Journal of Natural Disaster Science, Volume 36, Number 2, 2015, pp35-52

Toward resilient humanitarian cooperation:
examining the performance of horizontal cooperation among humanitarian organizations
using an agent-based modeling (ABM) approach
Junko Mochizuki a*, Fuminori Toyasaki b, and Ioanna Falagara Sigala ca Risk Policy and Vulnerability Program,the International Institute for Applied Systems Analysis (IIASA)b School of Administrative Studies, Management Science, York University,
4700 Keele St., Toronto, ON, Canada M3J 1P3
c Vienna University of Economics and Business* Corresponding author, postal address: Schlossplatz 1 - A-2361 Laxenburg, Austria.
Email: mochizuk@iiasa.ac.at

ABSTRACTThis article proposes a multi-agent simulation model to examine how different operational
environments and incentive mechanisms affect the collective performance of complex humanitarian
response systems. Using the UN Humanitarian Response Depot (UNHRD) system as an example, astylized model of one service provider, two member organizations and multiple humanitarian crises
is developed to illustrate the changing uses of four alternative relief goods sourcing options, namely:i) own storage for own items ii) UN storage for own items iii) stock-swaps and iv) white stock
uses. Under the plausible assumption that the past success of sourcing options influences member
organizations’ future resource allocation, the model indicates that the additional buffer stock capacity
offered by horizontal cooperation induces undesirable system dependency: while it gives member
organizations more flexibility to meet highly stochastic demands under uncertainty, it also encourages
them to store less of their own relief goods as a result. This tendency was particularly notable under aflexible budgeting regime, highlighting the further need to understand and evaluate the details of thedecision-making heuristics of individual member organizations.
Keywords: Horizontal Cooperation, Humanitarian Organization, Agent-Based Modeling

1. Introduction

risen in recent years (Thomas and Kopczak, 2005),
increasing efforts have been exerted globally to build more
effective and efficient coordination mechanisms among
humanitarian organizations.

Learning and improvement are seen as the key tosuccessful humanitarian coordination where ever-changing
and complex operational environments demand proactive
strategies for the successful delivery of emergency
assistance (UN 2013; Christopher and Tatham, 2014).
Responding to the rising demand for global humanitarianassistance observed in recent years, the total official
humanitarian assistance by bilateral and multi-lateral donors
has nearly tripled from approximately $6.5 billion in 2001 to
16.4 billion in 2013 (Global Humanitarian Assistance 2009;
Global Humanitarian Assistance 2014). As the frequency
and complexity of natural and man-made disasters have

Horizontal cooperation among humanitarianorganizations is increasingly seen as an efficient andeffective way to coordinate humanitarian preparedness,
response, and recovery processes under complex andchanging environments (Thomas and Kopczak 2005,
Van Wassenhove 2006, Schulz 2009). Unlike verticalcooperation in which organizations at different stages of
the humanitarian response chain collaborate, horizontal
cooperation encourages pooling of skills and resources

35

J.Mochizuki, F.Toyasaki, I.F.Sigala

proposes a multi-agent simulation model that examines how
different operational environments and incentive mechanisms
affect the collective performance of the complex humanitarian
response system. In particular, the article uses the UNHRD
system as an example and proposes a model that simulates
the emerging system behaviors of individual members
responding to simultaneously occurring humanitarian crises.
Prior to model development, a number of semi-structured
interviews with the UNHRD and its member organizations
were conducted (see Appendix 1 for a list of interviews)
to understand the current status of network operations andchallenges. These on-the-ground insights provided important
inputs to the design of the present agent-based simulation
model. While the development of a full-fledged model is an
ongoing process, this article describes the main features andalgorithms of the proposed stylized model and presents theresults based on an illustrative case application.

among organizations that are operating at similar levels(Schulz and Blecken 2010). The formation of horizontal
cooperation mechanisms globally is rapidly changing
the ways capacity building, warehouse management
and emergency delivery are carried out collectively byhumanitarian organizations.
One example of horizontal cooperation that has
grown in recent years is the UN Humanitarian Response
Depot (UNHRD) established in 2001. As of February 2015,
the UNHRD has grown to a truly global system of six
warehouses—strategically located across the globe in Accra(Ghana), Brindisi (Italy), Dubai (UAE), Subang (Malaysia),
Panama City, (Panama) and Las Palmas (Spain) offering
a range of humanitarian logistics services to 65 member
organizations (UNHRD, 2015). Member organizations
adhere to a standard set of operational procedures thatenables effective pooling of resources and substantial costsavings in terms of storage, procurement and transportation.
For example, it is estimated that transporting relief items via
air to N’Djamen, Chad cost between 25%-45% less fromthe regional depot in Accra, Ghana than the headquarters
in Brindisi, Italy. By utilizing such regional hubs with freestorage, many organizations may reduce their humanitarian
logistics operation costs (UNHRD 2015).

The use of a stylized agent-based model of one
service provider, two member organizations and multiple
humanitarian crises illustrates the difficulty in coordinating apurely voluntary system of collective inventory management
under highly uncertain demand. Under the plausible
assumption that the past success of sourcing options
influences member organizations’ future resource allocation,
5-yearsimulation runs found that the additional buffer
stock capacity offered by horizontal cooperation induces
undesirable system dependency. This was particularly thecase under the flexible budget system where additional donor
funding allows a more flexible use of such buffer stocks.

However, the growth of such distributed humanitarian
logistics internationally has also given rise to newchallenges. The truly voluntary nature of collaborationand membership means that their success is predicated onmember organizations’ good will and trust in the collective
management system. The shared logistic arrangement
globally requires continued willingness of organizations
to collaborate, while the rapid expansion of operations
seen in recent years will likely add further complexity incoordination. There is also a need to acquire continued
funding support of donor agencies.

Factors such as whether, and to what extent, budgets
remain binding in the case of a catastrophic event andwhether and to what extent each organization uses different
types of heuristics for pre-positioning decisions (e.g.
according to past success, cost minimization or mixture ofboth) seem to influence how effective incentive mechanisms(such as rising and reducing the prices of buffer stocks)
affect individual decisions and system performance.
While the present model adopts a highly simplistic set ofassumptions, further sophistication of model assumptions
will certainly improve our understanding of horizontal
cooperation among humanitarian organizations.

As previously identified by Schultz and Blecken(2010), horizontal cooperation for humanitarian logistics
coordination may be hampered by a number of institutionaland operational factors such as: i) the desire of humanitarianorganizations to maintain logistics as core competency of theorganization (therefore, its unwillingness to delegate coretasks under collective management); ii) the differences inorganizational culture and mistrust towards each other andcollective systems; iii) limited transparency regarding thebenefits of cooperation and iv) a potential lack of resources
especially during a period of high demand. It therefore
remains to be seen how a global coordination mechanism
founded on the humanitarianism of good will and mutual
support may thrive in the face of growing demands likely
to materialize in the age of climate change and ensuing
regional conflicts (Leiras et al. 2014).

