Aeromedical Evacuation Planning Using Geospatial Decision ...

MILITARY MEDICINE, 179, 2:174, 2014

Aeromedical Evacuation Planning Using GeospatialDecision-Support

CPT Nathaniel D. Bastian, MS USA*†‡; Lawrence V. Fulton, PhD§

ABSTRACT In this study, we proffer an algorithmic, geospatial-based decision-support methodology that assistsmilitary decision-makers in determining which aeromedical evacuation (MEDEVAC) assets to launch after receivingan injury location, given knowledge only of terrain, aircraft location, and aircraft capabilities. The objective is formilitary medical planners to use this decision-support tool (1) to improve real-time situational awareness by visuali-zation of MEDEVAC coverage, showing which areas can be reached within established timelines; (2) to supportmedical planning by visualizing the impact of changes in the medical footprint to the MEDEVAC coverage; and (3) tosupport decision-making by providing a time-sorted list of MEDEVAC asset packages to select from, given thelocation of the patients. This same geospatial-based decision tool can be used for proper emplacement of evacuationassets such that the theater is covered within a truly representative 1-hour response time. We conclude with a discussionof applicability of this tool in medical force structure planning.

INTRODUCTIONThe Army Health System (AHS), the U.S. Army Medical

Department (AMEDD) component of the Department of

Defense’s Military Health System (MHS), is focused on

delivery of health care across the continuum of military

operations. During combat, the AMEDD’s Health Service

Support (HSS) system provides medical treatment from

the patient’s point-of-injury (POI) through successive roles

of health care. As a critical component of the HSS system,

medical evacuation (intra- and intertheater) is the link between

roles of care because it sustains the patient during transport

by providing en route medical care and emergency medical

intervention. Although medical evacuation occurs at the

tactical, operational, and strategic levels, this study considers

the tactical level (intratheater) where medical evacuation

assets locate, acquire, treat, and evacuate patients from the

POI to an appropriate Medical Treatment Facility (MTF)—

such as a Combat Support Hospital (Role III)—where they

are stabilized, prioritized, and, if required, prepared for fur-

ther medical evacuation outside the battlefield.1

The AMEDD’s medical evacuation system comprises

dedicated, standardized air and ground evacuation platforms

designed, staffed, and equipped to provide rapid emergency

response to requests for support to efficiently move patients

to an MTF. All medical evacuation platforms provide en

route medical care to enhance patient survivability rates,

minimize mortality, improve patient prognosis, and reduce

the disability of the wounded, injured, or sick. An effective

medical evacuation system serves many purposes. It is a

force multiplier as it clears the battlefield, enabling the

ground commander to continue the mission. It builds the

morale of Soldiers by showing that care is quickly avail-

able on request. It provides medical economies of force

by potentially reducing the need for in-theater hospitals.

Finally, an effective evacuation system provides connec-

tivity of the AHS as appropriate to the MHS.1

Overview and Purpose

This article seeks to expand the current literature on aero-

medical evacuation (MEDEVAC) coverage and medical

planning by using geospatial analytics for decision-support.

The primary objectives are (1) to improve real-time situa-

tional awareness by visualization of MEDEVAC coverage,

showing which areas can be reached within established

timelines; (2) to support medical planning by visualizing

the impact of changes in the medical footprint to the

MEDEVAC coverage; and (3) to support decision-making

by providing a time-sorted list of asset packages to select

from, given the location of the patients.

This article is structured as follows. We first review the

literature to better grasp the evolution of medical evacua-

tion modeling and simulation. Afterward, we introduce

the application of geospatial analytics for MEDEVAC cover-

age visualization and medical planning decision-support.

Finally, we conclude with a discussion of applicability to

force structure planning. The primary significance of this

*Department of Industrial and Manufacturing Engineering, Center for

Integrated Healthcare Delivery Systems, Pennsylvania State University,

355 Leonhard Building, University Park, PA 16802.

†Center for AMEDD Strategic Studies, Medical Capabilities Integration

Center, U.S. Army Medical Department Center and School, 1608 Stanley

Road, Fort Sam Houston, TX 78234-5047.

