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The frequency and intensity of both human-made and natural disasters are predicted to increase, and hospitals play a critically important role in reducing injury and mortality rates. However, there is increasing evidence that many hospitals are vulnerable to disasters, and more effective strategies are needed to enable the safe evacuation of patients to alternative healthcare centres. Transport planning is central to this process, but there has been no systematic and critical review of the research on this critically important challenge. This means there is no collective synthesis of this literature for hospital managers, policymakers, and researchers to refer to in addressing these important vulnerabilities. This paper reports the findings of a critical literature review to contribute practical insights for health facilities planning and management decision-making in a context where both the likelihood and consequences of natural disasters are increasing in many countries.
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Hospital Evacuation Modelling: A Critical Literature Review on Current
Knowledge and Research Gaps
Maziar Yazdani
1
1*, Mohammad Mojtahedi1, Martin Loosemore2, David Sanderson1, Vinayak Dixit3
1School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia
2School of Built Environment, University of Technology Sydney, Sydney, NSW 2007, Australia
3School of Civil and Environmental Engineering, UNSW Sydney, Sydney NSW 2052, Australia
Abstract: The frequency and intensity of both human-made and natural disasters are predicted to increase, and
hospitals play a critically important role in reducing injury and mortality rates. However, there is increasing evidence
that many hospitals are vulnerable to disasters, and more effective strategies are needed to enable the safe evacuation
of patients to alternative healthcare centres. Transport planning is central to this process, but there has been no
systematic and critical review of the research on this critically important challenge. This means there is no collective
synthesis of this literature for hospital managers, policymakers, and researchers to refer to in addressing these
important vulnerabilities. This paper reports the findings of a critical literature review to contribute practical insights
for health facilities planning and management decision-making in a context where both the likelihood and
consequences of natural disasters are increasing in many countries.
Keywords: Built Environment; Disasters; Evacuation; Transportation; Hospital
Highlights
Relevant literature in transportation planning studies in hospital evacuation is reviewed.
Problem characteristics of hospital evacuation and modelling techniques are investigated.
Optimization and simulation-based solution methods are studied.
Research gaps in existing studies are identified, and recommendations for future work are presented.
1 Introduction
Over recent decades there has been a steady rise in the number, scale, and intensity of human-made and natural
disasters, including pandemics, wildfires, terrorist attacks, earthquakes, storms and major floods (Anshuka et al. 2021).
Recent disasters have highlighted the vulnerability of hospitals in many countries across the world (Aghapour et al.
2019). Some notable examples include the severe impacts on hospitals caused by the 2011 monsoon season floods in
Thailand (Rattanakanlaya et al. 2018), the health facilities impacts of natural disasters such as Cyclone Yasi in Australia,
Hurricanes Harvey and Katrina in the United States (Chand and Loosemore 2016), and flooding events in India (Chong
et al. 2018). Many hospitals have been built in hazard-prone areas (Field et al. 2012). In the Latin American and
Caribbean region alone, it has been estimated that 50% of healthcare facilities are located in high-risk regions (Bagaria
et al. 2009). High-income countries such as the United Kingdom are also vulnerable with some estimates indicating that
70% of hospitals are located in flood-prone areas (Bagaria et al. 2009). Similarly, in Australia, many hospitals are located
either close to rivers and exposed oceans or situated in the cyclone belt (Rojek and Little 2013). Thus, numerous
governments and hospital organizations are making more effort to find effective strategies to make hospitals more
resilient to disasters.
Although numerous researchers have recognised the pivotal role of resilient hospitals in a disaster response, there has
been little research devoted to the vulnerability of hospitals to providing reliable healthcare services to the community
during disasters. Disaster risk management research in the health sector has evolved in recent years(Bongiovanni et al.
2017); however, there are still many unresolved challenges that require further research. One such challenge is effective
evacuation planning for healthcare facilities (Childers and Taaffe 2010) which is widely recognised as one of the most
crucial aspects of emergency management in reducing the incidence of mortality from disasters(Haghani 2020, 2020,
2020).
The United Nations Office for Disaster Risk Reduction (UNISDR) defines evacuation as "Moving people and assets
temporarily to safer places before, during or after the occurrence of a hazardous event in order to protect them" (UNISDR 2009). It
is difficult to define clear boundaries for the academic literature in this rapidly growing multidimensional study field.
However, studies in the evacuation research area can be divided into two broad categories, including evacuation from
buildings (or other structures such as stadiums), e.g. (Aleksandrov et al. 2019; Bhushan and Sarda 2013; Marzouk and
Mohamed 2019; Poulos et al. 2018; Rendón Rozo et al. 2019;  ; Zhang 2017; Tubbs and
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* Corresponding author
E-mail addresses: Maziar.yazdani@unsw.edu.au (M.Yazdani), M.mojtahedi@unsw.edu.au (M.Mojtahedi),
Martin.loosemore@uts.edu.au (M.Loosemore), David.sanderson@unsw.ude.au (D.Sanderson), V.dixit@unsw.edu.au (V.Dixit)
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Meacham 2007); and evacuation from a region, e.g. (Kimms and Maiwald 2018; Lim, Lim, and Piantanakulchai 2019;
Quagliarini et al. 2018; Veeraswamy et al. 2018; Bayram 2016; Yazdani, Mojtahedi, and Loosemore 2020). Hospital
evacuation is another broad research area in evacuation problems that can be defined as a subcategory in both the above
research domains. Tekin et al. (2017) defined evacuation in a hospital context as "Emptying an entire hospital or a part of
it, which is insecure for patients and their relatives due to internal and external factors and transferring people to safer zones".
Although many countries have experienced hospital evacuation due to natural disasters such as wildfires, earthquakes,
floods and storms in recent years (Griffin et al. 2019; Haverkort et al. 2016; Lovreglio et al. 2019; Sandra et al. 2017), the
problem of evacuation planning for healthcare facilities has received less attention (Childers and Taaffe 2010;
Poppenborg and Knust 2016; Rojek and Little 2013).
Tayfur and Taaffe (2009) stated studies about hospital evacuation could be classified into two major groups, including
i) behavioural and social science and ii) modelling and operations. Hospital evacuation studies have been
predominantly restricted to the former group and explored emergency preparedness training or investigating previous
experiences (VanDevanter et al. 2017; Quarantelli 1980; Sorensen and Mileti 1987; Vogt 1990; Sorensen 1991; McGlown
2001). Relatively few studies have addressed the latter, i.e. evacuation modelling and operations (Childers and Taaffe
(2010). They can be broken down into two categories: 𝑖) the transport of the patients, medical staff, and supporting
equipment from inside the hospital to an area outside or inside the hospital; and 𝑖𝑖) the transport of the patients, medical
staff, and supporting equipment from a staging area to a set of receiving hospital(s) using transportation vehicles such
as ambulances (Tayfur and Taaffe 2007, 2009). As Childers and Taaffe (2010) assert, the second type of evacuation
modelling research has received the least attention in this specific body of research.
Addressing the lack of research in this area is important because, in recent years, many countries have experienced
major hospital evacuations in response to a wide range of disasters, including wildfires, earthquakes, major floods and
storms (Lovreglio et al. 2019). For example, Tropical Cyclone Yasi struck the northeast coast of Australia in 2011, causing
thousands of residents to be evacuated, including all patients from two major tertiary hospitals in Cairns (Loosemore,
Chow, and Harvison 2013). This is a recurring problem in Australia (Little et al. 2012) and in many other countries. For
example, in the United States (US), Hurricanes Rita and Katrina caused 58 hospitals to be evacuated, involving more
than 15,000 hospital patients, staff and visitors. In 2009, Pomerado Hospital, a 107-bed community hospital in Southern
California, was also evacuated to avoid the threat by wildfires (Barnett, Dennis-Rouse, and Martinez 2009). In Japan in
2011, after the Fukushima Daiichi accident, at least 50 elderly patients died during the evacuation from a hospital to
other healthcare facilities (Tanigawa et al. 2012).
Evacuating patients from an at-risk hospital to safe areas is much more complex than evacuating most other types of
facilities (e.g., an office building) due to the complications inherent in the patients' conditions. Patients require assistance
and medical care throughout the evacuation process (Hasegawa et al. 2016); therefore, the evacuation does not end
when the building is clear and must include transportation of patients to alternative hospital facilities. Because of the
complexities involved in hospital evacuation planning, immediate and accurate decision-making methods to cope with
emergency conditions are becoming more important. In response, the development and use of novel models and
concepts to support decision making in health care are increasing, and a variety of techniques are being applied to the
evacuation context, including optimisation, simulation paradigms, and hybrid approaches (Chen, Guinet, and Ruiz
2015; Rambha et al. 2021).
Despite the growing importance and interest in this research area, no systematic review of the literature has been
conducted. This paper presents a critical literature review of the existing research in this nascent and increasingly
important field of study to identify gaps in the current literature to inform future research and policy in this critically
important area.
2 Scope of the review
Evacuation is a complex and multiphase process that relies heavily on detailed planning (Shahparvari 2016). There is
no agreed model to define different steps of a hospital evacuation. Therefore, this research adopts the steps proposed
by Shahparvari et al. (2015). To narrow the scope of this review, different steps of the hospital evacuation process, as
shown in Figure 1, are discussed below.
Figure 1. Evacuation phases (adoption from: (Shahparvari et al. 2015))
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Incident detection: The first step in evacuation planning is incident detection. Disasters are associated with uncertainty
about the exact time of occurrence and the severity of the consequences (Yu and Lai 2011). Many countries are investing
considerable effort into developing innovative warning systems to give longer lead times on impending hazard events
than ever before possible (Hosseini et al. 2020). As a result, evacuation planning has improved considerably, although
recent post-event reviews have highlighted failures within the warning systems (Anderson-Berry et al. 2018).
Evacuation decision: Depending on the governance structures and systems in place, an evacuation decision-maker
can be an individual or an organisation/governance structure (Rojek and Little 2013). The role of the decision-maker
is to evaluate all aspects and consider risks, time, costs and uncertainties (Tayfur and Taaffe 2009), and when managers
make the wrong decisions, the consequences can be catastrophic. For example, 34 patients at a nursing home in
Louisiana, USA, drowned in 2005 after management decided to shelter-in-place (Dosa et al. 2007). In contrast,
unnecessary evacuation may put patients with severe conditions at even greater risk and waste precious resources
which could be used for healthcare services. For example, during Hurricane Irene in 2011, many hospitals in
Manhattan pre-emptively evacuated after the Mayor of New York City issued mandatory evacuation orders costing
millions of dollars in lost revenue when the hurricane was not as threatening as initially believed (Ricci et al. 2015).
Preparation for evacuation: In preparing for evacuation, patients should be assured of their required medications,
support structures and medical records. For example, during Hurricane Katrina, a psychiatric hospital in New Orleans,
USA, evacuated very successfully because the supply of medications for all patients for ten days was prepared before
the disaster struck (Thomas and Lackey 2008). In contrast, patients in a Washington, USA hospital were moved with
no care plans or medical records (Blumhagen 1987), and some patients evacuated from Denver Veteran's
Administration Medical Centre had to be moved twice since the first hospital they were evacuated to did not have the
necessary dialysis services (Blaser and Ellison III 1985). Communication, particularly between hospitals, is very
important in preparing for hospital evacuation (Bernard and Mathews 2008; Fuzak et al. 2010).