The remainder of this article is organized as follows.
First, Section 2 provides a brief overview of the literature
regarding humanitarian logistics and demonstrates how thisarticle makes a major contribution by applying agent basedmodeling analysis to the field of humanitarian horizontal
cooperation. Section 3 presents detailed descriptions of themodeling structure and algorithms, describing the individual
decision rules that collectively make up the complex systemof humanitarian coordination. This is followed by Section
4, which executes the proposed multi-agent model using an
illustrative example. Section 5 draws major insights gainedand describes the next steps for model development andfurther empirical application. Finally, Section 6 provides theconclusion of this article.

Given our need to understand the effectiveness androbustness of horizontal cooperation among humanitarianorganizations under changing environments, this article

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Journal of Natural Disaster Science, Volume 36, Number 2, 2015, pp35-52

2. Modeling Humanitarian Logistics Operation

while the latter operates to maximize share-holder values,
minimize costs and improve customer satisfaction. The
difference in strategic objectives is closely linked to thecontrasting ways in which the two entities raise funds—
while humanitarian organizations remain largely dependenton donor funding (Wakolbinger et al., 2011; Toyasaki and
Wakolbinger, 2014), business logistics must ultimately
be accountable to the shareholders. These differences
fundamentally shape the ways in which resources areallocated to various strategic, tactical and operational
objectives thereby ultimately determining the characteristicsof the two logistics systems. As illustrated in Section 3,
firstly, the proposed model incorporates a set of operational
goals that are identified as important by humanitarianorganizations participating in the UNHRD system byliterature and interviews such as reduction in lead-time andoverall logistic costs.

Quantitative modeling has long served as an importantanalytical tool in commercial supply chain management(Tayur et al., 1999; Cachon and Terwiesch, 2012; Christou,2011). Modeling approaches such as deterministic andstochastic optimization as well as various simulation
techniques have examined how different decision variables
such as the level of inventory, timing of production andbuyer-supplier relationships affect supply chain performancesuch as the overall cost of operation, customer satisfaction
and speediness of product delivery (Beamon 1998; Caunhye
et al., 2012). As opposed to commercial supply chain
logistics, which has long been assessed quantitatively inthe field of operations research: quantitative studies onhumanitarian logistics are relatively new with increasing
attention placed on topics such as strategic prepositioning ofrelief supply (Balcik and Beamon, 2008; Duran et al., 2011);
optimal inventory control under uncertainty (Beamon andKotleba, 2006) and the relationship between operation costs
and fund-raising (Wakolbinger et al., 2011; Toyasaki and
Wakolbinger, 2014) and others (see for example Caunhye
et al., 2012; Rafael et al., 2013; Ortuño et al., 2013 Göslingand Geldermann, 2014 for recent reviews on this topic).

Secondly, humanitarian logistics must face demands
that are highly unpredictable. Natural disasters (and to alesser extent regional conflicts), occur stochastically withlimited ability for organizations to predict timing, locations,magnitudes and types of events (e.g. hazards) in advance.
The specific relief items required also depend largely on
the nature of crises and the characteristics of the population
affected. Such unpredictability of demand contrasts sharply
with that of the commercial logistics system, which serves
relatively predictable demands in terms of their locations
and seasonality. Therefore, the proposed model incorporates
the stochasticity of crisis situations in terms of timing,
magnitudes and locations of demand occurrence.

In order to understand the complex interactions that
underlie the dynamics of global horizontal cooperation
among humanitarian organizations, analytical tools
should be sufficiently flexible to examine its multifaceted
nature. This gives flexible modeling environments suchas multi-agent modeling a natural advantage over othertechniques for quantitative analysis, in which collective
system performance can be modeled and evaluated basedon individual decisions of software agents. The utility ofagent-based modeling has been demonstrated in a number ofrecent studies on commercial logistics (Fu et al., 2000; Jiao
et al., 2006; Lai and Kao, 2009), while its application to thehumanitarian logistics domain remains limited even today.

Thirdly, humanitarian logistics serves a different
customer base, where recipients of support have noconsumer choice. There are neither explicit pricemechanisms that can regulate demand and supply nor
many alternative service providers to choose from. This
fundamentally departs from the commercial supply chain
where an individual consumer has the liberty to choose
from a variety of potential suppliers and exercises anoption to buy items if the market meets its expectation for
quality, price and overall service. Governing self-organizing
humanitarian supply chains therefore requires a unique setof principles that is uncommon to the commercially oriented
logistics systems. Instead of market prices, the concept of‘fair’ and ‘equitable’ access to relief items, for example,
becomes an important topic of humanitarian logistics
studies. We build an agent-based model that can explore
these supply chain governance issues (such as prioritizationof demand) in this study.

2.1. Commercial versus Humanitarian Logistics
The existing literature indicates that humanitarian
logistics are characteristically different from theircommercial counterparts (Van Wassenhove 2006; Kovács
and Spens 2007; Pujawan et al., 2009; Apte, 2010, Gatignon
et al., 2010). Quantitative analysis of humanitarian
supply chains therefore requires a sound understandingof the differences in operational objectives, management
approaches and workings of the two supply chain operations.
Beamon and Balcick (2008) for example summarize threemajor areas of divergence between commercial versus
humanitarian logistics, namely i) strategic goals ii) demand
characteristics and iii) customer characteristics.

2.2 Vertical versus Horizontal Cooperation—
Example of the UNHRD System

Firstly, Beamon and Balcick (2008) assert that thehumanitarian supply chain differs fundamentally from its
commercial counterpart since the former embraces strategic
goals such as reduction in human causality and suffering

Commercial and non-profit logistics cooperation
may be categorized as either being vertical or horizontal
in nature. Vertical cooperation may be def ined as a

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J.Mochizuki, F.Toyasaki, I.F.Sigala

24/48 hours to emergency locations (Table 1). The scopeof UNHRD service and membership has been steadily
growing. In 2013 for example, the UNHRD served a totalof 32 organizations, arranging 951 shipments (7278 MTof goods) to 90 countries with a total value of $49 million.
The system also facilitated the use of 30 stock swaps
between partners, or an exchange of 400 MT in relief
goods (UNHRD 2013). Our interviews indicate that the
UNHRD system is yet to achieve financial sustainability—
approximately 50% of the total operation costs are covered
through donor funding, and 25% are recovered from paid
services. The system currently has a 25% shortfall in
operational funding.

form of cooperation whereby there are “different actors
along the value chain of one industry, such as suppliers,
manufacturers, distribution centers and customers”(Schultz and Blecken 2010 p. 638) while that of horizontal
cooperation may be defined as those “tak[ing] place
between entities operating at the same level in the market”(ibid) such as international relief organizations that sharesimilar humanitarian operations. Horizontal cooperation is
being increasingly adopted in both commercial industries
such as maritime shipment, commercial aviation and others(Cruijssen et al., 2007) and also non-profit operations suchas fundraising (e.g. the Humanitarian Coalition Canada1),
procurement (e.g. the Humanitarian Procurement Centerof ECHO 2), and relief inventory management (e.g. theUNHRD).