‡U.S. Army–Baylor University, Graduate Program in Health and Busi-

ness Administration, U.S. Army Medical Department Center and School,

3630 Stanley Road, Fort Sam Houston, TX 78234-6100.

§Department of Computer Information Systems and Quantitative Methods,

McCoy College of Business Administration, Texas State University, McCoy

Hall 404, San Marcos, TX 78666-4616.

Any opinions, findings, and conclusions or recommendations expressed

in this material are those of the authors and do not necessarily reflect the

views of the National Science Foundation, the Pennsylvania State Univer-

sity, the Texas State University, or the U.S. Army.

doi: 10.7205/MILMED-D-13-00432

MILITARY MEDICINE, Vol. 179, February 2014174

Dow

nloaded from https://academ

ic.oup.com/m

ilmed/article/179/2/174/4160802 by guest on 06 February 2022

study is that we proffer an algorithmic, geospatial-based

decision-support tool that assists decision-makers in deter-

mining which MEDEVAC assets to launch after receiving a

POI location, given knowledge only of terrain, aircraft loca-

tion, and aircraft capabilities. Further, the same decision tool

can be used for proper emplacement of evacuation assets

such that the theater is covered within a truly representative

1-hour response time.

LITERATURE REVIEWSince the United States’ initial involvement in both Opera-

tion Enduring Freedom and Operation Iraqi Freedom, mili-

tary medical planners have sought to improve the Army’s

medical evacuation system and force structure using opera-

tions research and systems analysis techniques. Before 2005,

MEDEVAC companies were independently numbered units

generally assigned to evacuation battalions. As part of a

major restructuring between AMEDD and Army Aviation,

the Army realigned the MEDEVAC company to lettered sub-

ordinate units under the General Support Aviation Battalion

(GSAB). The Army also reduced the number of organic

MEDEVAC aircraft required from 15 to 12 platforms per

company. After this transition process, the allocation of

MEDEVAC companies became a function of the required

number of GSABs, which greatly reduced the total number

of MEDEVAC airframes required for an effective HSS

system. Fulton et al2 used Monte Carlo simulation to quan-

titatively evaluate these rules of allocation and planning

factors that had an effect on MEDEVAC units. The results

of their study were used to provide decision-support for the

MEDEVAC force structure based on a Total Army Analysis

(TAA) scenario. The TAA is the Army’s method for deter-

mining and resourcing force structure requirements.

Bastian3 developed a robust, multicriteria decision analy-

sis methodology using a scenario-based, stochastic goal pro-

gramming optimization model for medical planners to use as

a strategic and tactical MEDEVAC asset-planning tool. As

part of this model, Bastian developed a three-dimensional

shortest helicopter-path algorithm to compute nearly opti-

mal helicopter flight routes with respective flight times

between an evacuation site, POI, and MTF. The goal pro-

gramming model is optimized over a set of expected sce-

narios (generated from different experimental design factors)

with stochastically determined casualty locations to emplace

the minimum number of MEDEVAC helicopters necessary

to maximize the coverage of theater-wide casualty demand

and the probability of meeting that demand (computed using

Monte Carlo simulation), while minimizing the maximal

evacuation site total vulnerability to enemy attack. This analy-

sis assumed that MEDEVAC asset evacuation sites were

colocated with MTFs, which is the optimal relationship for

minimizing travel time requirements from evacuation loca-

tion to POI to treatment facility.4

Similar to the work of Bastian,3 Fulton et al5 described

a two-stage stochastic optimization model for the relocation

of deployable military hospitals, the reallocation of hospital

beds, and the emplacement of tactical medical evacuation

assets (both air and ground ambulances) during steady-state

military combat operations. In this model, the objective func-

tion was to minimize the time traveled, weighted by patient

severity, from the evacuation site to the POI and onward to

the MTF. The optimal solution also determined the number

of air and ground ambulances and the hospital beds of each

type required at each selected site. The number, location,

and severity of casualties were stochastically generated via a

method similar to Bastian.3 In addition to this study, Fulton

et al6 also evaluated the optimal arrangement of deployable

medical capability components (intensive care units, inter-

mediate care wards, and operating room suites), given sce-

narios designed to represent stability operations through

major combat operations. These capabilities are then com-

pared to achievable medical capabilities based on certain

engineering design characteristics (e.g., bed capacity) of a

modular Combat Support Hospital proposed by AMEDD. The

study also described five stochastic optimization models pro-

vided to AMEDD decision-makers for initial hospital design.