Movement through the evacuation network: This step is the most overlooked step of a hospital evacuation in the
literature (Bish, Agca, and Glick 2014) due to the challenges of moving patients, staff and equipment to other hospitals
using the transport network (Tayfur and Taaffe 2009). Poor resources planning and scheduling of vehicles such as
ambulances have been cited as some of the most frequent problems (Rojek and Little 2013). An evacuation is a
complicated traffic dynamic considering different interactions among fragmented systems and agents such as private
vehicles, rescue vehicles and evacuees (Chun and Nam 2019). In addition, failure to assign and schedule scarce
resources such as ambulances, nurses and medical equipment may worsen the disaster situation and increase the
number of casualties (Yaghoubi et al. 2017). To optimise hospital evacuation planning strategies, quantitative models
have proven successful in improving the immediate decision-making process (Bish, Agca, and Glick 2014).
Return to the hospital: The final step in the evacuation process is returning patients, staff, and resources to the hospital
when the disaster risk has passed. This process brings its own set of challenges such as damaged facilities, insufficient
staff and missing medical records (Blumhagen 1987).
While each of the above steps merits more research, this paper focuses on step IV (Movement through the evacuation
network) since research in this area is particularly scarce, and this is where quantitative decision-making models can
make the biggest difference (Bish, Agca, and Glick 2014). Therefore, the following section presents a critical literature
of the existing state of research in this area and identifies gaps for future research in mathematical and simulation-
based decision-making models in the under-researched field of evacuation management. In particular, two research
questions are addressed:
Research Question 1. What is the current status of studies in the application of mathematical and simulation-based
decision-making models in the field of the hospital evacuation transport problem?
Research Question 2. What are the limitations of the existing hospital evacuation transport models that are discussed
in this literature?
A systematic search strategy proposed by Tranfield, Denyer, and Smart (2003) is adopted to find the relevant studies.
This methodology is illustrated in Figure 2 and has been widely used in numerous previous disaster management
related studies, such as (Zhang et al. 2019).
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Figure 2. The workflow for reviewing the hospital evacuation planning literature
2.1 Identification of relevant studies
2.1.1 Searching
Electronic searches on Scopus were conducted to identify relevant papers. The Scopus database is the biggest database
of citations and abstracts (Geraldi, Maylor, and Williams 2011). It includes many peer-reviewed journals in social
science, medical, scientific, and engineering fields from a wide range of major and minor scientific publishers, it is
widely regarded as among the most complete and up to date databases (Aghaei Chadegani et al. 2013), and it is broadly
used by researchers to conduct literature reviews in other contexts (Kabirifar et al. 2020).
A structured keyword search was undertaken using the following keywords: evacuation; disaster; hospital;
mathematical; algorithm; optimization; and simulation. Keywords that have a close meaning to 'evacuation' were found
in the first round of analysis and then included in the second round of analysis. For example, in some studies, 'egress'
or 'discharge' are sometimes used instead of evacuation (Shiwakoti 2016). According to (Zhang et al. 2019; Altay and
Green 2006; Gupta et al. 2016), 'catastrophe', 'emergency crisis', 'disaster', and 'hazards' were used to describe the disaster
context. In addition, 'humanitarian' is a term that is closely associated with disaster planning. Hence, 'humanitarian' was
also considered in the keyword list. Keywords with a focus on hospitals were selected from the literature, such as (Chun
and Nam 2019) and (Shabanikiya et al. 2019). Words similar to 'hospital' in meaning, such as 'clinic', 'medical centre', and
'health care centre', were also included in the electronic search. Table 1 presents the exclusion and inclusion criteria used.
Table 1. Inclusion and exclusion criteria used to find relevant studies
Criteria
Inclusion
Exclusion
Full text
Full text is 'Available.'
The full text is 'Not available.'
Document type
Conference and journal papers
Editorial, Erratum and Book chapter
Language
English
Non-English languages, such as Chinese
Keywords were chosen and used in the search process by combining selected keywords with appropriate Boolean
operators. The Boolean operator was executed in the 'article title or abstract or keywords' field of the Scopus search engine
(see Table 2). In addition, 'Source type' was limited to 'Journal' and 'Conference Proceeding'. In addition, the primarily
Scopus search result shows that 'Evacuation' has been used in Medicine, Psychology, and many other fields; however,
they have been excluded from the 'Subject areas' if not relevant to the scope of this research.
Table 2. The search string used on the online digital database
Initial search string
(#TITLE-ABS-KEY#(#hospital#)#OR#TITLE-ABS-KEY#(#clinic#)#OR#TITLE-ABS-KEY#(#healthcare#)#OR#TITLE-
ABS-KEY#(#medical#AND#center#)#AND#TITLE-ABS-KEY#(#disaster#)#OR#TITLE-ABS-
KEY#(#emergency#)#OR#TITLE-ABS-KEY#(#hazard#)#OR#TITLE-ABS-KEY#(#humanitarian#)#AND#TITLE-ABS-
KEY#(#evacuation#)#OR#TITLE-ABS-KEY#(#egress#)#OR#TITLE-ABS-KEY#(#discharge#)#AND#TITLE-ABS-
KEY#(#mathematical#)#OR#TITLE-ABS-KEY#(#algorithm#)#OR#TITLE-ABS-KEY#(#optimisation#)#OR#TITLE-ABS-
KEY#(#simulation#))
The Scopus electronic search identified 1367 relevant papers from the years 2000 to June 2021. All the obtained papers
with their abstracts were exported into Endnote X8 commercial reference management software.
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
 



 
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
 






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2.1.2 Screening
The titles, abstracts, and keywords of obtained papers were evaluated in the next stage to remove studies not suitable
for inclusion in the final analysis. For example, those papers, such as 'Simulation of a hospital evacuation including
wheelchairs based on modified cellular automata' (Zou, Lu, and Li 2019), focused on building evacuation were removed.
Overall, 366 papers remained for further analysis. In the next step, the contents of papers were briefly analysed to
identify irrelevant papers. Similar documents that did not focus on hospital evacuation from a hospital to other hospitals
were removed. Furthermore, the primary focus of some studies was on assessing the current situation; therefore, they
were not taken into account in this review. After the final round of screening, ultimately, less than ten papers were
remained in hospital evacuation planning from one hospital to other hospitals. In addition, some conference papers,
such as (Taaffe and Tayfur 2006; Tayfur and Taaffe 2007, 2007), an extended version published in journals, were
removed from the final list to prevent double-counting. In addition, some studies in hospital evacuation problems like
Bish et al. (2017), which used the same modelling approach for the transportation planning phase of the evacuation as
Bish, Agca, and Glick (2014), were not considered in the final phase of the research. Even though there are many studies
dedicated to exploring the past experiences of hospitals compromised by natural disasters, few modelling studies
remained after screening. The results of the systematic search were eight studies (see Table 3).
Table 3. Search results
Reference
Type of the source
Taaffe, Johnson, and Steinmann (2006)
Conference
Tayfur and Taaffe (2009)
Journal
Tayfur and Taaffe (2009)
Journal
Bish, Agca, and Glick (2014)
Journal
Chen, Guinet, and Ruiz (2015)
Journal

Journal
Kim et al. (2020)
Conference
Rambha et al. (2021)
Journal
These articles are briefly summarised. Taaffe, Johnson, and Steinmann (2006) applied simulation to estimate the time
required to evacuate patients from a hospital to receiving healthcare centres, assuming there are three different types
of patients and patients could be moved to three receiving hospitals using a fixed number of ambulances. Tayfur and
Taaffe (2009) developed a mixed integer programming formulation to find the scheduling and allocation of resources
to minimise total operating and evacuation costs. The model provided the optimum number of patients of each type to
be evacuated to different receiving hospitals in different vehicles across time, the number of available vehicles and the
number of nurses for staging and transport. Tayfur and Taaffe (2009) used s-shaped curves and triangular distributions
to model stochastic vehicle travel times and waiting times and a heuristic solver to optimise the number of vehicles and
nurses needed to evacuate patients at minimum cost. In addition, the impact of the start time on the cost and duration
of the evacuation was also investigated. Bish, Agca, and Glick (2014) proposed an optimisation formulation to find the
optimal strategy to allocate patients to different receiving hospitals. They considered two different kinds of risk in the
model. The first risk was from staying (threat risk), and the second risk was associated with transport. The capacities of
receiving hospitals, different types of patients and rescue vehicle limitations were considered. Chen, Guinet, and Ruiz
(2015) presented a simulation model to assess and optimise the evacuation of a single hospital prior to the event of a
flood, considering both internal and external resources such as stretchers and ambulances. In this research, a factorial
experimental design was developed and carried out in order to evaluate the sensitivity of the evacuation time in relation
to main factors.  suggested a bi-objective mathematical model
for hospital evacuation, minimising the total weighted number of unevacuated patients in each period and the total
evacuation time. A possibilistic programming approach was adopted to address the uncertain nature of parameters,
and two metaheuristic algorithms, imperialist competitive algorithm and genetic algorithm, were applied. For patient
evacuation, Kim et al. (2020) developed a two-stage stochastic mixed-integer programming framework. The model
determines the location of staging sites and the number of vehicles to mobilise in the first stage, and the vehicle route
assignments in the second stage, while reducing the anticipated total cost of patient evacuation operations. The model
was evaluated using real-world data from the Southeast Texas area. Rambha et al. (2021) utilised a multi-stage stochastic
programme to identify the optimum number of patients of different types who must be evacuated at different time
periods to different hospitals, as well as the vehicles used to carry them, to address the problem of evacuating hospitals
during hurricanes. The problem's objective was to minimize a linear combination of risk and cost associated with
evacuation. Because the evolution of a hurricane is exposed to significant uncertainty, they employed a scenario-tree-
based method to describe uncertainty. Predicted storm trajectories were utilised to build a scenario tree, which served
as the foundation for a stochastic optimisation problem. Model applications were shown through a hypothetical
evacuation of a hospital in North Carolina.
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2.2 Content analysis
After finding the relevant papers in the previous step, a content analysis was conducted to investigate the research
questions stated above using the coding framework in Figure 3, which is explained in detail below. This study used a
subcoding approach (Saldaña 2015) which has widely been used by researchers in reviewing modelling problems, such
as (Eksioglu, Vural, and Reisman 2009; Braekers, Ramaekers, and Van Nieuwenhuyse 2016). Subcoding is appropriate
for content analyses and research with a broad range of data in journals (Saldaña 2015). Also, subcoding is suitable
when general code entries will later need more extensive categorising and subcategorising into taxonomies or
hierarchies (Saldaña 2015). The primary objective of this section is to present a subcoding framework to define the
domain of the existing hospital evacuation modelling literature in terms that are easy to understand. In the modelling,
the model was constructed based on the most important characteristics of the real-world problem, and then an
appropriate approach is applied to solve the model considering a specific objective (MirHassani and Hooshmand 2019).