1 Further information is available at:
http://humanitariancoalition.ca/
2 Further information is available at:
http://dgecho-partners-helpdesk.eu/actions_
implementation/procurement_in_humanitarian_aid/hpc

An example of horizontal cooperation explored inthis article is the UNHRD established in 2001. Throughstrategic prepositioning of relief items, the UNHRD aims
to achieve speedy delivery of relief goods globally within

Table 1: UNHRD Global Pre-Positioning Network
Location

Total storage (m2)

Dispatched in 2013

Users

Brindisi, Italy

6,600 (covered)
3,500 (open)

FAO, WHO, WFP, Italian Cooperation andInternational Medical Corps

Dubai, UAE

21,500(covered)
5,000 (open)

130 consignments to
23 countries with thetotal value of $ 9.5
million.
715 consignments to
91 countries with thetotal value of $30
million.

Subang,Malaysia

8,250 (covered)
1,000 (open)

Accra, Ghana

3,500 (covered)
5,000(open)

PanamaPanamacity,

3,605 (covered)

Las
Palmas,Spain

1,700 (covered)
4,400 (open)

Irish Aid, World Vision, CARE, FAO, Catholic
Relief Services, ECHO, Action Contre La Faim,
Johanniter, Mercy Corps, UNDP, International
Rescue Committee, Norwegian Church Aid, Qatar
Charity, OCHA, JICA, Handicap International,
Korea
International
Cooperation
Agency,ShelterBox, Lutheran World Relief, WHO, Italian
MOFA, WFP, Islamic Relief Worldwide, Concern
Worldwide, Solidarites International, UNRWA,
Save the Children, UNHCR, and ACTED.

40 consignments to 14
countries with the total
value of $4.4 million.
47 consignments to 17
countries with the total
value $4.6 million.
17 consignments to 11
countries with rhe total
value of $1 million.

ASEAN/AHA, AustralianAID, Care, Irish Aid, JICA,
Mercy Corps, Mercy Malaysia, NCA, Swiss Red Cross,
UNFPA, WFP, WHO and World Vision International.
UNICEF, Shelter Box, UNFPA, UNHCR, JICA, Irish
Aid, WHO, ACF, IFRC, Swiss Red Cross, World VisionInternational, WFP, and Global Mercy Mission Project.
Accion Contra El Hambre, Agencia Espanola deCooperacion Internacional Para El Desarrollo, Comisión
Cascos Blancos, Intermon Oxfam, Irish Aid, Korea
International Cooperation Agency, Mercy Corps,
Shelterbox, Swiss Red Cross, UNICEF Supply Division,
UNICEF TACRO, WFP, Panamerican HealthOrganization (PAHO), and World Vision International.

To be operational in
2014

To be operational in 2014

Source: UNHRD (2015)38

Journal of Natural Disaster Science, Volume 36, Number 2, 2015, pp35-52

by offering free-of-charge services such as warehousing,stock management, white stocks and transportation ofmember relief items. In addition, other services such as
procurement insurance and stock disposal may be obtainedon the basis of full cost recovery plus a management
recovery fee of 7%.

Confirming an observation in the existing literature,
our interviews also indicate that a variety of motivations
encourage or discourage humanitarian organizations to join
or exit horizontal cooperation (Schulz and Blecken 2010).
Many organizations interviewed stated that they havejoined horizontal cooperation to achieve cost reduction
through free-of-charge storage, consolidated procurement
and transportation, etc. Organizations also joined the
UNHRD to gain additional security and f lexibility inoperation made possible by an option to exchange relief
stocks in the case of shor tages. Others have joined
horizontal cooperation to create a collegial brand image
and to appeal for further donor support.

Voluntary coordination is an important aspect of
the UNHRD arrangement. There are neither explicit topdown mechanisms by which the WFP influences member
behaviors nor ways to ensure the optimal performanceof a syst e m . T he se r v ic e prov ide r ap proa ch t hu s
departs significantly from the commercial supply chain
management in which explicit incentives and penalties areused to influence supply chain behaviors (e.g., Cachon andZipkin, 1999, Cachon, 2001, Lee and Whang, 1999, andChen, 2001). The proposed model evaluates the emerging
behavior of voluntary horizontal cooperation in the absenceof such central management.

At the same time, a number of factors may also
hamper their continued membership. As Schultz andBlecken (2010) indicate, when an organization has different
operational philosophies, or if members feel that benefits
are allocated unfairly, or the system under-performs when
demands for goods are high, a member may be less willingto continue their collaboration. Our interviews indicate thatsome organizations are concerned with the transparency
and traceability of stock management and shipment and that
continued under-performance of the UNHRD (long leadtime) has already affected their willingness to continue
using the system. Given the diverse strategic, tacticaland operational goals of humanitarian organizations, it isimportant to evaluate how horizontal cooperation performs
under different operational environments and how theirexperience will affect a member’s willingness to continue
such cooperation. The proposed agent-based model
evaluates these individual incentives and disincentives forhorizontal cooperation.

3. Model Description
3.1. Autonomous Agents and Their Major Roles
This article proposes a stylized multi-agent model ofhorizontal cooperation based on the core functions of the
UNHRD system. The model represents horizontal rather
than vertical cooperation and has member organizations
cooperate in the same level of operations including
procurement, warehousing and shipping. The model iswritten and implemented using the agent-based modelinglanguage NetLogo 5.1.0 (Wilensky 1999).
The software agents included in the model are: i) a
humanitarian crisis agent that creates and communicatesst ocha st ic de ma nd s for rel ief it e m s i i) a me mbe rorganization agent that responds to stochastic relief
demands with a specific set of operational goals andconstraints; iii) a UNHRD warehouse agent that facilitates
the use of free storage, stock-swap, white stocks and iv) a
virtual observer agent, which ensures that the same random

The for m of horizontal inventor y management
adopted by the UNHRD system may be described as a‘service provider approach’ in which one organization
serves as logistics services provider for all members(Schultz and Blecken 2010). The World Food Program(WFP) acts as a service provider for the UNHRD system

39

J.Mochizuki, F.Toyasaki, I.F.Sigala

Table 2: Agents and their roles in horizontal cooperation

AgentsMajor roles

Humanitarian Crisis

Occurs at random geographic locations and creates stochastic
demands for relief items. Stores and communicates information suchas when the original demand occurs, when each relief shipment isreceived, what are the levels of remaining demands and whether
demands are met within specified lead-time targets.
Participates in horizontal organization and collectively seeks to fulfill
stochastic demands created by humanitarian crisis. Stores andcommunicates information such as the level of budget constraints,
the availability of stocks at each sourcing options, and their
willingness to swap stocks with other member organizations. Learns
from system experience and forecasts and make ex-ante sourcing
decisions based on the past success of each sourcing options.
Provides storage capacity and offers additional buffer stocks (known
as ‘white stocks’) in case member organizations run out of their own
relief items during the period of extremely high demand.
A virtual agent who pre-seeds random values and ensures that same
stochastic scenarios can be repeated.