The AMEDD continues to evaluate the medical evacua-

tion system by conducting capability-based analyses to assess

the requirements and capabilities of MEDEVAC units. As

part of a study for the future of joint force design for ver-

tical lift (Future of Vertical Lift, FVL), Bastian et al4 pro-

vided the initial assessment of the minimal MEDEVAC

aircraft capability (given doctrinal constraints) required to

ensure appropriate MEDEVAC response coverage in both

current and future combat operations. Specifically, they

investigated the required capabilities for future MEDEVAC

aircraft and developed a decision-support tool for mili-

tary medical planners in evaluating risk associated with

MEDEVAC platform capability and coverage (in terms of

aircraft ground speed and total distance traveled) within

the brigade operating space. They also determined that

colocation of MEDEVAC assets and surgical elements pro-

vides the optimum coverage for a single brigade when

casualty clusters are indeterminable or random.

In a similar study, Bastian et al7 conducted a DOTMLPF

(doctrine, organization, training, maintenance, leadership

and education, personnel, and facilities) assessment of the

MEDEVAC unit to determine gaps in the current force

structure and solutions for future force design. As part of

their mixed-methods research design, they performed quanti-

tative analysis of a Likert-scale survey (using primary data)

to identify statistically significant capability gaps associated

with the current force structure, and they conducted text

mining to analyze qualitative data (i.e., recommendations

from aviation and medical aviation leaders) to identify

themes that would provide potential solutions to those

gaps. In another part of the FVL study, Bastian et al8 use

a mixed-methods approach to provide three new, separate

analyses as part of the development of the next MEDEVAC

platform. In their analyses, they use discrete-event simulation

MILITARY MEDICINE, Vol. 179, February 2014 175

MEDEVAC Planning Using Geospatial Decision-Support

Dow


ic.oup.com/m


based partially on qualitative assessments from the field to

determine the optimal capabilities of an FVL MEDEVAC

platform, given an Afghanistan-like scenario and parameters

associated with the treatment/ground evacuation capabilities

in that theater. Furthermore, they conduct deterministic ana-

lyses to (1) explore the MEDEVAC trade-off considerations

associated with different aircraft engines operating under

variable conditions and (2) determine how the additional

weight of weaponizing the current helicopter fleet affects

MEDEVAC range, coverage radios, and response time.

METHODS AND RESULTSDuring a combat deployment, military medical planners

and senior decision-makers will need to understand and

coordinate the lay down of all MTFs, MEDEVAC assets,

and other medical resources. The classical approach to plan-

ning MEDEVAC coverage is to draw unobstructed range

rings and ellipses around the MTFs, with the radius of these

rings based on the range that MEDEVAC assets can cover

in a specified amount of flight time. The “unobstructed”

rings do not consider altitude restrictions or other obstruc-

tions such as a Restricted Operating Zone (ROZ) that would

prohibit a helicopter from flying directly from A to B. The

helicopter could of course try to fly around the obstacle, but

that deforms the range ring, as discussed further in this

article. Figure 1 shows an example of MEDEVAC cover-

age in Afghanistan based on MEDEVAC response times

adhering to the “60-minute” standard.4

The unobstructed range rings depicted in Figure 1 do

not consider altitude restrictions nor differences in the

capabilities of the available MEDEVAC airframes. They

do, however, quickly depict what could be covered by the

lay down of MEDEVAC assets and MTFs, given perfectly

flat terrain.

Aeromedical Evacuation Response Planning Times

The radius of unobstructed range rings is based on the

MEDEVAC mission requirements, which was standardized

FIGURE 1. Example of unobstructed range rings coverage in Afghanistan.