Based on this definition, the first level of the proposed taxonomy is similar to (Eksioglu, Vural, and Reisman 2009;
Braekers, Ramaekers, and Van Nieuwenhuyse 2016) and composed of three different aspects: scenario and problem
characteristics; evacuation modelling objectives; and applied solution methods.
1. In the first category, 'scenario and problem characteristics', a description of the problem is given by six attributes. In
hospital evacuation, patients are moved through the road network from hospitals under the risk of a disaster to safe
hospitals considering available resources (). The first attribute is
'patients' as the key component of a hospital evacuation model. In a hospital, there are different groups of patients
with different special resource requirements in evacuation. According to the available models, human resources and
transport resources are two important resources in hospital evacuation models, and each of them may have different
categories. Furthermore, hospitals are categorised into two groups, evacuating hospitals, and receiving hospitals,
each of which has a specific capacity. Characteristics of the disaster and travel times on the road network should also
be incorporated into the model.
2. 'Evacuation modelling objective' is divided into three main groups: time-related, cost-related, and risk-related
objectives.
3. 'Applied solution methods' is divided into five categories: exact methods, heuristics, metaheuristics, simulation, and
real-time solution methods.
Figure 3. The coding framework
In addition, the information in hospital evacuation models, similar to Psaraftis (1980), can be included in two important
dimensions: 'evolution' of information and 'quality' of information. The first dimension recognises the information used
for planning may change within the execution of the plan, while the second dimension indicates possible uncertainties
in the existing data. Similar to the classification proposed by Pillac et al. (2013), for hospital evacuation problems, four
groups can be defined as in Table 4. Although this classification initially was proposed for optimization problems, it
may provide an appropriate perspective for this study.
Table 4. Classification of the problem by information evolution and quality (Pillac et al. 2013)
Information quality
Deterministic input
Stochastic input
Information evolution
Inputs are known beforehand
Static and deterministic
Static and stochastic
Inputs changes over time
Dynamic and deterministic
Dynamic and stochastic
3 Results
In this section, the eight studies are further analysed to address each research question.
7
Research Question 1. What is the current status of studies in the application of mathematical and simulation-based
decision-making models in the field of hospital evacuation transport problems?
3.1 Number of patients and their priorities
This section discusses the number of patients and their priorities in existing hospital evacuation models. In a hospital,
there are different groups of patients with various health conditions. The number of patients and their ready time for
evacuation are very important factors.
Table 5. Number of patients and their classification
Reference
Modelling
approach
Number of
groups
Priority of
groups
Number of
patients
Ready time of
patients for
evacuation
Taaffe, Johnson, and Steinmann
(2006)
Simulation
Multi-group
No priority
Static and
deterministic*
At the beginning of
the evacuation
Tayfur and Taaffe (2009)
Mathematical
Multi-group
No priority
Static and
deterministic
At the beginning of
the evacuation
Tayfur and Taaffe (2009)
Simulation
Multi-group
Yes (critical
patients)
Static and
deterministic
Not at the beginning
of the evacuation
Bish, Agca, and Glick (2014)
Mathematical
Multi-group
No priority
Static and
deterministic
At the beginning of
the evacuation
Chen, Guinet, and Ruiz
(2015)
Simulation
One-group
No priority
Static and
deterministic
Not at the beginning
of the evacuation
Rabbani, Zhalechian, and

Mathematical
Multi-group
Yes (critical
patients)
Static and
stochastic
At the beginning of
the evacuation
Kim et al. (2020)
Mathematical
Multi-group
No priority
Static and
stochastic
At the beginning of
the evacuation
Rambha et al. (2021)
Mathematical
Multi-group
No priority
Static and
deterministic
At the beginning of
the evacuation
*More patients can be added before starting the evacuation
Table 5 shows the majority of studies assumed the number of patients that should be evacuated is fixed and known
at the beginning of the planning horizon, while  and Kim et
al. (2020) considered this parameter as a stochastic parameter. In addition, Taaffe, Johnson, and Steinmann (2006)
assumed more patients could be added before starting the evacuation. In addition, in some studies, it is assumed
that when transportation starts, all patients are already in the nearby staging area, while in real conditions, the
preparation of patients is time-consuming, and they are gradually moved to pick up points like the study by Chen,
Guinet, and Ruiz (2015).
The priority of patients in moving during a hospital evacuation is another challenging decision. In the literature,
Tayfur and Taaffe (2009) and yeh (2018) assumed that critical patients,
who required close monitoring, have the highest priority, while in other studies, it was not considered. Before
starting a hospital evacuation, decision-makers should define the priority of patients in the evacuation. The Centre
for Bioterrorism Preparedness and Planning (2006) advised that patients with a high demand of support have the
highest priority in a hospital evacuation. Similarly, Gray and Hebert (2007) stated for advanced evacuation; hospitals
should follow this strategy. The Association of Perioperative Registered Nurses (AORN) (2007) suggested all
ambulatory patients should be evacuated first, and then remaining patients should be prioritised from the least to
most critical. Johnson (2006) stated, "the implicit objective at each stage is to maximize the number of people who can be
moved to safety in the shortest available period of time." Moskop and Iserson (2007) stated ambulatory patients in a
hospital evacuation are the highest priority compared to those who require high levels of care. Gray and Hebert
(2007) asserted "advance agreement is needed about which patients will be evacuated first." Since there is no single priority
model that will work equally well for all hospitals and all circumstances, perhaps the most reasonable way to make
decisions depends on the situation (Schultz, Koenig, and Lewis 2003).
3.2 Available staff assignment and scheduling
This section discusses the number of available staff and their availability time in the hospital evacuation models. During
a hospital evacuation, patients' special requirements should be considered in the evacuation plan (Bovender and Carey
2006), such as coordination and use of staff (Hyer et al. 2009). When patients are moving to receiving hospitals, some
hospital resources, particularly doctors and nurses, should be reassigned to new facilities with the aim of continuity of
medical care of the patient (Hyer et al. 2009). Human resources assignment and scheduling are still broadly missing
from many hospital transport evacuation plans. In the literature, some studies assumed that there are several groups of
human resources, for example, nurses with different levels of skills, that should be assigned in hospital evacuation
planning including (Taaffe, Johnson, and Steinmann 2006; Tayfur and Taaffe 2009, 2009; Chen, Guinet, and Ruiz 2015),
8
while others did not incorporate human resources in their studies , e.g. (Bish, Agca, and Glick 2014; Rabbani, Zhalechian,
). Rambha et al. (2021) indirectly considered staffing costs in the model using overtime
wage rates and the time spent by patients at the evacuating hospital.
Table 6. Number of available staff and their availability
Author(s)
Modelling
approach
Number of
groups
Number of staff
Ready time of staff for
evacuation
Taaffe, Johnson, and
Steinmann (2006)
Simulation
Multi-group
Static and
deterministic
At the beginning of planning
Tayfur and Taaffe (2009)
Mathematical
Multi-group
Decision variable
At the beginning of planning
Tayfur and Taaffe (2009)
Simulation
Multi-group
Decision variable
At the beginning of planning
Bish, Agca, and Glick (2014)
Mathematical
-
-
-
Chen, Guinet, and Ruiz (2015)
Simulation
Multi-group
Decision variable*
At the beginning of planning
Rabbani, Zhalechian, and

Mathematical
-
-
-
Kim et al. (2020)
Mathematical
-
-
-
Rambha et al. (2021)
Mathematical
-
-
-
* Fixed in each simulation, but in order to assess its impact on evacuation, is changed in different scenarios.
A shortage of staff is a big challenge in hospital evacuation planning (Milsten 2000). Hospitals may have significantly
fewer staff on hand during disasters. For example, after the accidents at the Three Mile Island nuclear power station
in 1979, many hospital staff decided to leave the area to join their families. Therefore many hospitals experienced a
severe staffing problem (Maxwell 1982). In contrast, some hospitals may have staff on standby or can use volunteers
during a hospital evacuation. For example, in the Memorial Hermann Hospital evacuation in the US, a great number
of volunteers helped move patients (Cocanour et al. 2002). During the 2004 hurricanes in the US, although the
majority of nursing homes stated they had enough staff to move patients, maintaining sufficient staffing levels
during the evacuations was challenging (Hyer et al. 2009). Resource assignment and scheduling involve constructing
a schedule for staff and special equipment and resources and assigning them to different sections based on individual
and system preferences. Table 6 shows (Tayfur and Taaffe 2009, 2009; Taaffe, Johnson, and Steinmann 2006; Chen,
Guinet, and Ruiz 2015) considered human resource parameters in their model but assumed all resources were ready
at the beginning of the planning horizon.
3.3 Available transport resources
This section discusses transport resources used in hospital evacuation planning and their availability in the planning
horizon. Transport resources such as ambulances and their accessibility have been documented as a challenging issue
during a hospital evacuation (Hyer et al. 2009).
Table 7. Transport resources and their availability
References
Modelling
approach
Group of
land
vehicles
Number of vehicles
Availability of
vehicle
Except land
vehicles
Taaffe, Johnson, and
Steinmann (2006)
Simulation
Multi-
group
Static and deterministic
At the beginning
of the planning
horizon
No
Tayfur and Taaffe (2009)
Mathematical
Multi-
group
Decision variable
At the beginning
of the planning
horizon
No
Tayfur and Taaffe (2009)
Simulation
Multi-
group
Decision variable
At the beginning
of the planning
horizon
No
Bish, Agca, and Glick
(2014)
Mathematical
Multi-group
Static and deterministic (in the
different periods, there are
different numbers of vehicles)
Time-dependent
No
Chen, Guinet, and
Ruiz (2015)
Simulation
One-type
Decision variable*
At the beginning
of the planning
horizon
No
Rabbani, Zhalechian,

Geranmayeh (2018)
Mathematical
Multi-
group
Static and stochastic (in
different periods, there are
different numbers of
vehicles)
Time-dependent
and stochastic
Helicopter
Kim et al. (2020)
Mathematical
Multi-
group
Decision variable**
At the beginning of
the planning
horizon
No
Rambha et al. (2021)
Mathematical
Multi-
group
Static and deterministic
At the beginning of
the planning
horizon
No
* Fixed in each simulation, but in order to assess its impact on evacuation, is changed in different scenarios.
