PREPOSITIONING AID FOR ASIA AND OCEANIA
Rubel DASPhD candidate,Tokyo Institute of Technology
2-12-1-I4-12, O-okayama, Meguro-ku,
Tokyo, 152-8550, JAPANTEL/FAX: +81-3-5734-3468
E-mail: rubeldas@tp.ide.titech.ac.jp

Shinya HANAOKA
Associate Professor,
Tokyo Institute of Technology
2-12-1-I4-12, O-okayama, Meguro-ku,
Tokyo, 152-8550, JAPANTEL/FAX: +81-3-5734-3468
E-mail: hanaoka@ide.titech.ac.jp

ABSTRACTPast disasters such as, China’s earthquake, Myanmar’s cyclone, Pakistan’s earthquake, Indian Ocean
Tsunami, Taiwan’s earthquake, Christchurch earthquake, and Japan’s earthquake exhibit thevulnerability of Asia-Oceania zone for natural sudden onset disaster where the mean affected per
disaster is the highest. The mean affected people per Meteorological disaster in Asia are about 830
thousands and in the same region mean affected people is more than 100 thousands per earthquake
disaster. While Asia-Oceania zone is facing 60% of all natural disasters (1), forecast estimates thatnatural and man-made disaster will increase five folds both in number and impact over the next 50
years. Hence, need for disaster relief provided by humanitarian organization is inevitable with quick
response to minimize the causalities. There is negative relationship between preparedness andresponse delay, and pre-positioning of emergency supplies is one mechanism of increasing
preparedness for natural disasters. In this concern, this paper develops quantitative analysis for effectof prepositioning in Asia and Oceania zone. A series of formulations through P-median formulation
predict optimal facility configurations, and corresponding per capita distances. The analysis also
provides the insights into the effect of initial conditions. Moreover, demand patterns, along withcorrelated variables such as population and hazard frequency, offers views of country’s vulnerability
to natural disaster. Finally, from the variant solutions several recommendations are made to provide
guidance on planning for humanitarian relief operation.
Key words: Preparedness, Pre-positioning, Humanitarian response, Per capita distance

INTRODUCTION
Cyclone Nargis in Myanmar on 2008 left her footprint through 78,000 dead, 56,000 missing andabout 2.4 million victims (2). In the same year, China’s Sichuan province was shuttered by massive
7.9 earthquakes which tolls more than 68,500 dead, another 20,000 missing, 300,000 hurt and 5
million people homeless. The consecutive earthquakes in New Zealand on September 4 th 2010 ofmagnitude 7.1 earthquake followed by another major earthquake of magnitude 6.3 on February 22 nd
2011 cause the worst disaster in the history of New Zealand in terms of economic losses. While 500
small or large scale disasters affect 200 million people every year (3), forecast estimates that over the
next 50 years natural and man-made disasters will increase five-fold both in number and impact (4).
The disaster occurrence location distribution is not even in the world. Asia-Oceania region is facing
60% of total disaster (1). Besides high concentration of total number of disaster in Asia Oceania
region, the mean affected per disaster is also high in this zone. Asia bears more than 800 thousand
affected people per meteorological disaster, caused by short-lived/small to meso scale atmosphericprocess. The mean affected per meteorological disaster in Africa, America and Europe is 78 thousand,44 thousand and 23.75 thousand respectively. The geographical location, socio-economic
characteristics carry Asia to be the highest impacted from each meteorological disaster. Extensive
poverty in South Asian countries is a major cause of the massive human toll that is taken by naturetriggered disaster (5). The mean affected people in Asia per earthquake disaster are more than 100
thousand which is more than any other part of world. The highest impact from each disaster in thiszone has led to intense pressure on the humanitarian organizations to improve operational
effectiveness of disaster relief efforts in this zone.
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The aim of humanitarian organization in the humanitarian relief support is to rapidly provide relief(shelter, water, medicine, and food) to hazard affected areas, so as to minimize the human suffering(6). In general the purpose of humanitarian response is to mitigate the harmful impacts of the hazard
on the local population. However, with the increasing trend of disaster, and of perception that thequality of relief is inadequate, the international community is calling for the improvement in theprocess of delivering aid (7). In this regard, logistics is an issue in which improvement might yieldsignificant benefits because relief operation is conducted in disrupted transport network. The speed ofdelivery or responsiveness has been identified as the most desired effect (8). Acquisition and deliveryof adequate relief supplies from local and/or international suppliers are typically time-consuming andexpensive. Relief organizations are seeking alternatives to reduce the delay. Even though, there areheavy debates on best approach to meet this end, pre-positioning has been adopted as a possible
logistics strategy in the aim of reducing delay (9). The pre-positioning conception for disaster
response is borrowed from the military operation where it become popular since World War II (10), if
not earlier. There is similarity between military actions and humanitarian response where demand isunpredictable and rapid response is critical to save lives.
World Vision International (WVI) accepted the prepositioning strategy in 2000 and establishedfacilities in four locations. The locations are USA, Italy, Germany and Dubai (11). The World Food
Program (WFP) operates the United Nations Humanitarian Response Depot (UNHRD). Depots arelocated in Italy, Ghana, UAE, Malaysia, and Panama. The strategy of UNHRD is to reach the
emergency location within 24/48 hours. The HRDs serves as regional staging areas to support thehumanitarian response through ‘pull’ strategy rather than ‘push’ strategy (12).
In nutshell, the impact of natural disaster in Asia and Oceania tends to increase with population
growth, and global warming. Although this zone is the most vulnerable Most of the Asian andOceania countries have not paid serious attention to disaster management (13), per se, especially interms of preparedness, response and recovery. Logistics is often-cited as an area that might improve
this effort, and inventory pre-positioning has been specially suggested as a logistical strategy toward amore rapid response (8). In this study, we take an attempt to explore the distribution network
problems to response in Asia and Oceania zone and provide quantitative analysis to provide
assistance to humanitarian organizations to design the distribution network. We considered different
scenarios under two broad categories Blank page case and Status Quo case. We have used theclassical P-median problem to formulate the location decisions. It integrates distance, potential
demand and country characteristics. Demand is calculated for two different sudden onset disasters:
meteorological disaster and earthquake disaster. The model generates results for two different
disasters.
The rest of the paper is organized as follows: section 2 reviews the relevant literature. Section 3
describes the relief chain and explains details for critical and non-critical portion of the chain toformulate the model. Section 4 describes the dataset and analysis of model result. Finally, we
conclude in section 5 with recommendation for improvement of distribution network.

