAS. In contrast, for Libertador General Bernardo O’Higgins, the TOPSIS ranking was better than that of WASPAS. This could be because WASPAS emphasizes the importance of variability and the best assignment, while TOPSIS favors alternatives that move away from the negative-ideal solution. There were also minor differences of one position in the rankings of the other regions.

The combined rank appears to average out the differences between WASPAS and TOPSIS. With the previous normalization of each score, the combined rank tended to define divergences in value, positioning the alternative between both. Observing all the results, we see consistent rankings across the WASPAS, TOPSIS, and combined methods, suggesting stability in their performance.

Metropolitana de Santiago achieved the highest combined score, close to 1; this indicates that it is the preferred choice considering both WASPAS and TOPSIS results. Alternatives such as Tarapacá, Aysén del General Carlos Ibañez del Campo, and Arica y Parinacota have the lowest combined scores, indicating that they are the least favorable choices regarding the focus of this study.

Most alternatives clustered around the lower score range (below 0.4), suggesting a significant performance gap between the top-ranked alternatives (e.g., Metropolitana de Santiago) and the rest. Alternatives such as La Araucanía, Maule, and Valparaíso had moderate combined scores, indicating average performance compared to others.

The results suggest that alternatives with consistent rankings across methods (e.g., Metropolitana de Santiago) can be confidently selected as robust choices. Alternatives with divergent rankings (e.g., Maule and Valparaíso) require further analysis or refinement to understand the reasons for the discrepancies (e.g., different weight sensitivities and method assumptions).

Regarding methodology, combined rankings provide a balanced perspective by mitigating the biases of individual methods, and divergence between methods highlights the importance of using several multiple decision-making approaches to validate the results.

Figure 3 shows how rankings differ across the WASPAS, TOPSIS, and their combined approach. Decision-makers should prioritize alternatives with consistent ranks across methods and investigate those with significant disparities to ensure robust and reliable outcomes. Metropolitana de Santiago is clearly the preferred alternative, with significant performance gaps between the top- and lower-ranked options. Decision-makers should prioritize this alternative, while middle-performing alternatives could be considered backup choices.

A robust multicriteria decision-making model that considers factors from different perspectives and disciplines, assessing the geographical, socioeconomic, and psychosocial behavior of the population, enables investment in plan design for more vulnerable populations. Integrating such a model could help reduce economic damage and casualties from disasters in Chile. Vulnerability implies a high risk of facing human losses, because the population does not have the resources or psychosocial capacities to face a catastrophe adequately, but the government of a country can collaborate in education, prevention, and support of adequate infrastructure to avoid material and, above all, human losses. By combining education, prevention, and infrastructure support, the economic damage and percentage of casualties from natural disasters could be significantly reduced compared to a typical disaster response [76]. Given that Chile has high income inequality, class divisions, and targeted social policies [77], the country, like others in Latin America, needs mechanisms to identify affected individuals, assess post-disaster needs, and determine eligibility for social protection support [78,79] to ensure vulnerable populations receive the necessary attention and resources.

In recent years, Chile has been increasingly affected by large wildfires, especially in the central and southern regions [80]. By implementing a robust multicriteria decision-making model, increasing disaster education and prevention, and providing adequate infrastructure support, the economic damage and percentage of casualties from such events could be significantly reduced.

7. Conclusions

This work has contributed a comprehensive analysis of factors from diverse perspectives, including sociodemographic, socioeconomic, and psychological aspects. This integrated approach helps evaluate the importance of considering human factors in designing effective evacuation planning for emergency situations. According to the literature, there have been limited advancements in this direction, and the techniques of MCDM provide valuable tools for evaluating various factors and supporting related decisions.

The proposed method of using the synergies of the two MCDM models allows the incorporation of the particular benefits of each technique. With WASPAS, we considered the influence of variability and correlation between the criteria. With TOPSIS, geometric distances to the ideal solution could be considered, resulting in a proposal with more information for the decision-maker. Using the CRITIC method allowed us to ensure a difference in importance between the criteria for the prior calculation of criteria weights.

Our MCDM methodology allows us to easily include further criteria such as those related to the socioeconomic, sociodemographic, and psychosocial development.. We suggest that further studies be conducted in other countries with our suggested methodology to allow advances in the definitions of strategies and investments, focusing on human-centered catastrophic events. The limitation of this research is the availability of reliable official psychosocial data. Future studies could use uncertain information with grey or fuzzy numbers or apply machine learning techniques to help in the prediction of results of some factors and to obtain some parameters, such as $\lambda$ in the WASPAS method, which could be calculated with optimization and machine learning techniques. Furthermore, merging different MCDM methods and joining them is of interest in ensuring that the balance results in the final score.

The case study in this work has presented us an opportunity to apply MCDM methods to analyze criteria of different origins. These types of decisions require different perspectives for a unique problem. In future studies applying MCDM methods, further factors related to climate conditions which may cause catastrophes should be considered. In addition, it is important to address the human factor in the management of cities and consider sustainable policies in urban planning.