** The number of buses is limited, while the number of ambulances is not limited.
9
Based on the situation of the patients, different types of vehicles except ambulances may be used (Bovender and Carey
2006; Cocanour et al. 2002; Hyer et al. 2009). For example, commercial vehicles with high seating capacity, such as school
buses, can be used in an evacuation. There is no universal classification for the types of vehicles used in hospital
evacuation. For example, Houston et al. (2009) stated four groups, including buses, ambulances, ambulettes and vans,
that are used during hospital evacuation. Zane et al. (2010) stated hospital evacuation vehicles are Advanced Life Support
(ALS) ambulances, Basic Life Support (BLS) ambulances and buses. The type of the disaster may force the use of other
forms of vehicles (Bovender and Carey 2006; Cocanour et al. 2002; Hyer et al. 2009). In 2011, Tropical Cyclone Yasi
struck northern Queensland, Australia and patients in the two Cairns hospitals were evacuated by air to the state capital
city Brisbane (Little et al. 2012). Boats may be needed in some disasters such as floods. In the days after Hurricane
Katrina in 2005, boats were used to move patients from Louisiana's Charity Hospital (Gray and Hebert 2007).
Sometimes, in the event of a disaster when the number of patients exceeds the availability of ambulances, other modes
of transport can be considered. McGovern and Bogucki (2015) presented comprehensive characteristics of standard and
non-medical vehicles used in the evacuation of patients.
The number of vehicles and their availability in the planning horizon are two critical factors that should be considered
in the modelling. Some studies assumed selecting the number of required vehicles is a decision variable in the model,
or it should be assessed (Tayfur and Taaffe 2009, 2009; Chen, Guinet, and Ruiz 2015; Kim et al. 2020). While some studies
like Rambha et al. (2021) and Taaffe, Johnson, and Steinmann (2006) considered a fixed number of different types of
vehicles that move patients. While Bish, Agca, and Glick (2014) assumed in different periods of evacuation, different
numbers of vehicles are available. However, in various real cases, numerous barriers may challenge these assumptions.
In addition,      considered that the total available number of
vehicles at different periods is stochastic. In addition, Table 7 shows in the literature, almost all studies considered that
there are several types of land vehicles, while      stated air
transport could be considered in the model.
3.4 Evacuating and receiving hospitals and their capacities
This section discusses how the number of evacuating and receiving hospitals and their capacities have been considered
in the existing models. Table 8 shows all previous studies except 
and Kim et al. (2020) considered only one hospital should be evacuated, while all studies except Chen, Guinet, and
Ruiz (2015) assumed there are several receiving hospitals.
Table 8. The number of evacuating and receiving hospitals and their capacities.
References
Modelling approach
Number of
evacuating hospitals
Number of receiving
hospitals
Capacities of
receiving hospital(s)
Taaffe, Johnson, and
Steinmann (2006)
Simulation
Single
Multi
Deterministic but can
be added
Tayfur and Taaffe (2009)
Mathematical
Single
Multi
Static and
deterministic
Tayfur and Taaffe (2009)
Simulation
Single
Multi
Static and
deterministic
Bish, Agca, and Glick
(2014)
Mathematical
Single
Multi
Static and
deterministic
Chen, Guinet, and
Ruiz (2015)
Simulation
Single
Single
Have enough capacity
for all patients
Rabbani, Zhalechian, and

(2018)
Mathematical
Multi
Multi
Static and
deterministic
Kim et al. (2020)
Mathematical
Multi
Multi
Static and
deterministic
Rambha et al. (2021)
Mathematical
Single
Multi
Static and
deterministic
The impact area and the number of hospitals that should be evacuated is an important issue. Either one hospital is being
evacuated or an entire area, different strategies may be followed. Patients can be dispersed between hospitals in the
vicinity if just one hospital should be evacuated. In most metropolitan areas, this transport is less than 10 to 15
kilometres. A suburban or rural hospital may have to send patients farther away to suitable hospitals. In disasters may
cause the evacuation of many hospitals, destinations may be other states with available services and staff (Little et al.
2012). For instance, in 2005, no other hospital in Louisiana could care for all the Children's Hospital of New Orleans'
PICU patients. Therefore these patients were moved to other states (Aucoin 2005).
The receiving hospital capacity and its occupancy rate determine the number of patients that the hospital is able to
receive during an evacuation. Although a hospital may have a plan to transfer patients to specific receiving hospitals,
these proposed receiving hospitals may also have been forced to evacuate or may have already been filled by admitting
other patients with disaster injuries; thus, the intended receiving hospitals may not be available (Childers 2010).
10
Furthermore, some hospitals may not be ready to receive more critical patients due to the shortage of staffing or
resources (Childers 2010). Therefore, it is very important to consider the number of receiving hospitals and their
capacities in the model.
Most of the studies in hospital evacuation assumed that the receiving hospitals have sufficient capacity to accept
patients and are located in regions where there are enough reliable routes to deliver medical, humanitarian and other
services (Campos, Bandeira, and Bandeira 2012). However, in reality, the capacity of other hospitals may be reduced in
disaster situations, or some of them may even be evacuated too. Some hospitals may not be able to accept some special
types of patients. In addition, the majority of studies assumed that the capacity of receiving hospitals is fixed during
the planning horizon.
3.5 Disaster behaviour analysis
This section discusses how natural disasters and their behaviour are considered in hospital evacuation models. While
the specific type of disaster and its characteristics should greatly impact the developed model for hospital evacuation,
far too little attention has been paid to this aspect of hospital evacuation planning.
The majority of prior studies only specified a generic disaster type for the purpose of developing a hospital evacuation
model and did not include disaster parameters or assumptions in their model. With the exception of Kim et al. (2020)
and Rambha et al. (2021) that included some hurricane-related parameters in their models, other studies' mention of
implied a disaster type did not necessarily alter their model, e.g. (Chen, Guinet, and Ruiz 2015).
Table 9. Disasters and their impacts in the hospital evacuation literature
References
Modelling
approach
Considering the disaster
on the modelling
Time of evacuation
Evacuation
planning horizon
Taaffe, Johnson, and Steinmann
(2006)
Simulation
-
Before the disaster
Not limited
Tayfur and Taaffe (2009)
Mathematical
-
Before the disaster
Fixed and limited
Tayfur and Taaffe (2009)
Simulation
-
Before the disaster
Fixed and limited
Bish, Agca, and Glick (2014)
Mathematical
-
Before the disaster
Fixed and limited
Chen, Guinet, and Ruiz (2015)
Simulation
-
Before the disaster
Fixed and limited
Rabbani, Zhalechian, and

Mathematical
-
During the disaster
Fixed and limited
Kim et al. (2020)
Mathematical
Before the disaster
Fixed and limited
Rambha et al. (2021)
Mathematical
Before and during
the disaster
Fixed and limited
In order to model hospital evacuation properly, it is essential to consider the nature of the disaster event, including
affecting time, predictability, scale and impact region, and the expected effects.
For hospital evacuation planning, disasters can be classified into two main groups according to the time of happening:
'Advanced Warning Events' and 'No Advanced Warning Events' (Hachiya et al. 2014). In the case of some impending
disasters such as floods and hurricanes, when the hospital building and neighbouring areas are not yet remarkably
compromised, decision-makers have time before the event, and evacuation takes place before the disaster. In contrast,
in the case of earthquakes, as an example of No Advanced Warning Events, evacuation takes place either during or
immediately after a disaster event. Table 9 shows all studies considered hospitals are evacuated either before or during
the disaster.
The planning horizon is another important factor in hospital evacuation. The majority of studies assumed there is a
boundary for the evacuation period (Table 9). However, this boundary should be defined based on the characteristics
of the disaster. Fang and Zio (2019) provided characteristics of different natural hazards according to their
predictability, region of impact, the area impacted and duration (see Table 10).
Table 10. Characteristics of different natural hazards adopted from (Fang and Zio 2019)
Type of natural hazard
Region of impact
Predictability
Area impacted
Duration
Tropical storm hurricane
Coastal regions
2472 hours, moderate to good
Large (radius up to
500kms)
Hours to days
Drought, Wildfire
Inland regions
Days, good
Medium to large
Days to months
Tsunami
Coastal regions
Minutes to hours, moderate
Small to large
Minutes to hours
Blizzard, ice storm
High latitude regions
2472 hours, moderate to good
Large (up to 1500 kms)
Hours to days
Earthquake
Regions on fault
lines
Seconds to minutes, bad
Small to large
Minutes to days
(aftershock)
Tornado
Inland plains
02 hours, bad to moderate
Small (radius up to 8 kms)
Minutes to hours
Flooding
Low-lying regions
Moderate to good
Small to large
Days to months
Disasters may disrupt the road network and exacerbate the congestion on roads. Although the physical impacts of
disasters for hospital evacuation planning differ significantly across different disaster types, it has not been clearly
addressed in existing research. The impacts of disasters on the road network are discussed in the next section.
11
3.6 Road network, traffic conditions and travel time
This section discusses the impact of disasters on the road network as well as the travel times of vehicles on the road
network. The road network is a critical factor that cannot be ignored in proposing an efficient evacuation plan
(Shahparvari et al. 2016). Although many studies assumed that these systems are operating at or near design capacity,
the road network may face great risk by a disaster such as a flood. When a disaster strikes a road network, some links
may become disrupted. Although this aspect of the hospital evacuation problem is very important, less attention has
been devoted to addressing road disruption in the model. Table 11 shows    
Geranmayeh (2018) assumed that some routes are not available in some periods of evacuation, and route availability is
known before starting the evacuation. Rambha et al. (2021) incorporated the risks associated with wind speeds and
flood depths for different hurricane scenarios in their model and considered time-varying roadway conditions to
estimate risk.
Table 11. Disasters and their impacts on the road network.
References
Modelling
approach
Impact on the routes
Travel time
Taaffe, Johnson, and Steinmann
(2006)
Simulation
Not mentioned
Stochastic and also change during a time
based on congestion of the road
Tayfur and Taaffe (2009)
Mathematical
Not mentioned
Static and deterministic
Tayfur and Taaffe (2009)
Simulation
Not mentioned
Can change during a time based on
congestion of the road
Bish, Agca, and Glick (2014)
Mathematical
Not mentioned
Static and deterministic
Chen, Guinet, and Ruiz (2015)
Simulation
Not mentioned
Stochastic
Rab
Geranmayeh (2018)
Mathematical
Known before
evacuation
Stochastic
Kim et al. (2020)
Mathematical
Not mentioned
Static and deterministic
Rambha et al. (2021)
Mathematical
Risk due to flooding of
highway links
Can change during a time for different
departure times
Moreover, it is expected that travel time during an evacuation will be significantly higher than the travel time under
normal traffic conditions. In the literature, three different approaches were explored. Taaffe, Johnson, and Steinmann
(2006), Tayfur and Taaffe (2009), and Rambha et al. (2021) assumed travel time changes during the evacuation based
on congestion of the road, while, Tayfur and Taaffe (2009), Bish, Agca, and Glick (2014) and Kim et al. (2020)
considered travel time is fixed during the evacuation period. In addition, travel time was incorporated in the hospital
evacuation model as a stochastic parameter by  yeh (2018) and Chen,
Guinet, and Ruiz (2015).
Although the evacuation environment faces a wide range of risks, less attention has been devoted to incorporating
road network performance metrics in hospital evacuation models. Therefore, considering road network performance
metrics in hospital evacuation modelling can help make models more reliable and realistic. Faturechi and Miller-
Hooks (2015) reviewed different performance metrics to evaluate and analyse disaster impacts on transport systems.