Dow


ic.oup.com/m


to the “golden hour” by the U.S. Secretary of Defense.4

This 60-minute MEDEVAC response time can be broken

down into four planning times:

1. Notification to wheels-up at the helicopter landing zone

(HLZ) (<15 minutes)

2. Wheels-up at the HLZ to wheels-down at the POI

3. Casualty pickup and loading at the POI (<5 minutes)

4. Wheels-up at the POI to wheels-down at the MTF

Note that this “golden hour” standard MEDEVAC response

time does not include (1) the time between patient injury and

notification (submission and receipt of the 9-line MEDEVAC

request) and (2) the patient off-loading/transfer at the MTF.3

Despite the “golden hour” standard of MEDEVAC response

time, we emphasize rapid deployment of MEDEVAC assets

to the correct casualty location, providing immediate en route

medical treatment, and transporting the patient to the correct

MTF. We must assess those MEDEVAC missions lasting

longer than 60 minutes to identify systematic areas of con-

tinual improvement. One common method for improv-

ing MEDEVAC response time is for higher headquarters

to issue early warning to the MEDEVAC assets/crews based

on unfolding events/incidents rather than waiting for sub-

mission and receipt of the actual 9-line MEDEVAC request.

Higher headquarters normally relies heavily on instant

messaging/chat, which allows for informing multiple receivers

of unfolding events/incidents by so-called chat rooms in

which relevant notification, updates, and coordination are

achieved with very dense text messages.

Derivation of Aeromedical Evacuation Flight Timeand Range

Given certain characteristics and capabilities of each type

of MEDEVAC asset, we can derive the available flight time

(in minutes) and the range (in nautical miles [NM]); assum-

ing that notification time and patient transfer time are equal for

all platforms and not included in the 60-minute MEDEVAC

response time standard. Appendix A depicts an example

algorithm for computing MEDEVAC flight time and range

for the UH-60 Black Hawk helicopter.

The example algorithm in Appendix A is programmed

using the open-source Python 2.7.3 programming language.9

For this example, we assume the UH-60 Black Hawk flies

150 knots (nautical-miles per hour) with a maximum eleva-

tion of 1,500 m above ground level. In addition, we use a

notification to move (ntm) time of 10 minutes and a patient

load time of 5 minutes, both of which are validated times

based on recent combat deployment MEDEVAC experience.

When executed, the code in Appendix A will produce the

output: “UH-60 Black Hawk, 45 min, 112 nm @ 150 kts

and max elevation of 1500 m.”

From this output, we see that the maximum available

range (measured using two-dimensional Euclidean distance)

of a UH-60 Black Hawk is 112 NM, which we can use to

draw a MEDEVAC coverage area around an MTF with a

colocated HLZ; note that colocation of MEDEVAC assets

and surgical elements was proven to be mathematically opti-

mal in the study by Bastian et al.4 This MEDEVAC coverage

circle has a midpoint (Pm) and radius (R) that is half of

the calculated range because the helicopter starts at the

colocated HLZ/MTF (at Pm), flies to any POI inside

the circle to pick up the patient(s), and then drops off the

patient(s) back at Pm (this assumes that origin and destina-

tion points are both Pm). Figure 2 shows this for the exam-

ple output above, where the MEDEVAC coverage circle

has R = 56 NM.

Ellipses as Unobstructed Range Rings

The example shown in Figure 2 depicts the MEDEVAC

coverage circle where the HLZ and MTF are colocated. In

the case where MEDEVAC assets are forward deployed to a

remote, geographically separated location without colocated

surgical elements (i.e., Combat Support Hospital—Role III

MTF), the MEDEVAC assets begin at the HLZ, fly to the

POI to pick up the patient(s), and then transport the

patient(s) to the MTF not colocated with the HLZ. This

case is best shown in Figure 3; the MEDEVAC crew and

assets are located at the HLZ, whereas the MTF is at

another location.

Similar to the depiction in Figure 2, Figure 3 shows

the MEDEVAC coverage region (an ellipse rather than a

circle) where casualties can be reached and evacuated with

MEDEVAC assets and then transported to the MTF within

the available flight time. In this example, the derivation of

the ellipse depends on the Euclidean distance between the

HLZ and MTF (e.g., 60 NM) and the maximum allowable

range (e.g., 112 NM at 150 knots). The algorithm to derive

this ellipse (Fig. 3) is shown in Appendix B, and this gen-

erates the following output:

d(HLZ,MTF) = 60 nm #distance between HLZ and MTF

range = 112 nm #of given aircraft type

a=30 nm #a=0:5*d(HLZ,MTF)

c=R=56:00 nm #where r = 0:5*range=0:5*112:00=56

b=sqrt((c*c-a*a))=47:29

major_axis=117 nm

minor_axis=94:57 nm

We can also use the algorithm displayed in Appendix B

to determine the unobstructed range ring (circle) when the

HLZ and MTF are colocated. In this case, the distance

between the HLZ and MTF is 0 NM, as opposed to 60 NM.