Member Organization

UNHRD

Virtual Observer(Fig. 1). The model simulates the occurrence of multiple
humanitarian crises in which member organizations
collectively aim to deliver sufficient relief items withina specified window of lead-time. Based on the perceived
success of UNHRD system operation, budget constraints,
and forecasted demands, a member organization decides todecrease or increase relief items stored at the UNHRD andexperiments can be performed for reproducibility (Table 2).
A member orga n i zat ion may choose f rom thefollowing relief good sourcing options: i) own relief items
stored at a member’s own storage ii) own relief items
stored at the UNHRD storage, iii) relief items swappedwith other U NHRD members and iv) ‘white-stocks’(unbranded supplier’s stocks stored at the UNHRD system)

Shipment from own storage

Humanitarian
Crisis
Shipment from UN storage

UNHRD
Free storage
White stock

Free storage
White Stock

Swap
Shipment from own storage

Member Organization 1

Member Organization 2

Figure 1: Overview of the Proposed Model (Source: authors)

Figure 1: Overview of the Proposed Model (Source: authors)
40

Journal of Natural Disaster Science, Volume 36, Number 2, 2015, pp35-52

agents.3
Firstly, a humanitarian crisis agent is modeled as anautonomous agent, who specifies stochastic demands fora relief item with a given probability. It is assumed that acrisis agent determines its demand at the beginning of its
formation and exists in the simulation environment until
its demand is fully satisfied. In this article, all crises are
assumed to be rapid-onset events (in which members mustmeet acute demands within their lead-time target).

own facilities.
3.2. Behavioral Assumptions of Horizontal Cooperation.
Using the agent-based modeling framework, theUNHRD operation may be represented as a series of
information exchanges between software agents. Unlike
the existing multi-agent based modeling of commercial
logistics that incorporates explicit bidding processesof negotiations, shipment orders in the proposal modelare handled on a first-come first-served basis. Member
organizations handle simultaneously occurring demands
that are highly u npredictable in location, size andmagnitude. Figure 2 shows an illustrative sequence
diagram that shows the message flows between autonomous
UN Warehouse

Other member
agent(s)3 A sequence diagram is a common way to show objects
and message exchanges in the software development
literature. For those readers unfamiliar with the concept

Current member
agent

Crisis 1

Crisis 2

Check Demand

If still
insufficient,make a white
stock orderIf own item
not sufficient,make a swap
order

Swap delivery

Check Demand
White StockdeliveryIf remaining_demand = 0
completeIf own item
not sufficient,make a swap
order

At lead-timetarget,Check Delivery

At lead-timetarget,Check Delivery

Swap delivery

If remaining_demand = 0
Demand fully met

If remaining_demand > 0
Demand unmet

If remaining_demand = 0
complete

Figure 2: A sequence diagram illustrating the interactions of agents (Source: authors)
Note: At the beginning of the sequence diagram, the current member agent checks if any demands are created for the crisis
agent and makes a relief shipment arrangement. In the case that its own stock level is insufficient, the current member agent
sends a message to other member agent(s) requesting the use of stock swap (to be delivered at a later time). If demands
are still unmet, the current member agent sends a message to the UN warehouse, requesting the use of white stock (to bedelivered at a later time). At a particular lead-time target, the current member agent sends a message to the crisis agent
again, and checks if and to what extent demands have been met. These sequences are repeated for each member agent, as
and when new crisis demands are created. Please note that all member agents jointly aim to fulfill humanitarian demands,
therefore the model does not capture cases of conflict cooperation.

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J.Mochizuki, F.Toyasaki, I.F.Sigala

Rather than simply dividing the sum of past demands by
the number of days, Eq. (1) calculates a weighted average
whereby recent demands are emphasized over earlier
demands.

of a sequence diagram, see for example (B’Far, 2004).
Secondly, a member organization agent is modeled as
an autonomous agent, who interacts with other members
and the UNHRD warehouse agent to meet the demand
for a given crisis. In the base assumption a member
forecasts relief demands based on the experience of
past crises events. At the beginning of each time step(i.e. a day), a member organization agent communicateswith crisis agents to determine whether a new demandhas been generated. When a new demand is detected,
a member agent evaluates whether its own facility andthe UNHRD warehouse are closer to the newly createdcrisis location and places an order to ship items from thenearest facility. The quantity of demand, destination andtime of the delivery are then stored as a tuple to signal ashipment order. In such a case, if the available stock levelis insufficient to meet the demand, a member organization
solicits stock-swaps and use of white stocks at the UNHRD
facility. Appendix 2 provides a pseudo-code developed forthese operational steps.

Secondly, an ex-ante sourcing (i.e. prepositioning)decision is also determined based on how well eachsourcing option performed in the past. Performance ismeasured in terms of the proportion of items delivered
within a lead-time target (T*) from specific sourcing
options.
For each of the four options, a ratio of past delivery
success is calculated at the time of replenishment asfollows. T he model agai n assu mes t hat a member
organization has a fading memory.

3.2.1. Forecasting and ex-ante purchasing decisions of amember agent
Where
I n t h is st yl i zed exa mple, ea ch member ha s aunique set of initial variables that characterize its initialendowment and operational constraints including: budget
constraint, demand target (a percentage of disasterdemand), lead-time target, and willingness to swap
stocks. At every replenishment interval (T), a member
evaluates its experience of the UNHRD system recordingwhether and to what extent relief demands have been met
during the window of its lead-time target (T*). A member
organization is assumed to forecast a demand based on aweighted average of past demands and deliveries.

DS j,k.T : The extent of successful delivery from sourcing
option (k) at a time of replenishment (T) for member
organization (j).
DDkit:(T′ < T*) : Demands delivered from source option (k)for humanitarian crisis (i) that had delivery time (T’) below
the target lead-time (T*).
TDit:(T′ < T*) : Total demands delivered from all sources forhumanitarian crisis (i) that had delivery time (T’) below thetarget lead-time (T*).
ωt : a weighting factor

Forecasting decisions are modeled in two parts. First, forevery monthly replenishment period, daily demand isforecasted as a weighted average of past demands.

In addition, it is further assumed that a humanitarianorganization has two modes of budgeting behavior, namely:i) budget constrained and ii) budget unconstrained. In
the budget constrained mode, each member organization
makes purchase decisions based on its past experience
as described above, while also considering the level of
its monthly budget constraint. In the case that a purchasedecision made based on Eqs. (1) & (3) is above its budget
constraint, a member agent will adjust its purchasing
decisions by cancelling the order for the most expensive
category and searching for the next feasible alternative andrepeating this algorithm until the budget constraint is met.
In the case that a purchase decision made based on Eqs. (1)& (3) is below its monthly constraint, then the remaining
portion of the budget is carried over to the following
month.

FTDj,T : A forecasted total demand (FTD) for the next 30
day period determined at the time of replenishment (T;T=30) for member organization (j).
ID j,t,i : An initial demand for humanitarian crisis (i) for
member organization (j) recorded at time (t).
ωt : a weighting factor

In the budget unconstrained mode, ex-ante decisions
are made in the same manner as the budget containedmode, except that when a catastrophic humanitarian crisis
overwhelms its capacity, an organization is allowed toaccess swap items and white stocks beyond its original

T he weig ht i ng factor ω t i n t h is model ca n beinterpreted as a member organization having a ‘fading
memory’ (Norling et al., 2001) of past disaster demands.