Reviewing these performance metrics is out of the scope of this paper but is significantly important for future
research. Resources on metrics include (Faturechi and Miller-Hooks 2015, 2014; Miller-Hooks, Zhang, and Faturechi
2012; Mattsson and Jenelius 2015).
3.7 Evacuation modelling objectives
This section discusses the objectives used in the existing models. Considering the type, time, and severity of the disaster,
and depending on the aim of emergency decision-makers, various objectives can be used in the modelling. Table 12
shows existing objectives in hospital evacuation models can be classified into three main groups: time-related, cost-
related, and risk-related objectives.
According to Table 12, the most frequently used type of objective is cost minimisation (Tayfur and Taaffe 2009, 2009;
Kim et al. 2020; Rambha et al. 2021), while some studies considered time-related objectives in the objective function
(Taaffe, Johnson, and Steinmann 2006; Chen, Guinet, and Ruiz 2015; 
2018).   and Rambha et al. (2021) considered two objectives in
their model.  developed a model to concurrently minimise the
total weighted number of unevacuated patients in each period and the total evacuation time. Rambha et al. (2021)
focused on minimizing a weighted sum of the expected risk and the expected cost of evacuation. Bish, Agca, and Glick
(2014) stated the main goal of a hospital evacuation is to minimise risk to the patients (and staff). Therefore, they
considered minimising the total evacuation risk in the model. In addition to these objectives, some objectives from urban
evacuation planning studies can be used in this field of study for future research, such as minimising the maximum
latency, average evacuation time and the clearance time. (Bayram 2016) provides more information about different
objectives used in urban evacuation models.
12
3.8 Applied solution methods
This section discusses solution approaches applied in hospital evacuation planning. Solving hospital evacuation
problems is challenging, and the computational effort is expected to grow exponentially as the number of variables in
the problem increases. Possible solution methods for hospital evacuation problems can be categorised into five main
groups: exact methods, heuristics, metaheuristics, simulation methods and real-time solution methods.
Exact approaches can find a globally optimum solution. Although the performance of these methods for small size
problems is acceptable (Jourdan, Basseur, and Talbi 2009), they cannot handle practical cases within a reasonable
timeframe. Bish, Agca, and Glick (2014) proposed an integer programming model for the hospital evacuation
transport problem.      proposed mixed-integer linear
programming, aggregating the two objectives of minimisation of total evacuation time and minimisation of total
weighted number of patients yet to be evacuated in each time period into a single objective to solve small problem
instances. Rambha et al. (2021) developed a scenario tree-based stochastic optimization to minimise risk and cost.
In addition, mathematical models were solved by a commercial optimisation solver in Bish, Agca, and Glick (2014);
Tayfur and Taaffe (2009).
Heuristic approaches are developed based on the specific knowledge driven from the problem to achieve a
satisfactory solution.     stated heuristic approaches could be classified into two groups:
'constructive heuristics' and 'improvement heuristics'. Generally, constructive heuristics are used to produce an initial
feasible solution, while improvement heuristics explore the neighbours around the incumbent solution to get better
solutions. Tayfur and Taaffe (2009) tested alternative lower bound models employing a heuristic produced an
allocation-feasible solution of assignments of vehicles, nurses, and patients to particular transfer periods. In
addition, the proposed heuristic had an embedded local search routine to further improve the vehicle and staff
assignment.
Metaheuristic approaches have received considerable attention among practitioners and researchers to solve complex
optimisation with a reasonable computation time (Yazdani and Jolai 2016; Li, Cai, and Kana 2019). Metaheuristic
algorithms were only used by      for the hospital evacuation
problem. They proposed a hybrid imperialistic competitive algorithm and a genetic algorithm to find nearoptimal
solutions for their problem in a short amount of time. The performances of the proposed algorithms were evaluated
based on randomly generated problems.
Simulation approaches are used to model specific behaviour from a real complex system in a natural way (Juan et al.
2015). Although problem details could be considered in the simulation models with no mathematical sophistication,
complicated models might need slow development and challenging validation and verification processes. Besides,
simulation is not an optimisation method by itself (Juan et al. 2015). Consequently, to investigate the behaviour of the
model with respect to both decision and probability space, simulation experiments should be designed. Simulation
approaches were used by Taaffe, Johnson, and Steinmann (2006), Tayfur and Taaffe (2009) and Chen, Guinet, and
Ruiz (2015) in hospital evacuation planning.
Table 12. Different objectives used in the hospital evacuation literature and applied methods in the literature
References
Sim.
Opt.
Obj.
Model objective
Model type
Method(s) or software package
Taaffe, Johnson, and
Steinmann (2006)
Single
Evacuation time
minimisation
Discrete event
simulation
Arena Simulation
Tayfur and Taaffe
(2009)
Single
Cost minimisation
Mixed-integer linear
programming
Commercial optimisation solver
(CPLEX), LP rounding heuristic
Tayfur and Taaffe
(2009)
Single
Cost minimisation
-
Arena Simulation - OptQuest
software
Bish, Agca, and Glick
(2014)
Single
Risk minimisation
Integer programming
formulation
Commercial optimisation solver
(CPLEX)
Chen, Guinet, and Ruiz
(2015)
Time
SIMIO a commercial simulation
software
Rabbani, Zhalechian,

Geranmayeh (2018)
Bi-
Objective
Time and
unevacuated patients
number minimisation
A bi-objective mixed-
integer programming
model
Genetic algorithm (GA); Hybrid
imperialistic competitive algorithm
(HICA); Commercial optimisation
solver (GAMS)
Kim et al. (2020)
Single
Cost minimisation
Stochastic mixed-
integer programming
model
-
Rambha et al. (2021)
Bi-
Objective
Cost and risk
Stochastic
optimization
formulation
Commercial optimisation solver
(CPLEX)
* OptQuest software has been used in the Arena as the optimization tool.
13
4 Conclusions
This research was set within the context of the increasing vulnerability of hospitals to more and the lack of a systematic
critical literature review of the emerging yet fragmented research in this area. Reporting the results of a systematic
literature review of papers published in the last two decades, the following fourteen research gaps have been identified
which merit further attention:
Stochastic modelling: the majority of studies in the evacuation literature assume perfect information about all
aspects of the hospital evacuation is available. However, there is a high level of uncertainty associated with
evacuation situations, which makes deterministic studies not realistic and practical. Many parameters are usually
more predictable in normal situations. Likewise, some parameters can be estimated using historical data in normal
situations, but historical information for emergency situations may not be available. Addressing various aspects of
uncertainty and incomplete information is very important in hospital transport evacuation problems. Furthermore,
there is a great need for developing methods that can handle such problems, as a stochastic planning problem is
significantly more complex than a deterministic one.
Dynamic modelling: So far, too little attention has been paid to dynamic modelling in hospital evacuation
problems. The unavailability of reliable information is a big challenge in emergency planning. In almost all
emergency conditions, gathering the required data for planning is time-consuming, and some data may gradually
be revealed during the evacuation. For example, the number of beds available at each receiving hospital for each
group of patients and the number of human and scarce resources such as ambulances are updated continuously
after gathering more precise information. In addition, the number and status of patients are revealed after triage.
In addition, new disaster-related incidents and the collapse of infrastructure can cause some travel time dynamics.
These issues highlight the importance of studying dynamic models in future research.
Other type vehicles: The majority of the evacuation studies focused on evacuation with ambulances or buses,
while the nature of the disaster may force decision-makers to use modes other than land vehicles. For example, the
impact area of the disaster may be very large, such as a tropical storm hurricane with a radius of up to 500km, and
in this condition, decision-makers have to use aircraft. When a hospital is surrounded by flood, boats can be used
to evacuate patients.
Intelligent transport systems: The emergence of various high-tech transport systems cannot be ignored in
evacuation planning. For example, autonomous vehicles are being introduced as a new tool in urban transport
systems. The adoption of these intelligent transport systems in hospital evacuation planning has tremendous
potential to improve routing operations.
Solution methods: Decision-makers need to make good decisions in a short time. Therefore, with the development
of computers and the computational complexity of models with complex realistic assumptions, finding methods
to increase computational efficiency is becoming a popular topic in this research area. Thus, reducing
computational times to provide timely decision support for managers is becoming more important.
Disaster type: The type of disaster that is remarkably important may highly affect hospital evacuation plans. As
an example, there is little to no warning time in earthquakes, while some disasters such as storms happen with
notice, and planners have time to be ready for evacuation. In addition, disasters may affect road networks. For
example, a flood may disrupt some segments of a low-lying road network. So far, there has not been sufficient
research into incorporating disasters into evacuation models. Therefore, specific and associated types of disasters
should be investigated in future research, particularly sudden-onset disasters such as earthquakes.
Multi-objective and new approaches: The multi-criteria decision problem is still a research gap in the hospital
evacuation problem in comparison with single objective models. In reality, problems are rarely single-objective;
therefore, researchers must pay more attention to multi-objective models. Besides, methods and techniques for
handling multi-objective problems and achieving optimal and robust solutions need to be developed.
New variables in the model: Investigating past real experiences and analyses from distributing questionnaires
and interviewing experts can help update current decision variables and introduce new decision variables based
on new requirements.
Objectives: Cost and time-related objectives are the most common objectives used in the previous models. Other
objectives such as those related to risks or 'equity' (providing equitable service to demand when there are several
evacuation hospitals) can be studied in future research.
Changing the health conditions: All existing studies assumed stable health conditions for patients during the
planning horizon, while this is normally not realistic. In reality, the health condition of patients may change during
an evacuation, and sometimes it depends on time and the quality of operations. Therefore, presenting practical
probability functions is necessary for hospital transport evacuation problems.
Ready time of patients: The majority of the existing research assumed the patients are ready for pick up when
evacuation starts, while this assumption may not hold in real conditions. For example, past experiences show
evacuating patients from buildings and transferring them to other hospitals are performed simultaneously.
14
Therefore, it is reasonable that some patients arrive at pick up points sooner than other patients. Therefore,
exploring hospital evacuation problems under the different ready times of patients is very important.
Coordination and integration: Coordination between interrelated operations can help to use resources more
wisely and save the time in a disaster response. Therefore, developing integrated models that simultaneously
consider more than one category is an interesting future research direction. An example is making a decision to
find a place to establish a temporary medical facility, as the location of the facility may mutually affect many
parameters, including travel times of evacuation vehicles.
Personnel scheduling: The optimal use of human resources in emergency conditions is becoming a critical
challenge for hospitals. Therefore, hospital evacuation planning needs to be re-aligned to recognise this issue. To
advance available models, it is very highly important to include human resources scheduling during the
evacuation. While Table 6 shows, some existing models did not address this issue. An interesting direction for
future research is to develop hospital evacuation problem models, including hospital staff scheduling and
management.
Road network: Although existing studies assumed the road network infrastructure was reliable, the network may
be damaged by a disaster in the affected regions. Therefore, considering road network performance during
evacuations should be comprehensively addressed in future research.
