Learning How to Make Optimal Decisions in Design and Selection of Post-Disaster Shelters

Mahasti Tafahomi Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 1, (Part - 2) January 2016, pp.06-12
www.ijera.com 6|P a g e
Learning How to Make Optimal Decisions in Design and Selection
of Post-Disaster Shelters
*Mahasti Tafahomi
(Reseach departement, Shelterexpert .org The Netherlands)
ABSTRACT
Optimal choice of shelter solutions in post-disaster emergency situations depends on deep understanding of
needs, context of needs, up to date knowledge of the available solutions and smart strategies to connect the two
optimally.
Evidence of needs can be submitted both by the beneficiaries and relief specialists as NGO‟s and the UN. The
solutions can be entered by local or international suppliers. Post-disaster emergency shelter designers are as any
other designer concerned with the ecological, social, cultural and spatial application of technologies to meet
specific human needs after each disaster and in each location. The choice for an on-demand designed post-
disaster shelter contributes to optimal post-disaster shelter relief process and sustainable post-disaster emergency
shelters. The need for qualified engineers who can translate their technical know-how into tailored solutions is
eminent.
In this article we describe a research project that develops and tests a tool for design and selection of on-demand
post-disaster emergency shelters, a Decision Support System (DSS). The tool can be used by the beneficiaries,
relief specialists and politicians. The questions asked by the DSS for gathering evidence on needs and solutions,
can be adapted to each type of user. In addition to assisting relief specialists in decision making, the DSS can be
used as training tool for relief specialists in the field and educating engineers in their thinking.
Keywords: Design, disaster, needs, learning, post-disaster, innovation, Post-disaster shelter, technology,
research projects.
I. INTRODUCTION
The UN and relief organizations as the IFRC
acknowledge that adequate shelters are vital for
saving thousands of lives at risk. The available
budget is often decisive for the quality and quantity
of the available shelters. However the understanding
of local culture, knowledge of available solutions,
and the usage of the available technology, to access
information about the disaster, local climate and
conditions can contribute to more efficiency and
reduce costs.
Cases as Pakistan, Tsunami in Indonesia or the
recent Haiti post-disaster shelter aid where not rain
resistant shelters were provided shortly before the
rain season, demonstrate that decision making in post
disaster shelter aid does not lead to tailored shelter
solutions for each situation.
Relief organizations are mandated to provide
optimal aid after a disaster. Spite extreme efforts to
provide optimal shelter aid however, the shelters
often do not meet the requirements. Lack of proper
instruments, lack of professionalism, shortcomings of
relief specialists in decision making positions, in
terms of experience, training, insight or conflict of
interests are possible reasons for the ongoing failure
in providing optimal shelter aid.
Training a shelter relief specialist can cost up to
€7800, which is mostly not affordable for the
volunteers or the NGOs. The shortcomings as a result
of lack of training for example can partly be reduced
by using a proper tool as a decision making aid tool
that has access to the needed knowledge in the field,
a Decision Support System (DSS). This tool can
function as training tool as well.
II. HEADINGS
The headings and subheadings, starting with "1.
Introduction", appear in upper and lower case letters
and should be set in bold and aligned flush left. All
headings from the Introduction to
Acknowledgements are numbered sequentially using
1, 2, 3, etc. Subheadings are numbered 1.1, 1.2, etc. If
a subsection must be further divided, the 2
DECISION SUPPORT SYSTEM FOR ON-
DEMANS POST-DISASTER EMERGENCY
SHELTERS
The application of technologies to meet specific
human needs in case of post- disaster emergency
shelters, leads to finding the optimal solutions in as
short as possible time. The UNDRO (United Nations
Disaster Coordinator, now called OCHA) has been
offering post- disaster planning and relief
coordination since 1972. Although significant aspects
have been changed in the quality of post- disaster
emergency shelter aid since, the offered aid does not
RESEARCH ARTICLE OPEN ACCESS

meet the needs of the beneficiaries and the
requirements completely [1].
Post-disaster shelter relief specialists have to
deal with decision making under time pressure and in
a hectic and chaotic post- disaster environment and
are expected to think of various parameters
simultaneously.
In addition to human factors as altruism,
emotions and stress, the chance of human errors or
overseeing essential factors under high pressure of
time and in a hectic environment is more present
when optimal training is lacking.