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To demonstrate the utility of this modeling approach,this section implements an illust rative case of one
service provider, two member organizations and multiple
humanitarian crisis situations. While some variables usedin this illustrative example are selected based on empiricalobservations (e.g. crises demands for example closely
resemble empirical demands for relief goods reported innews releases from 2006-2014 as retrieved from the JICA
website (2006-2014) 4), most data values are chosen forpurely an illustrative purpose, and therefore should beinterpreted as such. Under the assumptions described inTable 3, we evaluate how the system behaves under twoalternative budgeting regimes, namely: i) flexible budget
regime (C1) and ii) budget constrained regime (C2).

budget constraint. This budgeting mode may be plausible
when additional donor funding can be expected in the caseof a catastrophic event, thereby an organization faces nonbinding budgets under high relief demands.
A l s o, t he U N H R D wa r ehou s e i s mo d ele d a s
an autonomous agent, which interacts with member
organizations to fulfill relief demands created by crisis
agents. In the following illustrative case, this article
assumes that only one central UNHRD warehouse exists
within the software environment, though this assumption
can also be relaxed by modifying the inventory schedulingsystem. Finally, there is also a virtual observer agent that
ensures the reproducibility of stochastic scenarios in the
proposed model.
4. An Illustrative Example

4 Available in Japanese at:
http://www.jica.go.jp/information/jdrt/2014/index.html

Table 3: List of baseline assumptions

Humanitarian Crisis
DemandStochastic demand with a gamma distribution( = 4, =0.16)

Member Organizations
Demand targets

Target lead-time
Initial level of own storage
Initial level of own item at UN
storage
Budget constrains
Willingness to swap stock

Cost for own storage use
Cost of UN storage use
Cost of Swap Use
Cost of White Stock Use
UNHRD Warehouse
White stock availability
Operation modes
Budgeting behaviors

Member 1
Attempts to satisfy 20%
of stochastic demandfor each disasters
5 days
5 units
10 units

Member 2
Attempts to satisfy
80 % of stochastic
demand for eachdisasters
5 days50 units
20 units$30/month$200/month
100% of own items
100% of own items
stored at the UN
stored at the UN
storagestorage$10/unit$5/unit$5/unit$5/unit1000 units/month
C1: Flexible Budget Regime
C2: Constrained Budget Regime

Source: authors

43

J.Mochizuki, F.Toyasaki, I.F.Sigala

4.1. Flexible budgeting regimeC1). The uses of stock-swap were on average 2.2 units for
Member 1 and 1.9 units for Member 2 (Fig. 4 C1). The usesof white stocks were on average 5.5 units for Member 1 and17.5 units for Member 2 (Fig. 5 C1).

Under the f lexible budget regime (termed C1), it
is assumed that a member organization may request for
further stock-swaps or white-stocks to meet the highdemands created by catastrophic humanitarian crises. Thisbudget flexibility allows for an individual member to meetits demand target even in the case of catastrophic disasterevents. During the 2-year simulation period, Member
Organization 1 and Member Organization 2 had 18 and 24days respectively in which success rates (as determined by
the proportion of relief items delivered within a lead-timetarget) dropped below 1. Under this regime, both Member 1
and Member 2 had the tendency to reduce their own stocks
stored both at their own facility and the UN facility (Fig. 3

Given there was no tight limitation of white stock
availability, the system tended toward the uses of white
stocks when acute spikes were observed. As a result,member organizations reduced storing their own items. At
the UN facility, for example, the level of own items stored
peaked at 12 units during days 90-120 for Member 1 (Fig.
3 C1 Member 1 UN storage) and at 35 units during days
90-120 for Member 2 (Fig. 3 C1 Member 2 UN storage).
For both members, the level of own items stored neverrecovered to their original levels. This is because

units
60
50
40
30
20
10
0
1

150

271

420

541

690

days of operation

C1 Member 1 UN Storage (own item)

C2 Member 1 UN Storage (own item)

C1 Member 2 UN Storage (own item)

C2 Member 2 UN Storage (own item)

Figure 3: Changes in members’ decision to store own items at the UNHRD warehouse
Figure 3
Source: simulation results by the authors

units
45
40
35
30
25
20
15
10
5
0
1

150

271

420

C1 Member 1 Own Storage (own item)
C1 Member 2 Own Storage (own item)

541

690

days of operation

C2 Member 1 Own Storage (own item)
C2 Member 2 Own Storage (own item)

Figure 4
Figure 4: Changes in members’ decision to store own items at own warehouses
Source: simulation results by the authors

44

Journal of Natural Disaster Science, Volume 36, Number 2, 2015, pp35-52

units
3.5
3
2.5
2
1.5
1
0.5
0
0

125

250

375

500

625

days of operation

C1 Member 1 Swap

C2 Member 1 Swap

C1 Member 2 Swap

C2 Member 2 Swap

5: The number of items swapped during the 5-year simulation periods
Figure Figure5
Source: simulation results by the authors
units
50
45
40
35
30
25
20
15
10
5
0
0

125

250

375

C1 Member 1 White Stock
C1 Member 2 White Stock

500

625

days of operation

C2 Member 1 White Stock
C2 Member 2 White Stock

FigureFigure
6 6: The number of white stocks used during the 5-year simulation periods
Source: simulation results by the authors
The modeling outcome will likely change, of course,when white stock availability is tightly constrained. In thiscase, direct trade-offs may be observed between white
stock availability, delivery success and member decisions
to store own items at both the UN and own facilities. Undera flexible budget, it is unlikely that a member organization
on its own volition will continue to store own items at the
UN storage and have adequate capacities to swap items in
the case of acute demand hikes. Quantitative measures (suchas minimum storage requirement at the UN storage) may
be effective to induce continued storage of own items insuch case.

sourcing decisions were strongly predicated on thepast successes of each sourcing option. As a member
organization experienced and learned that white stock
options were dependable in the case of catastrophic events,
there was a tendency for members to develop dependency
toward this reliable option. Such tendency did not
respond strongly to changes in the variance of underlying
distribution of humanitarian demand: when the varianceof distribution was doubled (Fig. A.1-2 in Appendix 3) orhalved (Fig. A.1-3 in Appendix3), member organizations
continued to store less of their own items in the UN storage
under the flexible budget regime.

45

J.Mochizuki, F.Toyasaki, I.F.Sigala

4.2. Budget constrained regime

as diverse members with a range of behavioral preferences
enter into the system. The service provider approachcurrently taken does not offer any explicit incentives or
penalties to influence member decision-making. In extreme
cases, therefore, pure free-riding is possible whereby anorganization does not store any items at the UN facility
while still availing itself of the buffer capacities offered byhorizontal cooperation.