Many research gaps still need to be studied, such as addressing stochastic and dynamic conditions in hospital
evacuation planning, incorporating human resource planning in the hospital evacuation plan, and developing an
efficient solution method to cope with the high uncertainties and complexities of hospital evacuation problems. The
gaps and potential opportunities identified may act as a starting point for academics and practitioners to further explore
these issues. The findings of this review have implications for decision-makers who are involved in disaster risk
management in the health sector, particularly in hospital evacuation management.
The review presented above indicates that addressing the above research gaps would have significant benefits for
researchers, policymakers and decision-makers who are involved in disaster risk management in the health sector,
particularly in hospital evacuation management. However, like all research methods, critical literature reviews have
their limitations and are potentially vulnerable to certain biases that should be acknowledged to ensure the results are
interpreted appropriately. The most common errors in the design of literature reviews are those related to the inclusion
and exclusion criteria used to find relevant studies. In this context, one limitation of this review was that only papers in
English were searched in the Scopus database, so any relevant papers in other languages were not considered.
Furthermore, only academic peer-reviewed research published in journals and peer-reviewed conferences were
included in this literature review, meaning that insights from the grey literature are excluded. Finally, the selection of
keywords used to filter the search process is clearly critical in determining the results of any literature review. To ensure
a comprehensive coverage of all potential studies, this study used a broad list of keywords, including those employed
in previous relevant literature reviews and comparable terms within the same conceptual definitions. However, some
research studies may have used uncommon synonyms that were inadvertently excluded from the keyword search
although we believe this risk is minimised by the methodology we adopted.
15
References
Aghaei Chadegani, Arezoo, Hadi Salehi, Melor Yunus, Hadi Farhadi, Masood Fooladi, Maryam Farhadi, and
Nader Ale Ebrahim. 2013. A comparison between two main academic literature collections: Web of
Science and Scopus databases. Asian Social Science 9 (5):18-26.
Aghapour, Amir Hossein, Maziar Yazdani, Fariborz Jolai, and Mohammad Mojtahedi. 2019. Capacity planning
and reconfiguration for disaster-resilient health infrastructure. Journal of Building Engineering 26:100853.
Aleksandrov, Mitko, Cheng Cheng, Abbas Rajabifard, and Mohsen Kalantari. 2019. Modelling and finding optimal
evacuation strategy for tall buildings. Safety Science 115:247-255.
Altay, Nezih, and Walter G. Green. 2006. OR/MS research in disaster operations management. European Journal of
Operational Research 175 (1):475-493.
Anderson-Berry, Linda, Tamsin Achilles, Shannon Panchuk, Brenda Mackie, Shelby Canterford, Amanda Leck,
and Deanne K. Bird. 2018. Sending a message: How significant events have influenced the warnings
landscape in Australia. International Journal of Disaster Risk Reduction 30:5-17.
Anshuka, Anshuka, Floris F. van Ogtrop, David Sanderson, Erin Thomas, and Andreas Neef. 2021. Vulnerabilities
shape risk perception and influence adaptive strategies to hydro-meteorological hazards: A case study of
Indo-Fijian farming communities. International Journal of Disaster Risk Reduction 62:102401.
Aucoin, Robert G. 2005. Hurricane Katrina one hospital's experience. Critical Care 10 (1):109.
Bagaria, Jayshree, Caroline Heggie, Jonathan Abrahams, and Virginia Murray. 2009. Evacuation and sheltering of
hospitals in emergencies: a review of international experience. Prehospital and disaster medicine 24 (5):461-
467.
Barnett, Joanne, Melissa Dennis-Rouse, and Valerie Martinez. 2009. Wildfire disaster leads to facilities evacuation.
Orthopaedic nursing 28 (1):17-20.
Bayram, Vedat. 2016. Optimization models for large scale network evacuation planning and management: A
literature review. Surveys in Operations Research and Management Science 21 (2):63-84.
Bernard, Marirose, and Pamela R Mathews. 2008. Evacuation of a maternal-newborn area during Hurricane
Katrina. MCN: The American Journal of Maternal/Child Nursing 33 (4):213-223.
Bhushan, Alka, and N. L. Sarda. 2013. Modeling of building evacuation using ladders. Fire Safety Journal 55:126-
138.
Bish, Douglas R, Esra Agca, and Roger Glick. 2014. Decision support for hospital evacuation and emergency
response. Annals of Operations Research 221 (1):89-106.
Bish, Douglas R., Hussein Tarhini, Roel Amara, Richard Zoraster, Nichole Bosson, and Marianne Gausche-Hill.
2017. Modeling to Optimize Hospital Evacuation Planning in EMS Systems. Prehospital Emergency Care 21
(4):503-510.
Blaser, Martin J, and Richard T Ellison III. 1985. Rapid nighttime evacuation of a veterans hospital. The Journal of
emergency medicine 3 (5):387-394.
Blumhagen, Dan W. 1987. Evacuation of patients during a fire at a general hospital. Annals of emergency medicine 16
(2):209-214.
Bongiovanni, Ivano, Erica Leo, Matteo Ritrovato, Antonio Santoro, and Pietro Derrico. 2017. Implementation of
best practices for emergency response and recovery at a large hospital: A fire emergency case study. Safety
Science 96:121-131.
16
Bovender, Jack O, and Bill Carey. 2006. A week we Don't want to forget: lessons learned from Tulane. Frontiers of
health services management 23 (1):3.
Braekers, Kris, Katrien Ramaekers, and Inneke Van Nieuwenhuyse. 2016. The vehicle routing problem: State of the
art classification and review. Computers & Industrial Engineering 99:300-313.
Campos, Vania, Renata Bandeira, and Adriano Bandeira. 2012. A method for evacuation route planning in disaster
situations. Procedia-Social and Behavioral Sciences 54:503-512.
Chand, Anumitra Mirti, and Martin Loosemore. 2016. Hospital learning from extreme weather events: using causal
loop diagrams. Building Research & Information 44 (8):875-888.
Chen, Wanying, Alain Guinet, and Angel Ruiz. 2015. Modeling and simulation of a hospital evacuation before a
forecasted flood. Operations Research for Health Care 4:36-43.
Childers, Ashley. 2010. Prioritizing patients for emergency evacuation from a healthcare facility.
Childers, Ashley, and Kevin M Taaffe. 2010. Healthcare facility evacuations: Lessons learned, research activity, and
the need for engineering contributions. Journal of Healthcare Engineering 1 (1):125-140.
Chong, Wai K., Hariharan Naganathan, Huan Liu, Samuel Ariaratnam, and Joonhoon Kim. 2018. Understanding
            
Engineering 4 (2):218-223.
Chun, Sookyung, and Kyungsook Nam. 2019. A public healthscape framework for assessing geriatric long-term
care public hospitals in South Korea by a Delphi expert consultation. Building and Environment 165:106291.
Cocanour, Christine S, Steven J Allen, Janine Mazabob, John W Sparks, Craig P Fischer, Juanita Romans, and Kevin
P Lally. 2002. Lessons learned from the evacuation of an urban teaching hospital. Archives of Surgery 137
(10):1141-1145.
Dosa, David M, Nancy Grossman, Terrie Wetle, and Vincent Mor. 2007. To evacuate or not to evacuate: lessons
learned from Louisiana nursing home administrators following Hurricanes Katrina and Rita. Journal of
the American Medical Directors Association 8 (3):142-149.
Eksioglu, Burak, Arif Volkan Vural, and Arnold Reisman. 2009. The vehicle routing problem: A taxonomic review.
Computers & Industrial Engineering 57 (4):1472-1483.
 variants and solution
approaches. Journal of Advanced Transportation 2019.
Fang, Yi-Ping, and Enrico Zio. 2019. An adaptive robust framework for the optimization of the resilience of
interdependent infrastructures under natural hazards. European Journal of Operational Research 276
(3):1119-1136.
Faturechi, Reza, and Elise Miller-Hooks. 2014. Travel time resilience of roadway networks under disaster.
Transportation Research Part B: Methodological 70:47-64.
. 2015. Measuring the performance of transportation infrastructure systems in disasters: A comprehensive
review. Journal of infrastructure systems 21 (1):04014025.
Field, Christopher B, Vicente Barros, Thomas F Stocker, and Qin Dahe. 2012. Managing the risks of extreme events and
disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change:
Cambridge University Press.
Fuzak, Julia K, Benjamin D Elkon, Louis C Hampers, Kathleen J Polage, Jerrod D Milton, Linda K Powers, Karen
Percell-de'Shong, and Joseph E Wathen. 2010. Mass transfer of pediatric tertiary care hospital inpatients
17
to a new location in under 12 hours: lessons learned and implications for disaster preparedness. The
Journal of pediatrics 157 (1):138-143. e2.
Geraldi, Joana, Harvey Maylor, and Terry Williams. 2011. Now, let's make it really complex (complicated).
International Journal of Operations & Production Management.
Gray, Bradford H, and Kathy Hebert. 2007. Hospitals in Hurricane Katrina: challenges facing custodial institutions
in a disaster. Journal of Health Care for the Poor and Underserved 18 (2):283-298.
Griffin, Anne Reid, Alicia R. Gable, Claudia Der-Martirosian, and Aram Dobalian. 2019. Hospitals Providing
Temporary Emergency Department Services in Alternative Care Settings After Hurricane Sandy. Critical
Care Nursing Clinics of North America.
Gupta, Sushil, Martin K. Starr, Reza Zanjirani Farahani, and Niki Matinrad. 2016. Disaster Management from a
POM Perspective: Mapping a New Domain. Production and Operations Management 25 (10):1611-1637.
Hachiya, Misao, Takako Tominaga, Hideo Tatsuzaki, and Makoto Akashi. 2014. Medical Management of the
Consequences of the F ukushima Nuclear Power Plant Incident. Drug development research 75 (1):3-9.
Haghani, Milad. 2020. Empirical methods in pedestrian, crowd and evacuation dynamics: Part I. Experimental
methods and emerging topics. Safety Science 129:104743.
. 2020. Empirical methods in pedestrian, crowd and evacuation dynamics: Part II. Field methods and
controversial topics. Safety Science 129:104760.
. 2020. Optimising crowd evacuations: Mathematical, architectural and behavioural approaches. Safety
Science 128:104745.
Hasegawa, Arifumi, Tetsuya Ohira, Masaharu Maeda, Seiji Yasumura, and Koichi Tanigawa. 2016. Emergency
responses and health consequences after the Fukushima accident; evacuation and relocation. Clinical
Oncology 28 (4):237-244.
Haverkort, J. J. Mark, Tessa H. Biesheuvel, Frank W. Bloemers, Mirjam B. de Jong, Falco Hietbrink, Lukas L. van
Spengler, and Luke P. H. Leenen. 2016. Hospital evacuation: Exercise versus reality. Injury 47 (9):2012-
2017.