SYSTEM AND MODEL
In this section, we delineate the key components of relief distribution network affecting on facility
location decisions and formulate the model.

HUMANITARIAN LOGISTICS STAGE
As each disaster has unique characteristics and requires different strategy to response, we makeboundary of our study and focus on sudden on-set natural disasters. This group includes
meteorological phenomenon such as typhoons, cyclone etc. and geophysical phenomenon such as
earthquake etc. Here, it is worth to note that natural disaster can be classified in two groups: sudden
on set disaster and slow onset disaster. The sudden-onset and slow-onset disaster have distinct featurein exposure and actions. The slow-onset disaster such as flood, famine etc. does not pose initial time
sensitive urge rather it needs consistent support for long time. The response to sudden onset disaster ischallenging considering the demand unpredictability, dynamic, and network limitation. Even though2the uncertainty and variability of environment prevails just aftermath of sudden onset disaster, quick
response to the disaster theater is critical for minimizing human suffering and for overall success ofhumanitarian operation.

Figure 1. Phases of disaster response
The importance of initial response is emphasized in several articles (17). The relief mission life cycle
analysis shows that fewer amounts of resources were available in hazard sites after math of sudden
onset disaster. The more quickly commodities reach the disaster theater, the better the chance of
mitigating disaster related harm. Reducing delivery time in this crucial time is the objective of prepositioning in humanitarian logistics. The prepositioning of aid is named here as preparedness. Balickand Beamon (2008) states the speed of relief operation during first few days of the disastersignificantly affects the lives of many people threatened by the disaster. Sheu (2007) formulates relief
distribution model by assuming the critical period is 72 hours for earthquake. This period is known as‘immediate response’. However, relief activities sustain for little longer time to support the survivors
to overcome the losses. Considering less time sensitivity compare to immediate response the supply
chain focus also on cost efficiency with the given expected level of service to survivors. This stage isnamed as ‘survival help’ in this research. In the ‘immediate response’, Most of the critical supplies
arriving to the disaster areas are sourced from relief organizations’ global prepositioned stocks (6) byfollowing ‘push’ strategy. ‘Survivor help’ propels pull strategy to bring the product to disaster
stricken area. In this perspective humanitarian logistics is characterized by three distinct stages
considering time of actions: preparedness, immediate response and survival help. These three stages
are interlinked and the success of humanitarian logistics depends on appropriate decision on eachstages.