Under the constrained budget regime, a different
system behavior was observed. Assuming that a member
organization may not request any additional stock-swaps
or white-stocks in the case of catastrophic humanitarian
events, success rates drop as a result. Under tight budget
con st r ai nt s, Member O rga n i zat ion 1 a nd Member
Organization 2 experienced 24 days and 22 days in which
success rates dropped below 1. Ironically, the member
organizations’ inability to access stock-swaps and white
stocks in catastrophic cases ensured that they continued
to store own items at both UN and own facilities (Fig. 3
C2). The levels of own items stored at the UN facilities
were recorded on average at 12.3 units and 26.2 units for
Member 1 and Member 2, respectively. The uses of stockswaps on average were recorded at 0 units for Member 1
and 1 unit for Member 2 (Fig. 4 C2). The uses of white
stock were on average 0 units for Member 1 and 3 units for
Member 2 (Fig. 5 C2).
5.

Currently, the model lacks sophistication in many
aspects such as budget behaviors, sourcing decisions
and entry and exist options. Further research is therefore
required to realistically model humanitarian cooperation.
Key research questions that remain include:• Inter-temporal budget constraints and seasonal
forecasting: The current budgeting rule simply assumed
that members must meet monthly budget limits and any
excess budgets were carried over to following months. It
did not, however, allow for the uses of future budgets tomeet current demands. In the case of highly stochastic
and varying demands, some organizations may decide
to serve current demands in the hope that they will
face lower demands in following periods. This type ofdecision rule may be plausible under seasonal demand
fluctuations (such as high relief demands during cyclone
and followed by low demands during non-cyclone
seasons). These types of inter-temporal flexibility should
be explored for more realistic model humanitarian
coordination.

Initial lessons drawn and for further research

The modeling results indicate the system behaved
sensitively to changes in few parameters and assumptions,highlighti ng the i mpor t ance of u nderst anding andincorporating individual decision heuristics of member
organizations. Given the diverse character istics ofhumanitarian organizations participating in horizontal
cooperation, it is highly plausible that the increasing
number of organizations with different purchasing, prepositioning and dispatching preferences will affect overallsystem behaviors.

• Sourcing preferences and alternative resource
allocation rules: The current sourcing rules simply
assumed that member organizations chose sourcing
options based on the past performance. While suchlearning behavior is plausible, some organizations may
employ other heuristics such as cost minimization andminimum storage levels. Further sophistication in termsof alternative sourcing decision rules should be exploredin the future.

Even with a simple set of assumptions and a few agent
interactions, this stylized model offers important insights
into the system behaviors of voluntary humanitariancooperation. Due to the highly stochastic nature of rapid
onset humanitarian crises, it is difficult for member
organizations to accurately forecast and pre-position relief
items that also meet their monthly budgets. When demands
were particularly high, therefore, organizations often
failed to meet their delivery target. Budget flexibility wasan important element that allowed member organizations
to meet their coverage targets under highly stochastic
demands, confirming the observations made by the existingstudies such as Toyasaki and Wakolbinger (2014).

• Stochasticity of lead time: In the interest of ease ininterpretation, the current model simply assumed thatthe lead-time of each sourcing option did not change due
to locations or delivery orders. This may be unrealistic
since the time it takes to deliver relief goods may affectfactors such as distances to crises, administrative
processes required at each location, and the number oforganizations that are making simultaneous shipment
orders. Further studies exploring these aspects will be
useful.

When we further assumed that member organizations’past success influenced their future sourcing decisions,the system began to exhibit undesirable dependency.
As more demands were met by buffering capacities,
organizations were discouraged from stocking their own
items. Under such arrangement, simultaneously meeting
goals of providing sufficient buffering capacities andserving highly stochastic demands within a short leadtime (of 24 to 48 hours) under the fully voluntary natureof common stock pooling becomes increasingly difficult• Entry and exist decisions: The cur rent model issimplistic in that no entry and exist decisions are
explicitly modeled (i.e. member organizations may
reduce stock levels at but never completely exit thesystem). This is unrealistic since member organizationsat any ti me may decide to exit, when conti nued

46

Journal of Natural Disaster Science, Volume 36, Number 2, 2015, pp35-52

demands. At the same time, it encourages member
organizations to store less of their own relief goods as aresult. The tendency was particularly notable under theflexible budgeting regime where additional funding became
available in the case of a catastrophic event. The model
therefore highlights the difficulty in meeting multiple
system goals such as providing suff icient buffering
capacities, meeting highly stochastic demands, and also
maintaining the fully voluntary nature of individual storage
decisions.

membership conflicts with their strategic and operational
goals. Likewise, it is reasonable to assume that a newmember may enter at any time, potentially alteringsystem performance. How their individual experience
as well as member linkages and reputations (Stephenson
Jr and Schnitzer, 2006) may inf luence horizontal
cooperation is an important area for further research.
• Detailed incorporation of additional costs including
consolidated transport and penalty for unused
stocks: The current model lacks many details regarding
inventory and operation costs including detailed costs
for storage, transportation (e.g. evaluation of individual
versus consolidated transportation under horizontal
cooperation as discussed in Gatignon A., et al. (2010)),and potential penalty costs for unused stocks.5 Further
studies incorporating empirical information in theseregards will be helpful.

While the model was kept simple to allow for clear
interpretation of results, further fine-tuning and evaluations
are certainly useful to explore aspects such as the impact ofinter-temporal budget constraints and alternative allocation
rules, stochasticity of lead time, entry and exist decisions,and the potential incorporation of multiple depot locations
and relief items.

REFERENCES• Multiple depot locations and relief items: While thisstylized case only considered one collective depot and
one relief item, the actual UNHRD operation is far more
complex where six depots located globally hold as muchas 400 types of relief items (UNHRD 2015). Althoughmodeling full complexity may be impractical, further
inclusion of some of these aspects such as additional
search costs that may arise from unstandardized relief
items (Toyasaki and Wakolbinger 2014) and undermultiple depot locations may be useful to answer key
policy and coordination questions.

Apte, A., 2010. Humanitarian Logistics: A New Field of
Research and Action. Now Publishers Inc.
Balcik, B., Beamon, B.M., 2008. Facility location inhumanitarian relief. Int. J. Logist. Res. Appl. 11, 101–121.
doi:10.1080/13675560701561789
Beamon, B.M., 1998. Supply chain design and analysis:models and methods. International Journal of Production
Economics, 55(3), pp. 281-94

5 While the UNHRD system does not specify such penalty
cost at the time of writing, member interviews indicatethat there has been discussion where organizations
may either be encouraged to remove unused stocks
that are stored for more than two years at the common
warehouse or to pay penalty fees.

Bea mon, B.M., Kotleba , S.A., 20 06. I nventor ymodeling for complex emergencies in humanitarianrelief operations. Int. J. Logist. Res. Appl. 9, 1–18.
doi:10.1080/13675560500453667
Beamon, B.M. B. Balcik, 2008. Performance measurementin humanitarian relief chains. International Journal of
Public Sector Management, 21(1), pp.4 – 25

6. Conclusion

B’Far, R., 2004. Mobile Computing Principles: Designingand Developing Mobile Applications with UML and XML.
Cambridge University Press.