The algorithm output for the colocation scenario follows:





Dow


ic.oup.com/m


a=0 nm #a=0:5*d(HLZ,MTF)


b=sqrt((c*c-a*a))=56

major_axis=112:00 nm

minor_axis=112:00 nm

This algorithm can also be used to determine how far

the unobstructed range ring (ellipse) can be stretched before

the MEDEVAC coverage is infeasible. Following the pre-

vious examples, if the HLZ and MTF are not colocated

and the distance between them exceeds the maximum allow-

able range (e.g., 112 NM), then the algorithm outputs that

the coverage region is infeasible as follows:



NOTE: distance cannot be covered by given range:

In addition, there must be a realistic lower limit to the

minor axis (aka ellipse height) of the unobstructed range

ring because if the maximum allowable range is equal

to the distance between the HLZ and the MTF, the range

ring would become a line segment between these two

points. For example, if both the range and the distance

are input into the algorithm as 112 NM, this would allow

the MEDEVAC asset to depart the HLZ, pick up the

patient(s) on the straight line to the MTF, and then trans-

port the patient(s) to the MTF. However, this situation

would not allow for any deviation from that straight line,

making it operationally infeasible for MEDEVAC response.

For medical planning purposes and when visualizing the

MEDEVAC coverage region, it is clear that unobstructed

range rings with a minor axis of 0 NM are infeasible.

Therefore, the lower bound of the minor axis is set to

10 NM in the algorithm (the subsequent output for this

example follows).

d(HLZ,MTF) = 112:00 nm #distance between HLZ and MTF

range = 112:00 nm #of given aircraft type

a=56:00 nm #a=0:5*d(HLZ,MTF)


FIGURE 2. MEDEVAC coverage (circle) for colocated HLZ and MTF.



Dow


ic.oup.com/m


b=sqrt((c*c-a*a))=0:00 nm

NOTE: minor_axis=0:00 below cut-off point

(cut-off currently set to 10:00 nm)

Combining Circle and Ellipse UnobstructedRange Rings

In previous examples, we assumed that MEDEVAC assets

had to return to their origin, which resulted in circular

unobstructed range rings. However, if we were to allow

MEDEVAC assets to also return to another location (if

within maximum allowable range), then we can develop

multiple coverage options for MEDEVAC response. For

instance, suppose points Pm and Pn each represent a

colocated HLZ and MTF. At Pm there are 2 +UH-60Black Hawks (range 112 NM at 150 knots) stationed and

at Pn there are 2 + HH-60 Pave Hawks (range 90 NM

at 120 knots) stationed, which are U.S. Air Force search

and rescue helicopters used for MEDEVAC. Furthermore,

suppose we have three POI casualty locations (q1, q2, and

q3) with varying distance from both Pm and Pn. If we

allow crossover of these MEDEVAC assets, then two of

these three imaginary casualty locations (q2 and q3) are

covered by multiple options; the MEDEVAC coverage

of q1 remains unchanged. Using these example inputs,

we have depicted the algorithm for determining mul-

tiple MEDEVAC coverage options in Appendix C, and

the output of the algorithm follows:

q1 resolution options (within 60-min standard):

39.51 min / 61.28 nm using “vtA”: Pm->q1->Pm55.51 min / 101.28 nm using “vtA”: Pm->q1->Pn


37.61 min / 56.52 nm using “vtA”: Pm->q2->Pm50.64 min / 89.10 nm using “vtA”: Pm->q2->Pn59.55 min / 89.10 nm using “vtB”: Pn->q2->Pm


49.94 min / 87.34 nm using “vtA”: Pm->q3->Pn51.02 min / 72.04 nm using “vtA”: Pn->q3->Pn56.06 min / 102.64 nm using “vtB”: Pm->q3->Pm58.67 min / 87.34 nm using “vtB”: Pn->q3->Pm

Aeromedical Evacuation CoverageDecision Support

Immediately after an injury-causing event, the ground

forces typically submit a 9-line MEDEVAC request that

reaches high-level medical planners as it occurs in time;

although sometimes multiple MEDEVAC requests occur

simultaneously. Following the previous example, if we

assume that q1, q2, and q3 casualty events occur in that

order, then medical planners could use the output shown

above as an ad hoc decision-support tool to determine the

FIGURE 3. MEDEVAC coverage (ellipse) for HLZ and MTF not colocated.