Hosseini, Seyed Hossein, Hamid Reza Khankeh, Mehrdad Farrokhi, Mohammad Ali Hosseini, Pirhossein
Koolivand, and Mohammad Raeiszadeh. 2020. Early warning system-related challenges in health sector:
A qualitative content analysis study in Iran. Journal of Education and Health Promotion 9.
Houston, Nancy, A Vann Easton, EA Davis, J Mincin, BD Phillips, and M Leckner. 2009. Evacuating Populations
with Special Needs. Routes to Effective Evacuation Planning Primer Series. Washington, DC: The Federal
Highway Administration, US Department of Transportation.
Hyer, Kathryn, Lisa M Brown, Janelle J Christensen, and Kali S Thomas. 2009. Weathering the storm: challenges to
nurses providing care to nursing home residents during hurricanes. Applied nursing research 22 (4):e9-e14.
Johnson, CW. 2006. Using computer simulations to support a risk-based approach for hospital evacuation. A
Department of Computing Science Briefing.
Jourdan, L., M. Basseur, and E. G. Talbi. 2009. Hybridizing exact methods and metaheuristics: A taxonomy.
European Journal of Operational Research 199 (3):620-629.
Juan, Angel A., Javier Faulin, Scott E. Grasman, Markus Rabe, and Gonçalo Figueira. 2015. A review of
simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems.
Operations Research Perspectives 2:62-72.
18
Kabirifar, Kamyar, Mohammad Mojtahedi, Changxin Wang, and Vivian W. Y. Tam. 2020. Construction and
demolition waste management contributing factors coupled with reduce, reuse, and recycle strategies for
effective waste management: A review. Journal of Cleaner Production 263:121265.
Kim, Kyoung Yoon, Erhan Kutanoglu, John Hasenbein, Wen-Ying Wu, and Zong-Liang Yang. 2020. A Large-Scale
Patient Evacuation Modeling Framework using Scenario Generation and Stochastic Optimization. Paper
read at IIE Annual Conference. Proceedings.
Kimms, A., and M. Maiwald. 2018. Bi-objective safe and resilient urban evacuation planning. European Journal of
Operational Research 269 (3):1122-1136.
Li, Yapeng, Wei Cai, and Austin A. Kana. 2019. Design of level of service on facilities for crowd evacuation using
genetic algorithm optimization. Safety Science 120:237-247.
Lim, Ma Bernadeth B., Hector R. Lim, and Mongkut Piantanakulchai. 2019. Flood evacuation decision modeling
for high risk urban area in the Philippines. Asia Pacific Management Review.
Little, Mark, Theona Stone, Richard Stone, Jan Burns, Jim Reeves, Paul Cullen, Ian Humble, Emmeline Finn, Peter
Aitken, Mark Elcock, and Noel Gillard. 2012. The Evacuation of Cairns Hospitals Due to Severe Tropical
Cyclone Yasi. Academic Emergency Medicine 19 (9):E1088-E1098.
Loosemore, Martin, Vivien Chow, and Tracie Harvison. 2013. Inter-agency governance risk in managing hospital
responses to extreme weather events in New South Wales, Australia: a facilities management perspective
of shared situational awareness. Construction Management and Economics 31 (10):1072-1082.
Lovreglio, R., E. Kuligowski, S. Gwynne, and K. Boyce. 2019. A pre-evacuation database for use in egress
simulations. Fire Safety Journal 105:107-128.
Marzouk, Mohamed, and Basma Mohamed. 2019. Integrated agent-based simulation and multi-criteria decision
making approach for buildings evacuation evaluation. Safety Science 112:57-65.
Mattsson, Lars-Göran, and Erik Jenelius. 2015. Vulnerability and resilience of transport systems A discussion of
recent research. Transportation Research Part A: Policy and Practice 81:16-34.
Maxwell, Christopher. 1982. Hospital organizational response to the nuclear accident at Three Mile Island:
implications for future-oriented disaster planning. American Journal of Public Health 72 (3):275-279.
McGlown, K Joanne. 2001. Evacuation of health care facilities: A new twist to a classic model. Natural Hazards
Review 2 (2):90-99.
McGovern, Joanne, and Sandy Bogucki. 2015. Mass casualty evacuation and patient movement. Emergency Medical
Services: Clinical Practice and Systems Oversight:303-312.
Miller-Hooks, Elise, Xiaodong Zhang, and Reza Faturechi. 2012. Measuring and maximizing resilience of freight
transportation networks. Computers & Operations Research 39 (7):1633-1643.
Milsten, Andrew. 2000. Hospital Responses to Acute-Onset Disasters: A Review. Prehospital and Disaster Medicine
15 (1):40-53.
MirHassani, SA, and F Hooshmand. 2019. Methods and Models in Mathematical Programming: Springer.
Moskop, John C., and Kenneth V. Iserson. 2007. Triage in Medicine, Part II: Underlying Values and Principles.
Annals of Emergency Medicine 49 (3):282-287.
Nurses, Association of Perioperative Registered. 2007. AORN guidance statement: mass casualty, triage, and
evacuation. AORN journal 85 (4):792, 794.
19
Pillac, Victor, Michel Gendreau, Christelle Guéret, and Andrés L. Medaglia. 2013. A review of dynamic vehicle
routing problems. European Journal of Operational Research 225 (1):1-11.
Poppenborg, Jens, and Sigrid Knust. 2016. Modeling and optimizing the evacuation of hospitals based on the
MRCPSP with resource transfers. EURO Journal on Computational Optimization 4 (3):349-380.
Poulos, Alan, Felipe Tocornal, Juan Carlos de la Llera, and Judith Mitrani-Reiser. 2018. Validation of an agent-
based building evacuation model with a school drill. Transportation Research Part C: Emerging Technologies
97:82-95.
Psaraftis, Harilaos N. 1980. A dynamic programming solution to the single vehicle many-to-many immediate
request dial-a-ride problem. Transportation Science 14 (2):130-154.
Quagliarini, Enrico, Gabriele Bernardini, Silvia Santarelli, and Michele Lucesoli. 2018. Evacuation paths in historic
city centres: A holistic methodology for assessing their seismic risk. International Journal of Disaster Risk
Reduction 31:698-710.
Quarantelli, Enrico Louis. 1980. Evacuation Behavior and Problems: Findings and Implications from the Research
Literature: Ohio State Univ Columbus Disaster Research Center.
          bilistic
programming approach to multiperiod hospital evacuation planning problem under uncertainty.
International Transactions in Operational Research 25 (1):157-189.
Rambha, Tarun, Linda K. Nozick, Rachel Davidson, Wenqi Yi, and Kun Yang. 2021. A stochastic optimization
model for staged hospital evacuation during hurricanes. Transportation Research Part E: Logistics and
Transportation Review 151:102321.
Rattanakanlaya, Kanittha, Achara Sukonthasarn, Suparat Wangsrikhun, and Chawapornpan Chanprasit. 2018.
Flood disaster preparedness experiences of hospital personnel in Thailand: A qualitative study.
Australasian Emergency Care 21 (3):87-92.
Rendón Rozo, Kelly, Julian Arellana, Alcides Santander-Mercado, and Maria Jubiz-Diaz. 2019. Modelling building
emergency evacuation plans considering the dynamic behaviour of pedestrians using agent-based
simulation. Safety Science 113:276-284.
Ricci, Karen A, Anne R Griffin, Kevin C Heslin, D Kranke, and A Dobalian. 2015. Evacuate or shelter-in-place? The
role of corporate memory and political environment in hospital-evacuation decision making. Prehosp
Disaster Med 30:1-6.
Rojek, Amanda, and Mark Little. 2013. Review article: Evacuating hospitals in Australia: What lessons can we learn
from the world literature? Emergency Medicine Australasia 25 (6):496-502.
              
buildings during emergencies. Physica A: Statistical Mechanics and its Applications 528:121432.
Saldaña, Johnny. 2015. The coding manual for qualitative researchers: Sage.
Sandra, Keret, Nahari Meital, Merin Ofer, Aharonson-Daniel Limor, Goldberg Sara, and Adini Bruria. 2017.
Facilitating emergency hospital evacuation through uniform discharge criteria. The American Journal of
Emergency Medicine 35 (5):681-684.
Schultz, Carl H, Kristi L Koenig, and Roger J Lewis. 2003. Implications of hospital evacuation after the Northridge,
California, earthquake. New England Journal of Medicine 348 (14):1349-1355.
20
Shabanikiya, Hamidreza, Mehdi Jafari, Hasan Abolghasem Gorgi, Hesam Seyedin, and Azin Rahimi. 2019.
Developing a practical toolkit for evaluating hospital preparedness for surge capacity in disasters.
International Journal of Disaster Risk Reduction 34:423-428.
Shahparvari, Shahrooz. 2016. Enhancing emergency response in short-notice bushfire evacuation.
Shahparvari, Shahrooz, Prem Chhetri, Ahmad Abareshi, and Babak Abbasi. 2015. Multi-Objective Decision
Analytics for Short-Notice Bushfire Evacuation: An Australian Case Study. Australasian Journal of
Information Systems 19.
Shahparvari, Shahrooz, Prem Chhetri, Babak Abbasi, and Ahmad Abareshi. 2016. Enhancing emergency
evacuation response of late evacuees: Revisiting the case of Australian Black Saturday bushfire.
Transportation research part E: logistics and transportation review 93:148-176.
Shiwakoti, Nirajan. 2016. Understanding differences in emergency escape and experimental pedestrian crowd
egress through quantitative comparison. International Journal of Disaster Risk Reduction 20:129-137.
Sorensen, John H. 1991. When shall we leave? Factors affecting the timing of evacuation departures. International
journal of mass emergencies and disasters 9 (2):153-165.
Sorensen, John H, and Dennis S Mileti. 1987. Decision making uncertainties in emergency warning system
organizations. International Journal of Mass Emergencies and Disasters 5 (1):33-61.
Taaffe, Kevin, Matt Johnson, and Desiree Steinmann. 2006. Improving hospital evacuation planning using
simulation. Paper read at Proceedings of the 2006 Winter Simulation Conference.
Taaffe, Kevin M, and Esengul Tayfur. 2006. Evaluating the robustness of hospital evacuation plans. Paper read at
IIE Annual Conference. Proceedings.
Tanigawa, Koichi, Yoshio Hosoi, Nobuyuki Hirohashi, Yasumasa Iwasaki, and Kenji Kamiya. 2012. Loss of life
after evacuation: lessons learned from the Fukushima accident. The Lancet 379 (9819):889-891.
Tayfur, Esengul, and Kevin Taaffe. 2007. Allocation of resources during hospital evacuations: a simulation
approach. Paper read at IIE Annual Conference. Proceedings.
. 2007. Allocation of resources for hospital evacuations via simulation. Paper read at 2007 Winter Simulation
Conference.
. 2009. A model for allocating resources during hospital evacuations. Computers & Industrial Engineering 57
(4):1313-1323.
. 2009. Simulating hospital evacuationthe influence of traffic and evacuation time windows. Journal of
Simulation 3 (4):220-234.