This article proposed a multi-agent simulation modelof horizontal cooperation using the UNHRD system as an
example. A stylized model of one service provider, twomember organization and multiple humanitarian crises
was developed to evaluate four sourcing options, namely:i) own storage ii) UN storage for own items iii) stock-swap
and iv) white stock uses under two budgeting regimes of
fixed and flexible constraints.

Cachon, G., P. Zipkin, 1999. Competitive and cooperative
inventory policies in a two-stage supply chain. Management
Science, 45(7): 936-953.
Cachon, G. 2001. Stock wars: inventory competition ina two echelon supply chain. Operations Research. 49(5):658-674.

Unde r t he plau sible a ssu mpt ion t hat membe rorganizations choose sourcing options based on how
well each have performed in the past, the simulation
found that the additional buffer stock capacity offered byhorizontal cooperation (i.e. stock swaps and white stocks)
seems to induce system dependency. The buffer capacity
gives members more flexibility to meet highly stochastic

Cachon, G., Terwiesch, C., 2012. Matching Supply with
Demand: An Introduction to Operations Management.
McGraw-Hill Education.

47

J.Mochizuki, F.Toyasaki, I.F.Sigala

Lai, K.R., Kao, B.-R., 2009. A Negotiation-Based Approachto Supply Chain Planning and Scheduling Problems in a
Fully Distributed Environment, in: Chou, S.-Y., Trappey,A., Pokojski, J., Smith, S. (Eds.), Global Perspective forCompetitive Enterprise, Economy and Ecology, Advanced
Concurrent Engineering. Springer London, pp. 633–644.

Caunhye, A.M., Nie, X., Pokharel, S., 2012. Optimization
models in emergency logistics: A literature review.
Socioecon. Plann. Sci., Special Issue: Disaster Planning and
Logistics: Part 1 46, 4–13. doi:10.1016/j.seps.2011.04.004
Chen, F., A. Federgruen, Y. S. Zheng, 2001. Coordination
mechanisms for a distribution system with one supplier andmultiple retailers. Management Science, 47( 5): 693-708.

Lee, H., S. Whang, 1999. Decentralized multi-echelon
supply chains: Incentives and information. Management
Science, 45(5): 633-640.

Christou, I.T., 2011. Quantitative Methods in Supply Chain
Management: Models and Algorithms. Springer Science &Business Media.

Leiras , A., Irineu de Brito Jr , Eduardo Queiroz Peres, Tábata Rejane Bertazzo , Hugo Tsugunobu Yoshida
Yoshizaki , 2014. Literature review of humanitarian
logistics research: trends and challenges. Journal of
Humanitarian Logistics and Supply Chain Management
Vol. 4 Iss: 1, pp.95 – 130.

Christopher, M., Tatham, P., 2014. Humanitarian Logistics:
Meeting the Challenge of Preparing for and Responding toDisasters. Kogan Page Publishers.
Cruijssen, F., Dullaert, W., Fleuren, H., 2007. Horizontal
cooperation in transport and logistics: a literature review.
Transp. J. 22–39.

Norling, E., Sonenberg, L., Rönnquist, R., 2001. Enhancing
Multi-agent based Simulation with Human-like Decision
Making Strategies, in: Multi-Agent-Based Simulation.
Springer, pp. 214–228.

Duran, S., Gutierrez, M.A., Keskinocak, P., 2011. Prepositioning of emergency items for CARE International.
Interfaces 41, 223–237. doi:10.1287/inte.1100.0526

Ortuño, M.T., Cristóbal, P., Ferrer, J.M., Martin-Campo,
F.J., Muñoz, S., Tirado, G., Vitoriano, B., 2013. Decision
Aid Models and Systems for Humanitarian Logistics. A
survey, in: Decision Aid Models for Disaster Managementand Emergencies. Springer, pp. 17–44.

Fu, Y., Piplani, R., De Souza, R., Wu, J., 2000. Multi-agent
Enabled Modeling and Simulation towards Collaborative
Inventory Management in Supply Chains, in: Simulation
Conference, 2000. Proceedings. Winter. IEEE, pp. 1763–
1771.

Pujawan, I.N., Kurniati, N., Wessiani, N.A., 2009. Supply
chain management for Disaster Relief Operations:principles and case studies. Int. J. Logist. Syst. Manag. 5,
679–692.

Gatignon A., Van Wassenhove L.N., A. Charles, 2010.
The Yogyakarta Earthquake: Humanitarian relief through
IFRC's decentralized supply chain. International Journalof Production Economics, 126, pp 102-110.

Rafael, D., Joshua, B., Ange-Lionel, T., Bridget, G.,
ManWo, N., Francesco, L., Letizia, N., 2013. Humanitarian/Emergency Logistics Models: A State of the Art Overview,
in: Proceedings of the 2013 Summer Computer Simulation
Conference, SCSC ’13. Society for Modeling &#38;
Simulation International, Vista, CA, pp. 24:1–24:8.

G l o b a l H u m a n i t a r i a n A s s i s t a n c e (2 0 0 9) G l o b a l
Humanitarian Assistance Report 2009. Available at: http://
www.globalhumanitarianassistance.org/wp-content/
uploads/2009/07/GHA-Report-2009.pdf(2014) Global Humanitarian Assistance Report 2014.
Available at:
http://www.globalhumanitarianassistance.org/wp-content/
uploads/2014/09/GHA-Report-2014-interactive.pdf

Schulz, S., 2009. Disaster Relief Logistics. Benefits and
Impediments of Horizontal
Cooperation between Humanitarian Organizations. Haupt
Verlag, Bern.

Gösling, H., Gelder mann, J., 2014. A Framework toCompare OR Models for Humanitarian Logistics. Procedia
Eng., Humanitarian Technology: Science, Systems andGlobal Impact 2014, HumTech

Schulz F. S., A. Blecken, 2010. Horizontal cooperationin disaster relief logistics: benefits and impediments.
International Journal of Physical Distribution & Logistics
Management, 40 (8/9), pp.636 – 656.

Jiao, J., You, X., Kumar, A., 2006. An agent-based
framework for collaborative negotiation in the global
manufacturing supply chain network. Robot. Comput.Integr. Manuf. 22, 239–255. doi:10.1016/j.rcim.2005.04.003

Stephenson Jr, M., Schnitzer, M.H., 2006. Interorganizationalt r ust, bou ndar y span ning, and hu manitar ian relief
coordination. Int. J. Logist. Syst. Manag. 17, 211–233.

Kovács, G. K.M. Spens, 2007. Humanitarian logistics indisaster relief operations. International Journal of PhysicalDistribution & Logistics Management, 37(2), pp. 99-114.

48

J.Mochizuki, F.Toyasaki, I.F.Sigala(2013) 2013 Year in Review. Available at: http://www.
unhrd.org/docs/UNHRD_2013_Year_in_Review.pdf

Tayur, S., Ganeshan, R., Magazine, M. (Eds.), 1999.
Quantitative Models for Supply Chain Management,International Series in Operations Research & Management
Science. Springer US, Boston, MA.