DELIVERY CHAIN
Prepositioning aid aims to support immediate response in sudden onset disaster. However, the relief
chain starts before disaster. Delivery chain for prepositioning has two phases: before disaster and after
disaster. The chain starts with inventory at vendor’s location. The vendor selection is correlated withlocation of preposition (here after ‘facility’ indicates the location of preposition). The inbound transit
to facility from vendor’s location is one decision making criteria for vendor selection. The inboundtransit is not time sensitive action as it takes place before disaster. The time sensitive section starts
after approval of relief aid. The delivery chain is represented in figure 2.

Figure 2. Relief chain (edited from (8))
It is customary that disaster stricken country ask international organizations to assist. The
humanitarian organizations assess the appeal and estimate the demand. In response to approved
appeal, the pre-stocked commodities load on truck and shift to nearby airports. Herewith, sufficient3documents such as invoice, consignee documents, visa (for accompanied personnel) etc. are required
before transporting commodities. The documentation is a source of delay to response. Without
sufficient documentation, waiting time at port of entry will be prolonged. After customs processing,
the commodities transport by using suitable transport mode to central distribution center anddistributes through distribution center. The delivery chain ends with distribution of aid to the victims.
As demand is unpredictable, there is possibility of supply and demand mismatch. Unused aid or nonconsumable aids are returned. The reverse flow of aid is named as reverse logistics.

CHAIN SIMPLIFICATION
We consider a delivery chain in which a relief organization locates facility to response immediately todisaster stricken area. As this study aims to facility location for improving immediate response after
disaster, the vendor location and inbound transit are ignored for our model. To make the modeltractable, we simplify the delivery chain by assuming some reasonable assumption. One assumption
about facility location is that it will be close to airport. Locating facility near airport can reduce thedelay by cutting the in-transit inventory to source airport. WFP followed this strategy to establish
UNHRD in five locations. Ghana UNHRD is established near Kotoka International airport, Malaysia
UNHRD is near Subang Military airport; Italy in military airport, Panama in Tocumen International
airport and UAE in Dubai international airport. Herewith, it is assumed that relief aid transports by air
cargo to port of entry. Delayed associate with last mile, including the custom processing is consistentacross the range of disaster and geographic location. The activities of custom processing and last mile
distribution are relaxed in this model. The simplified relief chain consists of facility location (i), intransit inventory on air cargo and destination country (j).

Figure 3. Simplified chain

MODEL FORMULATION
To facilitate the model formulation, the following assumptions are postulated
1) The sudden onset disasters are only considered (earthquake, storms)
2) Stocks are positioned before disaster and there is no capacity limitation of facility3) Total affected people in stricken area (denoted j) is the proportional to demand for aid
4) Facilities (denoted i) are located in (or near) airport
5) Aids are carrying by only air-craft. It can fly with infinite capacity (8). All air craft is flown
in same speed (500km/hr).
Based on these assumptions, we have adopted P-median facility location model (15,16). This modelseeks to place p facilities strategically among the demands. It consists of finding of optimal locationfor exactly p facilities to meet a specified demand at the lowest possible transportation cost. The Pmedian problem uses the average distance between service and demand points to determine theservicing costs of a given location. The p median problem can be called as the binary program.
(1)
subject to
(2)
(3)
(4)
(5)
4

In this formulation, represent demand at location . is a binary variable for showing open siteandis a binary variable to allocate location to open facility
The distance ( ) is calculated by using Haversine method which is used to calculate the great circle
distance between any pair of latitude ( ) and longitude ( ) coordinates on a sphere.
(6)
(7)