Preparedness has been recognized as a key factor
by post-disaster relief specialists. Preparedness
includes educating engineers in their design thinking
and training the relief specialists who operate in the
field. A tool, a Decision Support System (DSS), that
does not feel the pressure and has the knowledge of
the needs, solutions and the rules to connect the two
can be of assistance to the relief specialists, can be of
assistance in the field and in training.
III. The Structure Of Decision Support
System Architecture
To formulate the characteristics for such a tool,
we develop and test a prototype DSS. The Decision
Support System consists of two essential parts:
Input: Collect variables in user screens; set up
data structures to store information about disaster; set
up data structures to store information about
Location; set up data structures to store information
about Situation; Set up data structures to store
information about Materials/Techniques; record
Shelter with variables information; set up admin
functionality to maintain data (user administration;
shelter data)
Output: Set up calculations; generate report
present conclusions.
System specifications, system structure and
system rules make the final decision making process
possible. Technical specifications for the DSS for
post- disaster emergency shelters are standard
specifications: ICT specialists, tailored physical
infrastructure (web browser, server, and engine),
database, data security and data storage.
The system is rule based and makes decisions
based on the disaster and location data. Behind the
scene, the disaster workflow will be activated.
To provide on-demand shelter advice, the DSS
needs, a logistics module, water and sanitation
module, a design module, energy module, waste
module and a rubble module can be implemented in
the DSS.
The performance based decision making in the
DSS results in the need for additional matching with
the current product standards as the sphere [6] and
shelter standard. The optimal situation is when
performance standards are applied by all decision
makers or a DSS that checks the compliance with
various standards.
The DSS asks a range of questions related to the
disaster, location and solutions, according to the
shelter workflow to provide a list of the most optimal
shelter/shelter items. Fig.1 is an example how the
workflow structures the steps that are taken by the
DSS. In this example, the logistics workflow, once
the disaster location is defined and the most suitable
shelters are identified, stock location and the package
size will be determined. This information will be
used by the DSS to define the optimal logistics for
the distribution of the shelter. The final step of this
process is to confirm the shelter and producing a final
report. The user can decide to end the process or add
more Auxiliary materials.
Fig.1
The DSS can be divided in three sections: Users,
data and rules. Rules and data are not visible to the
users. The DSS knows four levels of users:
Administrator, data manager, disaster manager and
shelter manager. The administrator has access to the
whole system, including the engine and the rules.
Data manager is responsible for data gathering in the
system, service calls and master data. Disaster
manager enters and the disaster data can be a
beneficiary, a relief specialist or a politician for
example. Shelter manager is the representative of a
manufacturer, can enter shelter data and has only
access to his/ her own shelter data.
Regarding data gathering; data storage and data
connectivity, data security is the key element in post-
disaster relief process. Relief organizations have
pointed out that certain data needs not to be saved or
archived due to safety measurements.
The strategy in decision making that combines
the demand side of the infrastructure to the solution
side, needs to have the capacity to cope with diversity
of supply, in a broad crosscutting environment.
System and data security are in addition to
infrastructure capacity primary requirements for an
optimal functioning of the DSS.
Two ways of defining the on-demand optimal
shelter can be followed: the optimal shelter regarding
absolute functionalities and performance and the
optimal shelter including extra parameters as

recycling; delivery time and local economy
parameters. The latter provides the overview that we
need for long-term effects of the shelter.
To meet the needs of the beneficiaries and
provide an integral on-demand shelter solution, a
logistics module, water and sanitation module, a
design module, an energy module, a waste module
and a rubble module are to be included in the DSS.
3.1 Data connection-Entity Relation Diagram
To create a prototype DSS, first we create an
Entity-Relationship Diagram (ERD), the blueprint
that explains motivation for the advices that the
decision support system suggests. This step is
essential in creating a functional knowledge
management system. Fig.2 illustrates an example of
the diagrams.
Fig. 2
The table “Location” refers to all geographic
information, the user can decide how detailed the
data need to be. The tables “Location Small Scale
Information” and “Large scale information” can be
used for implementation of GPS functions or other
links with usable data in the DSS. In case of location
data the data concern master data, available directly
at any time while using the DSS,.
The section “Disaster” refers to the information
regarding the disaster. The database distinguishes
between “natural” and “man- made” disasters. Relief
organizations participating in relief activities are in
most cases defined by the type of disaster: Man-made
disasters are mostly handled by the UNHCR as the
IFRC mostly is involved in natural disasters. In
addition the knowledge of the kind of disaster assists
the DSS to provide tailored advice concerning the
“After- Disaster-Effects”.
Data about the beneficiaries is gathered in the
table called “Disaster_Beneficiary”.