UNHRD (2015) the UN Humanitarian Response Depot.
Available at: http://www.unhrd.org/

Thomas, A., L. Kopczak., 2005. From Logistics to Supply
Chain Management: The path Forward in the Humanitarian
Sector, White Paper, Fritz Institute, San Francisco, CA

Van Wassenhove LN., 2006. Blackett Memorial Lecture
Humanitarian aid logistics: supply chain management inhigh gear. Journal of the Operational Research Society, 57,
pp. 475-489.

Toyasaki, F., Wakolbinger, T., 2014. Impacts of earmarked
private donations for disaster fundraising. Ann. Oper. Res.
221, 427–447.

Wakolbinger, T., Toyasaki, F., Christopher, M., Tatham,P., 2011. Impacts of Funding Systems on Humanitarian
Operations, in: Humanitarian Logistics: Meeting the
Challenge of Preparing for and Responding to Disasters.
pp. 33–46.

U N [ Un it e d Nat ion s] 20 03. St r e ng t he n i ng of t hecoordination of emergency humanitarian assistance of theUnited Nations. A report of the Secretary-General at sixtyeighth session. available at :http://www.un.org/en/ecosoc/docs/adv2013/sg _reportadv_strengthening_coordination_of_%20humanitarian_assistance.pd
UNHRD [United Nations Humanitarian Response Depot]

Wilensky, U., 1999. NetLogo. http://ccl.northwestern.edu/
netlogo/. Center for Connected Learning and ComputerBased Modeling, Northwestern University, Evanston, IL.

APPENDIX
Appendix 1:
Table A.1:
List of Interviews conducted
Appendix 1: List of Interviews
conducted

Organizations
Cooperative for Assistanceand Relief Everywhere(CARE)
United Nation
Humanitarian Response
Depots (UNHRD)Countries Positions

Dates

US

Senior Manager Logistics andOperations Emergency

08.07.2014

Italy

Chief

22.07.2014

Japan

Deputy Director General

30.07.2014

Lutheran World Relief
(LWR)

US

Emergency Program Manager

04.11.2014

MercyCorps

US

Strategic Emergency ResponseTeam

04.11.2014

Norwegian Church Aid’s(NCA)

NorwayHead of Global Logistics

17.11.2014

World Vision

Australia

18.12.2014

ShelterBoX

UK

Global Lead - Emergency
logistics
Supply Chain Manager

Japan International
Cooperation Agency (JICA)

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18.12.2014

Journal of Natural Disaster Science, Volume 36, Number 2, 2015, pp35-52

Appendix 2: Pseudo-Code for Shipment Decision-Making by Member Agents
To calculate distance and make shipment
at each timestep, ask each member agent:[ask each crisis		
if (remaining demand > 0)		[determine if the location of the crisis is closer to UNHRD storage or own storage of the member			
if (UNHRD storage is closer and UNHRD stock is sufficient)			then schedule a delivery from the UNHRD facility that will satisfy all the demand and designate thedelivery as ‘UNHRD storage’						

if (UNHRD storage is closer but UNHRD stock is not sufficient)then schedule a delivery from the UNHRD facility first that will satisfy part of the demand (‘UNHRD
storage’), then schedule a delivery from your own facility (‘own storage’)

			
			

if (own storage is closer and own storage stock is sufficient)then schedule a delivery from own facility that will satisfy all the demand and designate the delivery
as ‘own storage’

			
			

if (own storage is closer but own storage stock is not sufficient)then schedule a delivery from own facility first that will satisfy part of the demand (‘own storage’),then schedule a delivery from the UNHRD facility (‘UNHRD storage’)]

		
		
end;

if demand is still unsatisfied at this stage schedule (remaining demand > 0) a delivery from unused sources(own/UN storage) to fulfill remaining demand]

To swap
if demand is still unsatisfied at this stage (remaining demand > 0)[then select other member with the largest remaining stock at the UNHRD facility for stock swap
			if (found and available swap stock is sufficient)			then schedule a delivery from UNHRD that will satisfy all the demand and designate it as (‘swap’)
			
			
			
end;

if (found but available swap stock is insufficient)then schedule a delivery from UNHRD that will satisfy part of the demand and designate thedelivery as ‘swap’]

to use white stock
if demand is still unsatisfied at this stage [then see if UNHRD has white stock available			if (white stock available is sufficient)			then schedule a delivery from UNHRD that will satisfy all the demand and designate the delivery as ‘white stock’				

if (white stock available is insufficient)then schedule a delivery from UNHRD that will satisfy part of the demand and designate the delivery as‘white stock’]end;
To schedule a delivery [member ID, Crisis ID, quantity of shipment, lead-time, origin of shipment]
create a tuple that describes a member organizations’ delivery order
allow delivery (list quantity of shipment, crisis ID, scheduled delivery time, origin of shipment, time of initial crisis,
end time for record)
add a new tuple at the end of their list of deliveries
decrement stocks levels at each sourcing option accordingly
end;

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J.Mochizuki, F.Toyasaki, I.F.Sigala

To process a delivery
at each timestep, ask each member agent to search through its list of deliveries to see if there are any orders that are ready [if (scheduled delivery time <= ticks) [		
ask each crisis agent to receive relief items by decrementing their remaining demand variable.
		
remove the current delivery from the list of deliveries entry from the		
add the delivery information to the list of recorded deliveries]
		

proceed to the next tuple]

end;

Appendix 3:
units
80
70
60
50
40
30
20
10
0
1

150

271

420

541

690

days of operation

C1 Member 1 UN Storage (own item)

C2 Member 1 UN Storage (own item)

C1 Member 2 UN Storage (own item)

C2 Member 2 UN Storage (own item)

Figure A.1: UN storage use from an alternative stochastic run with (alpha = 2, gamma 0.08)
Figure A.1
units
50
45
40
35
30
25
20
15
10
5
0
1

150

271

420

541

690

days of operation

C1 Member 1 Own Storage (own item)

C2 Member 1 Own Storage (own item)

C1 Member 2 Own Storage (own item)

C2 Member 2 Own Storage (own item)

Figure A.2: Own storage use from an alternative stochastic run with (alpha = 2, gamma 0.08)
Figure A.2

51

Journal of Natural Disaster Science, Volume 36, Number 2, 2015, pp35-52

units
60
50
40
30
20
10
0
1

150

271

420

541

690

days of operation

C1 Member 1 UN Storage (own item)

C2 Member 1 UN Storage (own item)

C1 Member 2 UN Storage (own item)

C2 Member 2 UN Storage (own item)

Figureananalternative
stochastic
runrunwithwith(alpha= 8, gamma
0.32)0.32)
Figure A.3
A.3:UN
UNstorage
storageuse
usefrom
fromalternative
stochastic(alpha= 8, gamma

units
50
45
40
35
30
25
20
15
10
5
0
1

151

271

421

541

691

days of operation

C1 Member 1 Own Storage (own item)

C2 Member 1 Own Storage (own item)

C1 Member 2 Own Storage (own item)

C2 Member 2 Own Storage (own item)

Figure A.3 Own storage use from an alternative stochastic run with (alpha = 8, gamma 0.32)
Figure A.4: Own storage use from an alternative stochastic run with (alpha = 8, gamma 0.32)

52