Where
and
represent the difference between latitude and longitude difference between two
points and R is earth equilateral radius (6377 km). For the sake of computation, capital of a country is
considered as demand and generator and coordinates for each capital is collected through Google. The
Haversine distance is calculated by using online service (20).
Finally, the objective of this model is to identify the facility location to minimize the deliver lead time.
Facility positioned near demand point will yield lower response time; the transportation time is alinear function of distance to the demand point(8)
In order to evaluate the efficiency of logistics decision a metric is adopted here, distance per capita at
risk. This measure represent the mean distance per person to be at risk of hazard induced affectedfrom the nearest facility. The mean distance per capita is calculated by using following formula(9)
Until now, it has not been possible to predict the relief demand. However, there are ongoing
initiatives to predict the relief demand by using proxy variable. For instance, Dilley et al (2005)
portrait high-risk geographic areas, based on historical worldwide disaster frequency and mortality
data, population data and economic indicator. Balick and Beamon (2008) use mortality as an indicatorfor demand. FEMA’s Hazus software is an assessment tool that can estimate losses from potential
hurricane, earthquake and flood. Since facility location is directly influenced by demand data,
realistic forecast of demand data can significantly contribute to effectiveness of facility location(Balick and Beamon, 2008). In this study, we model demand quantities as the proportional of the total
affected people as a result of hazard. The total affected people for each hazard from 1980 to 2011 in acountry are collected and the maximum value for single disaster in each country from this list is
identified.
(10)
Data for total affected people for each disaster is collected from EM-DAT database. EM-DAT definesthe ‘total affected’ as the sum of injured, homeless and people requiring immediate assistance during
a period of emergency. EM-DAT stores hazard data for events if at least one of the following criteria
is fulfilled 10 or more people reported killed 100 people reported affected Declaration of a state of emergency
 Call for international assistance.
EM-DAT database store data about country, disaster group, disaster type, date, killed, injured,
homeless, affected, total affected, estimated damage. We have analysis the facility location formeteorological disaster and earthquake disaster. Both disasters are sudden onset disaster and cause
wide spread damage. Some countries in Asia Oceania zone are prone to meteorological disaster and5others are earthquake prone. Some countries such as Japan, China, and India are vulnerable to bothtype of disasters. EM-DAT database does not have any entry for Brunei and Singapore of
meteorological and earthquake history.

RESULT
We group the study area in 29 regions based on geographic location. Here regions indicate thegeographical boundary for each country except for Oceania. Oceania consists of hundreds of islands
and we use clusters of Oceania in four groups: Australia and New Zealand, Polynesia, Melanesia, andMicronesia. One point to note, we have not included UN defined Western-Asia considering proximityfrom two UNHRD depots: one is in Dubai and another one is in Italy. It is noted that the purpose ofthis study is to see the optimal facility location for immediate response on sudden onset disaster. The
study area regions are shown in table 1. The fourth and fifth column represent that demand data foreach zone.
id

country

population(thousand)

c1
c2
c3
c4
c5
c6
c7
c8
c9
c10
c11
c12
c13
c14
c15
c16
c17
c17
c18
c19
c20
c21
c22
c23
c24
c25
c26
c27
c28
c29

Australia
Bangladesh
BhutanBrunei
Cambodia
China
East Timor
India
IndonesiaJapan
Lao PDR
Malaysia
Maldives
Melanesia
Mongolia
MyanmarMicronesia
Nepal
New Zealand
Korea, Dem. Rep.
PakistanPapua new Guinea
Philippines
Polynesia
Singapore
Korea, Rep.
Sri Lanka
TaiwanThailand
Vietnam

22,327
164,425708
407
14,138
1,338,300
1,171
1,170,938
232,517
127,380
6,436
27,914
314
8800
2,701
50,496546
29,853
4,371
23,991
173,383
6,888
93,617668
5,140
48,875
20,452
23,071
68,13988,362

max affected in a year forearthquake disaster (19802011)
5,025
15,20020,0160047,437,647
0
6,321,812
3,215,982
543,187
0
5,063
27,214
14,100021,27771
7,367
301,845
05,128,309
20,2001,632,072
5,585
0
0
1,019,306
108,918
67,0070

max affected in a year for
meteorologicaldisaster(1980-2011 )
2,860,414
15,439,149
65,0000178,091
107,403,094
8,730
13,870,008
10,000331,039
1,000,000
41,00023,849117,500
1,071,000
2,420,000
8834
165
2,000
88,6251,650,000
162,140
12,221,663
195,000
0627,180
375,000
2,307,523
1,894,238
15,651,884

Table 1. Zonal data for population and disaster impact (18,19)

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In the first step of our analysis, we assume that humanitarian organization will not use UNHRD. wenamed this situation is ‘free form’ distribution network configuration. In this situation, we calculated
the number of facility requirement to ensure the less than 500 km per capita. The solution of thisquery for earthquake is 2 (Figure 4). The potential location is c6 and c8 for earthquake disaster. Twofacilities can ensure the distance per capita is 464 km in this zone. The single facility c6 make thedistance per capita is 1131 km. The maximum distance of demand point from facility location is 9024
km.
The similar analysis for meteorological disaster, the minimum required facility is four (Figure 6). The
potential locations are c6, c28, c8, c1. The mean distance per capita is 397 km/person. The maximum
distance of demand point from nearest facility is 3288 km. The facility c1 significantly impact on
reducing the maximum distance.
4500