The solutions are structured in solution tables.
The shelters will be registered with all the relevant
technical specifications. On the level of shelter parts
and materials the material specifications is gathered.
Local (construction-) materials, shelter items are
available via table local materials.
The steps in the current DSS are as follows:
Step 1: Logging in, step 2: Entering data, step 3:
Selecting continent and disaster type, step 4:
Selecting location and disaster, step 5: Confirming
the selection, step 6: Entering disaster specifications,
step 7: Producing report, step 8: Selecting desired
solutions, Step 9: Shelter choice, step 10: Final
report.
3.2 Requirements for a DSS for post- disaster
shelter relief
The basic needs for a DSS are the correct engine,
working infrastructure and computational
environment including connection to the
infrastructure. When the correct engine is chosen, the
DSS needs data; structured information on the needs
and the solutions and a set of Decision criteria and
rules to decide by. Location data, disaster data and
solution data are the sources that are consulted by the
DSS. A profound maintenance of the system and the
accessibility of the DSS for the users are the key
elements for trust and acceptance of the DSS as a tool
by the users. This includes internet connection and a
reliable infrastructure. In this case, clear rules that
connect the needs and the solutions together and
performance standards are crucial for the DSS to
provide tailored, uniform on-demand post-disaster
shelter advice.
In this research project we develop and test a
prototype DSS.
IV. TESTING THE PROTOTYPE DSS
To learn from the developed prototype DSS, we
test the tool and formulate the characteristics for the
DSS that can bring the needs and the solutions in
post-disaster shelter aid optimally together.
In the test, in a role-play, three groups of
students provide shelter advice for the selected test
locations. We choose to perform the test with
students as test panel, as although students are no
authority in the field of shelter relief, they are more
objective in this matter. Relief specialists would be
testing their own previous performances or their own
way of operating versus a tool.
As students have no interests in the field of post-
disaster shelter relief or have to consider the
consequences of their answers, they will provide
uninfluenced, neutral results.
Twelve students in three groups of four, each
with a specific role, test the DSS for three locations.
Two groups have one extra member as beneficiaries.
These students tested the DSS within their study
program. Each location is provided with shelter aid
by two groups of test panel members. One group has
access to the DSS and the second group will provide
shelter aid as currently the shelter specialists do. In

this test we will refer to the decision making without
using the DSS as “the paper-run”. Both groups have
access to the same disaster- and location parameters
and both can choose the optimal shelter solution from
a list of forty five shelters, specially formulated for
this test. By comparing the results, we can discover
the characteristics of a DSS that leads to a tailored,
uniform and optimal shelter choice in post- disaster
situations. With the aid of the test results we can
formulate the characteristics for a DSS for post-
disaster emergency shelter relief that can effectively
connect the needs for post-disaster emergency
shelters in a specific situation with the available
shelter solutions.
Each group provides shelter solutions for two
locations, one location with the aid of the DSS and
for one location, implementing the Paper-Run
method. This results in two shelter advices for each
location; one advice provided implementing the DSS
and one advice implementing the „Paper-run‟ method
(without the aid of the DSS). We compare the
provided advice and the needed time for decision
making and we investigate the findings of the panel
members.
Disaster data and shelter data are available in the
test documentation. The location data for paper-run is
available in the project documentation. The DSS test
panel has access to location data via the master data;
however they do not need to access this data when
using the DSS. For the test using the DSS, each
group of students fill in the location and disaster
parameters or fill in shelter/shelter item data and
consult the prototype DSS for one disaster.
The panel members have specific roles as actor
in post- disaster shelter relief. The roles are: Shelter
relief specialist, Local Mayor of the disaster location,
Minister of foreign affairs of a donor country and
Beneficiary. Each panel member has a public and a
hidden agenda.
The test panel members are monitored in their
communication and interaction with one another,
while performing the test. Each panel member has
specific goals defined by his/her role and the hidden
agenda that is to be realized. The panel members
need to discuss the requirements related to their role
with the other panel members in the group. One
shelter type is to be chosen by each group while each
group members individually try to realize their
hidden agendas.
In this test we provide pre-assembled shelters in
fictive designs that can be advised by the DSS.