Tekin, Erdal, Atif Bayramoglu, Mustafa Uzkeser, and Zeynep Cakir. 2017. Evacuation of hospitals during disaster,
establishment of a field hospital, and communication. The Eurasian journal of medicine 49 (2):137.
Thomas, Joan, and Nancy Lackey. 2008. How to evacuate a psychiatric hospital: a Hurricane Katrina success story.
Journal of psychosocial nursing and mental health services 46 (1):35-40.
 
informed management knowledge by means of systematic review. British journal of management 14 (3):207-
222.
Tubbs, Jeffrey, and Brian Meacham. 2007. Egress design solutions: A guide to evacuation and crowd management
planning: John Wiley & Sons.
21
UNISDR, UNOFDRR. 2009. Terminology on disaster risk reduction. Geneva, Switzerland.
VanDevanter, Nancy, Victoria H. Raveis, Christine T. Kovner, Meriel McCollum, and Ronald Keller. 2017.
Challenges and Resources for Nurses Participating in a Hurricane Sandy Hospital Evacuation. Journal of
Nursing Scholarship 49 (6):635-643.
Veeraswamy, Anand, Edwin R. Galea, Lazaros Filippidis, Peter J. Lawrence, Simo Haasanen, Robert J. Gazzard,
and Thomas E. L. Smith. 2018. The simulation of urban-scale evacuation scenarios with application to the
Swinley forest fire. Safety Science 102:178-193.
Vogt, Barbara M. 1990. Evacuation of institutionalized and specialized populations: Oak Ridge National Lab., TN
(USA).
Yaghoubi, T., A. Ardalan, D. Khorasani Zavareh, H. Khankeh, A. Nejati, and A. Ebadi. 2017. Decision-making on
Hospital Emergency Evacuation in Disasters and Emergencies: Findings From a Systematic Review. Iran
Red Crescent Med J 19 (11):e14214.
Yazdani, Maziar, and Fariborz Jolai. 2016. Lion optimization algorithm (LOA): a nature-inspired metaheuristic
algorithm. Journal of computational design and engineering 3 (1):24-36.
Yazdani, Maziar, Mohammad Mojtahedi, and Martin Loosemore. 2020. Enhancing evacuation response to extreme
weather disasters using public transportation systems: a novel simheuristic approach. Journal of
Computational Design and Engineering 7 (2):195-210.
Yu, Lean, and Kin Keung Lai. 2011. A distance-based group decision-making methodology for multi-person multi-
criteria emergency decision support. Decision Support Systems 51 (2):307-315.
Zane, Richard, Paul Biddinger, Andrea Hassol, Tom Rich, Jennifer Gerber, and Jennifer DeAngelis. 2010. Hospital
evacuation decision guide. Agency for Healthcare Research and Quality, US Department of Health and Human
Services:1-55.
Zhang, Fanshun, Cejun Cao, Congdong Li, Yang Liu, and Donald Huisingh. 2019. A systematic review of recent
developments in disaster waste management. Journal of Cleaner Production 235:822-840.
Zhang, Xin. 2017. Study on rapid evacuation in high-rise buildings. Engineering Science and Technology, an
International Journal 20 (3):1203-1210.
Zou, Baobao, Chunxia Lu, and Yi Li. 2019. Simulation of a hospital evacuation including wheelchairs based on
modified cellular automata. Simulation Modelling Practice and Theory:102018.
... Climate change causing floods can require ongoing attention. Over time, weather and human-related factors not only play a role in causing floods to worsen, but limited data on past flood events can also make it difficult for authorities to measure comparatively climate-change-driven flood trends to reduce their risk [1][2][3]. It is increasingly clear that the effects of climate change are a contributing factor to floods, which cause economic damage and major casualties [4]. ...
... The increasingly alarming phenomenon of floods in Malaysia needs to be balanced with increased awareness, response, and preparedness for disasters from all elements of society, especially in at-risk communities [6]. A natural hazard is a physical event or condition that has the potential to cause death, injury, property damage, agricultural loss, loss of livestock, environmental damage [7], business disruption loss [8][9][10], and interrupt the normal functioning of society [2]. ...
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... Healthcare buildings flat roofs have unique characteristics compared to other buildings [19,20] related to the need of fully continuous operation [21]. The roof of healthcare buildings require a high degree of reliability and a higher state of condition that other buildings, since it is not possible to evacuate the rooms under the roof in case of anomalies appearance [22]. Besides, the water infiltration rate must be null, since watertightness losses enhance efflorescence risk and fungal growth, increasing the nosocomial diseases appearance likelihood [23][24][25]. ...
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... Also, while the issue of hospital evacuation has been particularly of the focus of attention of crowd researchers within both numerical and experimental realms in the years prior to pandemic, it may be worth noting that with hospitals around the world working at capacities treating Covid-19 patients, there may be further complications with respect to hospital evacuation that may require specific attention (Haghpanah et al., 2021;Yazdani et al., 2021). Development of specific strategies for the evacuation of hospital in which a mixture of Covid-19 patients and other patients are treated may require simulation testing and optimisation methods. ...
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With the issues of crowd control and physical distancing becoming central to disease prevention measures, one would expect that crowd research should become a focus of attention during the Covid-19 pandemic era. However, I will show, based on a variety of metrics, that not only has this not been the case, but also, the first two years of the pandemic have posed an undisputable setback to the development and growth of crowd science. Without intervention, this could potentially aggravate further and cause a long-lasting recession in this field. This article, in addition to documenting and highlighting this issue, aims to outline potential avenues through which crowd research can reshape itself in the era of Covid-19 pandemic, maintain its pre-pandemic momentum and even further expand the diversity of its topics. Despite significant changes that the pandemic has brought to human life, issues related to congregation and mobility of pedestrians, building fires, crowd incidents, rallying crowds and the like have not disappeared from societies and remain relevant. Moreover, the diversity of pandemic-related problems itself creates a rich ground for making novel scientific discoveries. This could provide grounds for establishing fresh dimensions in crowd dynamics research. These potential new dimensions extend to all areas of this field including numerical and experimental investigations, crowd psychology and applications of computer vision and artificial intelligence methods in crowd management. The Covid-19 pandemic may have posed challenges to crowd researchers but has also created ample potential opportunities. This is further evidenced by reviewing efforts taken thus far in pandemic-related crowd research.
... Optimization methods for pedestrian evacuations (i.e., architectural design, mathematical programming, training) are studied by Haghani (2020b) [9], aiming on interventional approaches that seek to improve evacuation efficiency. With reference to specific building types that are characterized by critical crowd dynamics, to improve effective strategies for safe evacuation processes, the case of hospitals and school buildings are studied in Yazdani et al., (2021) [10] and Rostami et al., (2021) [11], respectively. ...
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In the last few years, modern technologies such as numerical simulations, virtual and augmented reality, and agent-based models represented effective tools to study phenomena, which may not be experimentally reproduced due to costs, inherent hazards, or other constraints (e.g., fire or earthquake emergencies and evacuation from buildings). This paper shows how to integrate a virtual reality platform with numerical simulation tools to reproduce an evolutionary fire emergency scenario. It is computed in real time based on the building information model and a fluid dynamic software. A specific software was also used to simulate in real time the crowd dynamic in the virtual environment during the emergency evacuation process. To demonstrate the applicability of the proposed methodology, the emergency fire evacuation process for an existing school building is presented. The results show that the proposed virtual reality-based system can be employed for reproducing fire emergency scenarios. It can be used to help decision-makers to determine emergency plans and to help firefighters as a training tool to simulate emergency evacuation actions.
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Emergency medical services (EMS) around the world face the challenging task of allocating resources to efficiently respond to medical emergencies within a geographical area. While several studies have been done to improve various aspects of EMS, such as ambulance dispatch planning and station placement optimization, few works have focused on the assessment of existing rich real-world emergency response data to systematically identify areas of improvement. In this paper, we propose DAPI (data-driven analysis of potential response inefficiencies), a general tool for analyzing inefficiencies in emergency response datasets. DAPI efficiently identifies potential response bottlenecks based on spatial distributions of ambulance responses and statistically assesses them with respect to inferred activity levels of relevant dispatch stations to aid causality analysis. DAPI is applied on a dataset containing all medical emergency responses in mainland Portugal, in which we find statistical evidence that inefficiencies are correlated with high levels of activity of stations closer to an emergency location. We present these findings, along with the associated patterns and geographical clusters, serving as a valuable decision support tool to aid EMS in improving their operations.
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Facility emergence evacuation is often a complicated process under extreme conditions. Most of the buildings today use pre-installed signages to guide the emergence evacuation. However, these guidances are sometimes insufficient or misleading, particularly for evacuating from high-rise buildings or complex buildings, such as schools, hospitals, and stadiums. Following a planned route may lead the crowd to move towards dangers, such as smoke and fire. The future emergency guidance system should be more intelligent and be able to guide people to evacuate with a higher survival possibility. This study proposes a real-time building evacuation model with an improved cellular automata (CA) method. This algorithm combines cellular automata with the potential energy field (PEF) model in fluid dynamic theory (FDT) to choose safe paths for the crowd and reduce the possibility of stampedes. Custom-designed wireless sensors, artificial intelligence (A.I.) enhanced surveillance cameras, intelligent emergency signage systems, and edge computing servers are used to sample fire and crowd data, operate the intelligent evacuation algorithm, and guide the crowd with the signage system in real-time conditions. In addition, we performed the algorithm simulation on a two-dimensional plane generated based on the building structure of the Beijing Capital Airport Hospital. The evacuation drill simulations show that the average escape time is significantly shortened with optimal real-time guidance. In one case, a 72% reduction in evacuation time is achieved compared with evacuation using pre-installed signages. The results also demonstrated that the proposed model and system’s evacuation time reduction performance is particularly good in crowded buildings, such as schools or stadiums.
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Unique vulnerabilities are intrinsic to Pacific island countries which shape risk perception and influence adaptive decision making to natural hazards. This study aims to examine ongoing risks caused by hydro-meteorological hazards, with a focus on micro-level household response to increasing vulnerabilities, in addition to macro-level community related vulnerabilities. Data collection was undertaken through semi-structured interviews in three hydro-meteorological hazard-prone communities, dominated by members of the Indian diaspora, in the Western part of Fiji. The findings were analysed using descriptive, interpretive and inferential analysis. The findings reveal that climatic, physical, cultural and socio-economic factors render households more vulnerable at a micro-level. The research also revealed that members of the Indian community normally have lower levels of societal cohesion, have an inherent individualistic approach to disasters and lack access to communal assets such as land rendering them more vulnerable at a community level. As a coping mechanism, households were found to have a higher likelihood of adhering to social adaptive strategies such as making behavioural, informational, and educational changes for risk reduction. According to this study, participants show a high degree of risk perception with a sound understanding of storm surge, flood peaks and extents as well as prolonged dry spells. The study recommends avenues for combining scientific knowledge together with citizen science for better hazard risk analysis as future research. To ensure appropriate risk mitigation, governments should implement effective warning systems and undertake capacity building prior to disasters to initiate adequate response to forecast warnings.
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