1200

c6

4000

1000

c12

3500
3000

km/person

km/person

800

2500

600

2000

c6, c8400

1500

c6,c8,
c9, c16200

c12,c6,c8, c91000

c12,c6

500
0

0
1

2

3

Number of position

1

4

2

3

Number of position

4

Figure 4. Per capita distance sentivity for Figure 5. Per capita distance sensitivity forearthquake disaster (free form)
earthquake disaster (status quo)
1,600

4000

1,400

3500 c12

c6

3000

km/person

1,200
km/person

1,000
800
600

c6,c28,c8, c1,c16

c6, c28

400
200

2500
2000
1500

c12,c6,c8, c11000

c12,c6

500
0
1

0
1

2

3

Number of position

4

2

3

Number of position

4

Figure 6. Per capita Distance sensitivity for Figure 7. Per capita distance sensitivity for
metorological disaster (free form)
meteorological disaster (status quo)
EQ=Earthquake; met = Meteorological

Status Quo situation represent the current location of UNHRD in Malaysia (c12) and humanitarianorganization is willing to expand the network with respect to pre-existing UNHRD. Since any
humanitarian organization can use UNHRD to stock the commodities, the extension of network is
designed with respect to c12. With the single pre-positioned in c12, the mean distance per capita forearthquake reveals 4110 km/per person (Figure 5). After addition of one new facility in c6, the mean
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distance per capita downs to 885 km/person. After addition to one facility in c6, the mean distance percapita for meteorological disaster was 1020 km/person (Figure 7). Both measures are more thatdesired service level which is 500 km/ per capita. To achieve this target, another facility in c8 is
required.
1

2

3

4

0

-500

1

-1500

free form-500

status quo

-1000

-2000

km

km

2

3

4

0

-1000

-2500

-2000

Free form
status quo

-1500
-2500

-3000

-3000

-3500

Figure 8. Diminish return on position for Figure 9. Diminish return on position forearthquake
meteorological disaster
Numbe
r
of
facility
status
quo
free
form

1

2

3

4

-3224

-612

-62

Numbe
r
of
facility
status
quo

-31

free
form

-667

-251

Table 2. Diminish return on position forearthquake

1

2

3

4

-2685

-419

-128

-573

-259

-145

Table 3. Diminish return on position formeteorological

The improvement (reduction) in distance gained by adding single facility to the system is calculated
as the distance per capita minus the distance per capita from the previous iteration. A steeper slope
indicates a more significant impact in the reduction of per capita distance. It is observed that each
additional facility makes a lesser impact. It shows the trend of diminishing return on positions. The
slope of curve in figure 8 and figure 9 indicate the marginal impact of each additional position to thesystem. The status Quo situation has vast impact after adding first facility in network.

CONCLUSION
The data suggest that the majority of affected people in both earthquake and meteorological disaster
belong to China. India and Pakistan are the second and third most affected country for earthquake
hazard respectively. The meteorological disaster shows different demand structure where second andthird most affected countries are Bangladesh and Vietnam respectively. The strategic design ofhumanitarian network requires including both types of disasters since humanitarian organization
responses to all type of disasters. The network design will be sub-optimal if the design considerationis only single type of disaster. Slow-onset disaster is not considered here since quick response is not
required for slow-onset disaster; rather it requires constant supply of commodity.
The model was evaluated by using two criteria distance per capita and maximum distance. The single
facility location in c6 can minimize the distance per capita while location in c1 can minimize themaximum distance from facility to demand point. Herewith, it is required to mention that according toour analysis it shows that Malaysia UNHRD is not in optimal location. However, facility location8

decision also depends on other criteria such as land availability, political stability, land suitability,transports mode availability etc besides the distance per capita criteria. Since other criteria are notmeasureable, we use single most important factor to show prepositioned sensitivity for immediate
response.
Finally, Funding structure in humanitarian organization is one obstacle for making decision about
facility location that donors are not interested to invest for network design. Since preparedness can
reduce probable impact from disaster and Asia-Oceania is highly vulnerable, the proposed solution
may be helpful for humanitarian organization to make decision in network design.

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