Dow


ic.oup.com/m


best solution for allocation of MEDEVAC assets. Follow-

ing is an example:

1. When q1 occurs, the algorithm in Appendix C could

generate the list of possible solutions shown earlier.

The medical planners could then select the best option

based on many dynamic variables, such as the patient(s)

clinical needs, availability of MTFs, equipment required,

and impact on the overall MEDEVAC coverage.

2. When q2 occurs during the execution of the q1

MEDEVAC mission, the best option for the medical

planners depends on its earlier decision (see above

point). Using the fastest option for q1, the medical

planner uses the 2 + UH-60 Black Hawks based at

Pm, making them unavailable to resolve q2 within

the “60-minute” standard. Therefore, the medical plan-

ners would have to use the 2 + HH-60 Pave Hawks

based at Pn to resolve q2 in time.

3. When q3 occurs, the best option for medical planners

is to use the 2 + UH-60 Black Hawks based at Pm

because it is the more efficient method (assuming the

MEDEVAC assets already returned to Pm after com-

pletion of the q1 mission).

As previously noted, unobstructed range rings do not

consider altitude restrictions or other obstructions such as

no-fly areas, ROZs, mountains, and other physical barriers

that prevent a helicopter from traveling from origin to des-

tination directly. Therefore, please note that these unob-

structed range rings (circles and ellipses) are nearly always

overly optimistic in representing MEDEVAC coverage.

Nevertheless, unobstructed range rings provide the neces-

sary visualization useful for medical planning and decision-

making at the strategic, operational, and tactical levels.

CONCLUSIONSThere are numerous applications that require the ability of

decision-makers to take certain actions over a geographic

region. In particular, the U.S. Army needs to allocate limited

resources (e.g., mobile hospitals, forward surgical elements,

and MEDEVAC assets) across a geographic area during

combat operations. However, allocating any resource will

cause certain effects based on the network connections

among geographic locations. Understanding what true cover-

age capabilities of evacuation assets are, given 3-dimensional

terrain considerations, is vital to in-theater evacuation analy-

sis and yet currently unavailable. The algorithms provided here

directly address this problem.

Using the algorithms provided, medical planners can

discover the optimal configuration of assets necessary to

support the medical requirements in a theater of operation.

The algorithms here can be implemented in a wide variety of

medical planning tools currently in existence, including the

Medical and Casualty Estimation tool currently maintained

by the Center for AMEDD Strategic Studies and promul-

gated through the Army Knowledge Online Web site. We

have used the algorithms presented here for several mock

and historical scenarios, and we are currently incorporating

them into simulations. Most importantly, however, future

work will validate its “in the field” utility.

In this article, we presented geospatial-based algorithms

for computing unobstructed range rings as a methodology for

MEDEVAC coverage visualization and medical planning

decision-support. The intent is for military medical plan-

ners to use these geospatial analytical methods to improve

real-time situational awareness by visualization of MEDEVAC

coverage, showing which areas can be reached within estab-

lished timelines. Furthermore, the aim of this decision-support

tool is to support medical planning by visualizing the impact

of changes in the medical footprint to the MEDEVAC cover-

age. Finally, these methods can be used to support decision-

making by providing a time-sorted list of MEDEVAC asset

packages to select from, given the location of the patients.

ACKNOWLEDGMENT

This material is based on work supported by the National Science Foun-

dation under Grant no. DGE1255832.

APPENDIX AThe algorithm below (coded in Python 2.7.2) can be used

to compute MEDEVAC flight time and range for the UH-60

Black Hawk helicopter.