4.1 Test criteria for a DSS for post- disaster
shelter relief
The first test is performed in a controlled
environment where the questions over the location
and disaster parameters are specifically defined for
the test. The location and kind of disaster are actual
cases where the disaster has occurred in the past. To
create a measurable test environment, the disaster
parameters including the amount of beneficiary is for
all the locations identical in the test. The shelters
(solutions) are specially created with each one
differing characteristic (shelters for the test) to create
a controlled test environment. The prototype DSS
will be tested on her contribution to uniformity in
decision-making. For the test performed by shelter
experts and non-expert individuals, the test panel
uses the DSS according to the available test manual.
As the current DSS is a prototype, the data on the
locations and shelters are limited.
We test the performance of the prototype DSS
for on-demand post-disaster shelter advice, according
to the criterion of providing tailored, uniform
solutions regardless external influences.
In the test, shelter choice, made by the prototype
DSS will be compared to the shelter choice, made
without the assistance of the DSS. We test if
arbitrarily and non-objective decision making can be
eliminated by the implementation of the DSS and
then we formulate the characteristics needed for a
DSS that can achieve this performance.
The test conditions include extreme factors that
can occur and can lead to less optimal solutions. The
parameters in the test panel members‟ hidden agenda
for example do not implement that per definition
these scenarios will indeed happen. These are
extreme test conditions.
Theoretically the DSS can be tested in any
location. Data on the ten locations are currently
available in the DSS. We test three locations, Haiti,
Japan and the United States of America. The disasters
are man -made and natural disasters.
Disaster locations are locations where previously
disasters have occurred. However disaster data and
shelter solution data are fictive and specifically
prepared for this test to create controlled measurable
results for objective comparison.
The advice that the test panel provide in each
case, will be compared one to another. The panel
members are provided with the information and
instructions. The test panel members will answer the
questions as formulated in the manual. Disaster data
are equal for all test panel members. The test panels
are:
1 Member of a donor government/minister
(Donor) | User level: Disaster manager
Aim: A: To provide as much shelter aid as
possible/high quality/fast; to meet the needs of the
beneficiaries; as he/she is accountable towards the
taxpayers: see to that the taxpayer‟s money has to be
spent on shelter products; to lose as little time as
possible for making the decisions (To make fast
decisions).
B: To make sure that his country‟s economy can
profit from this shelter relief; to bring at least 10% of

the shelter orders to his shelter manufacturers; to get
logistics realized by a his country‟s logistics
company; to convince the voters that their tax money
is spent optimally; to present his country to the world
as a nation that makes significant contributions to
relief activities.
2 Shelter technical advisor international relief
organization. | User level: Disaster manager
Aim: A: To provide as much shelter aid as
beneficiaries; to have an overview of the donated
items /the costs; to purchase as many shelters with
each euro he/she has.
B: To be the first organization that provides
shelters to the beneficiaries; to be on the news as
active and efficient organization that is doing all the
work; to buy the shelters from a friend; to get an
order for an innovative manufacturer that is trying to
use the innovative material XT in his shelters in order
to help him innovate further.
3 Local relief organizations | User level:
Disaster manager
A: To provide as much shelter aid as
beneficiaries; to find structural solutions for the
future disasters.
B: To get 10% of the donations.
4 Beneficiary User level: Disaster manager
Aim A: To secure shelter for himself and his
family fast; to survive.
B: To provide shelter for not-affected poor
family members.
In this test, the available time to choose a shelter
solution and produce a report is max 20 minutes. The
test panel with the DSS has access to the location
data via the master data, as the Paper-run group is
provided with documents including location data.
Location data are retrieved from Google- earth. Both
the DSS group and the paper-run group can choose to
investigate the location data on the internet. Both
groups have access to the list of 45 shelters. As the
shelter specifications are specially prepared for this
test, they are fictive products. Each shelter has one
parameter that is different from the other shelters. A
uniform and comprehendible data matrix is then
realized. We compare the provided advices. Finally
we gather the suggestions how the DSS can be
improved.
4.2 Test results
The test results are summarized in the table 7. As
the table shows, three members of group three did not
finish any part of the test, due to system failure (DSS)
and shortage of time (paper-run). Two groups had
two beneficiaries, to test the effect of presence and
democratic choices. The effect of extra panel
members on the outcome appeared not to be
significant. Location Fukushima was chosen as the
only location where the shelters were to be situated
inside. The shelters did not need to meet the local
climate conditions. The test panel group three
members, who did finish the test did make the error
of ignoring this fact in the paper- run, the DSS users
in all groups chose automatically for shelters suitable
for being places in covered areas. Human error was
prevented by the DSS.