## ALGORITHM FOR DERIVING MEDEVAC FLIGHT TIME

AND RANGE

# Input: characteristics of each platform type

vtA = {‘key’: ‘vtA’, ‘name’: ‘UH-60 Black Hawk’, ‘speed’: 150.0,

‘max_elev’: 1500.0, ‘ntm’: 10, ‘load_pt’: 5}

# Input: list of platform types (with their characteristics)

vehicle_types = [vtA]

# Input: Constants applicable to all platform types

max_time = 60.0

# Determine available time and range for each platform type

for vt in vehicle_types:

# Initialize flight time to the maximum time of 60 minutes

flight_time = max_time

# Further subtract platform type specifics

flight_time = flight_time - vt[‘ntm’] - vt[‘load_pt’]

vt[‘flight_time’] = flight_time

vt[‘range’] = flight_time * (vt[‘speed’] / 60.0)

# Output: show results

print ‘=’*70print ‘key: name, flight, range’

for vt in vehicle_types:

print ‘-’*70

print ‘%(key)s: %(name)s, %(flight_time)d min, %(range)d nm’

% vt

print ‘@ %(speed)d kts and max elevation of %(max_elev)d m’

% vt



Dow


ic.oup.com/m


APPENDIX BThe algorithm below (coded in Python 2.7.2) can be used

derive the unobstructed range ring (ellipse) useful for

MEDEVAC coverage visualization and medical planning.

APPENDIX CThe algorithm below (coded in Python 2.7.2) can be used

for determining multiple MEDEVAC coverage options to

assist with optimal medical planning.

## ALGORITHM FOR DERIVATION OF UNOBSTRUCTED

RANGE RING (ELLIPSE)

import sys

from math import sqrt

# Defining a lower bound (arbitrary at this time)

MIN_MINOR_AXIS = 10.0

# Function for determining the ellipse

def determine_ellipse(distance_between_points, vt_range):

# Initialize and print the distance between HLZ and MTF

d = distance_between_points

print ‘d(HLZ,MTF)=%0.2f nm\ t\ t# distance between HLZ and

MTF’ % d

range = vt_range

print ‘range=%0.2f \ t\ t\ t# of given aircraft type’ % range

# Print error if the distance between points is greater than range

if ((vt_range - d) < 0.0):

print ‘NOTE: distance cannot be covered by given range’

return None, None

# Initialize and print various distances

a = 0.5 * d

print ‘a=%0.2f nm\ t\ t\ t# where a=0.5*d(HLZ,MTF)’ % a

c = R = 0.5 * range

print ‘c=R=%0.2f nm\ t\ t\ t# where R=0.5*range=0.5*%0.2f=%0.2f’

% (R, range, R)

b = sqrt((c*c)-(a*a))

print ‘b=sqrt((c*c)-(a*a))=%0.2f nm’ % b

# Ellipse expressed with major- and minor axis

major_axis = 2.0 * R

minor_axis = 2.0 * b

if (minor_axis < MIN_MINOR_AXIS):

print ‘NOTE: minor_axis=%0.2f below cut-off point’

% minor_axis

print ‘(cut-off currently set to %0.2f nm)’

% MIN_MINOR_AXIS

return None, None

return major_axis, minor_axis

# Input: distance between HLZ/MTF and maximum range

distance = 112.0

range = 112.0

# Compute the ellipse by calling the “determine_ellipse” function

print ‘=’*70major_axis, minor_axis = determine_ellipse(distance, range)


print ‘-’*70

if (not (major_axis is None)):

print ‘major_axis=%0.2f nm’ % major_axis

print ‘minor_axis=%0.2f nm’ % minor_axis

else:

print ‘Infeasible for coverage. . .’

% vt

## ALGORITHM FOR DERIVING MULTIPLE MEDEVAC

COVERAGE OPTIONS

# Input: characteristics of each platform typevtA = {‘key’: ‘vtA’, ‘name’: ‘UH-60 Black Hawk’, ‘speed’: 150.0,


vtB = {‘key’: ‘vtB’, ‘name’: ‘HH-60 Pave Hawk’,‘speed’: 120.0,


# Input: list of platform types (with their characteristics)vehicle_types = {‘vtA’: vtA, ‘vtB’: vtB}

# Input: constants applicable to all platform typesmax_time = 60.0

# Input: lay-down of MEDEVAC assets at locations Pm and Pnlaydown = {‘Pm’: {‘vtA’: 2}, ‘Pn’: {‘vtB’: 2}}

# Input: POI locations with casualtiesevent_locs = [‘q1’, ‘q2’, ‘q3’]

# Input: distances between pointslst_dist = [(‘Pm’, ‘q1’, 30.64), (‘Pm’, ‘q2’, 28.26), (‘Pm’, ‘q3’, 51.32),

(‘Pn’, ‘q1’, 70.64), (‘Pn’, ‘q2’, 60.84), (‘Pn’, ‘q3’, 36.02)]

distances = {}for (src, dst, dist) in lst_dist:

distances[(src, dst)] = distdistances[(dst, src)] = dist

# Determine available time and range for each platform typefor vt in vehicle_types.values():

# Initialize flight time to the maximum time of 60 minutesflight_time = max_time

# Further subtract platform type specifics

flight_time = flight_time - vt[‘ntm’] - vt[‘load_pt’]vt[‘flight_time’] = flight_time

vt[‘range’] = flight_time * (vt[‘speed’] / 60.0)

# Determine (multiple) coverage options; aka potential solutions

solutions = []

for (location, assets) in laydown.items():

src = location

for dst in laydown.keys():

for pup in event_locs:

leg1 = distances.get((src, pup), None)

leg2 = distances.get((pup, dst), None)

if ((leg1 is None) or (leg2 is None)):

continue

dist = float(leg1 + leg2)

for (asset, count) in assets.items():

if (count <= 0):

continue

vt = vehicle_types[asset]

range = vt[‘range’]

if (range >= dist):

# Estimate required time

flight_time = dist / (vt[‘speed’] / 60.0)

time = flight_time + vt[‘ntm’] + vt[‘load_pt’]

sln = (pup, time, dist, src, dst, asset, count)

solutions.append(sln)

(continued)



Dow


ic.oup.com/m


REFERENCES

1. Department of the Army. Field Manual 4-02.2—Medical Evacuation

2007: Washington, DC. Available at http://armypubs.army.mil/doctrine/

DR_pubs/dr_a/pdf/fm4_02x2.pdf; accessed September 30, 2013.

2. Fulton LV, McMurry P, Kerr B: A Monte Carlo simulation of air

ambulance requirements during major combat operations. Mil Med 2009;

174(6): 610–14.

3. Bastian ND: A robust, multi-criteria modeling approach for optimizing

aeromedical evacuation asset emplacement. J Def Model Simul 2010;

7(1): 5–23.

4. Bastian ND, Fulton LV, Mitchell R, Pollard W, Wierschem D, Wilson R:

The Future of Vertical Lift: initial insights for aircraft capability

and medical planning. Mil Med 2012; 177(7): 863–9.

5. Fulton LV, Lasdon LS, McDaniel RR, Coppola MN: Two-stage stochas-

tic optimization for the allocation of medical assets in steady-state combat

operations. J Def Model Simul 2010; 7(2): 89–102.

6. Fulton LV, Perry M, Wood S, Bewley L: Engineering the new combat

support hospital. J Def Model Simul 2010; 7(1): 25–37.

7. Bastian ND, Fulton LV, Mitchell R, Pollard W, Wilson R: Force design

analysis of the Army aeromedical evacuation company: a quantitative

approach. J Def Model Simul 2013; 10(1): 23–30.

8. Bastian ND, Brown D, Fulton LV, Mitchell R, Pollard W, Robinson M,

et al: Analyzing the future of army aeromedical evacuation units and

equipment: a mixed methods, requirements-based approach. Mil Med

2013; 178(3): 321–9.

9. Python Programming Language 2013. Available at http://www.python

.org/; accessed September 30, 2013.

## ALGORITHM FOR DERIVING MULTIPLE MEDEVAC

COVERAGE OPTIONS

# Sort the solutions found (less time needed is better)

solutions.sort()


print ‘=’*70for event_loc in event_locs:

print ‘%s resolution options (within 60-min-standard):’ % event_loc

for (pup, time, dist, src, dst, asset, count) in solutions:

if (pup <> event_loc):

continue

rec = (time, dist, asset, src, pup, dst)

print ‘%8.2f min / %5.2f nm using “%s”: %s->%s->%s’ % rec

print ‘-’*70

APPENDIX C. Continued



Dow


ic.oup.com/m


Aeromedical Evacuation Planning Using Geospatial Decision ...

Documents

Transcript of Aeromedical Evacuation Planning Using Geospatial Decision ...