Table1- test results
Test panel group one did not succeed to
complete the selection in the paper-run due to time
limitations. Regarding amount of shelters and the
total price, the group could not reach an agreement in
the available time. The differences in the hidden
agendas lead to discussions which was not the case
when the group used the DSS. The choice of the
shelter for location New Orleans was made based on
permanency (B shelter), however in New Orleans
permanent shelters were not required in this test. The
duration of shelter aid is three Q (9 months). Fast
delivery and price were disregarded by the group
members.
In case of New Orleans, in paper-run version, the
hidden agenda of the minister was crucial for
choosing a shelter fabricated in his country (B
shelter). The fastest shelter would have been the E
shelter located in Laplace USA.
As the beneficiaries claimed more shelters than
needed due to their hidden agenda the group could
not agree on the amount of shelters in the paper-run.
In the DSS version , the official amount of needed
shelters, was higher than defined in the test manual as
well.
In the DSS version the test panel group one,
choose the F shelter, located in Port Au prince Haiti
which is the fastest shelter as the prices were
equal, this is the optimal choice.
Test panel group two had a discussion during the
test with the DSS about the choice of the Z shelter
from Tokyo that would be the fastest shelter
according to the current DSS or the AO shelter that
had to provide shelter fast, however as the choice for
the cheapest or the fastest was possible, a number of
panel members did choose for the fastest shelter (the
assignment). This resulted in non-uniform advice
from the DSS. This step in the DSS leaves space for
discussion.

The discussions for the choice of the most
optimal shelter indicate that without the DSS there is
more opportunity for choosing non optimal solutions:
Test Panel Three (Paper-run) succeeded to convince
his group to buy shelter 945 (the B shelter) as he was
friends with the manufacturer. And in case of
Fukushima the paper run panel members ignored the
fact that the shelter was situated inside.
When using the DSS the discussion was possible
when the system capacity was not sufficient (system
crashed) and when the fastest and cheapest shelters
were to be selected manually in the last step.
The panel members of test group three could not
finish the DSS as the system failure caused by
infrastructure capacity limitations and the panel
members started to fill in the amount of needed
shelters manually at the last interface page.
Regarding data entry, the amount of beneficiary
that was to be filled in equally for all the situations,
was filled according to the hidden agendas of the test
panel members. As the panel members did not follow
the test manual structures when filling in the amount
of beneficiaries, to achieve their hidden goals the
amount of needed shelters vary. Data gathering in
case of disaster data depends on the information that
the users provide to the DSS in the current situation.
In the Paper-Run test, the panel members could
not choose a shelter in the limited time of the test. A
shelter choice was too complicated to perform in this
short time. The average time spend to discuss a
shelter choice in a group in this test was 7,75 minutes
for the DSS users and 14,23 minutes for the Paper-
Run group.
V. Charachteristics Of A Decision
Support System For On-Demand
Post-Disaster Shelters
The test results can be summarized in a set of
characteristics for a Decision Support System for
post- disaster emergency shelter aid. The DSS breaks
the complex variables in post- disaster emergency
shelters down into simple rules, and is able to
combine the results into complex decision advice that
is needed to provide a tailored solution for each
disaster. In order to provide optimal advice detailed
and sophisticated data gathering.
The DSS for post- disaster emergency shelters
has the ability to provide tailored and uniform on-
demand shelter advice by means of elimination of
arbitrary choice of shelter / shelter parts, involvement
of the users, and a comprehensive structure. The
characteristics of such a DSS can be summarized as:
- The DSS has access to the infrastructure,
technical capacity in the web browser, server, the
engine to process and store the needed data on
and provide data security ;
- The DSS has up to date knowledge of the needs
and/ or has the flexibility and the ability to
gather data on the needs fast (reliable
assessments).
- The DSS has up to date knowledge of the
solutions (local –traditional-innovative) / or has
the flexibility and the ability to gather data on the
solutions fast, via links and other engines
- The DSS is transparent, the prices and the stock
are available on line
- The DSS is accessible to all (the beneficiaries;
donors and relief specialists) and can be
expanded to the level of policymaking, medical
aid, and can provide advice in long-term shelter
and urban design activities.
- The DSS is reliable while being used by the
involved parties as relief specialists, the
beneficiaries and the manufacturers: objectivity,
system stability and data security.
- The DSS has the knowledge of the local and
international standards.
- The DSS is flexible in programming.
- The DSS is user-friendly.
- The DSS learns from the earlier events, provided
shelters/ evaluations.
- The DSS needs limited maintenance.
- The DSS provides a sector memory, has a record
of lessons learned from the past disasters, learns
from the earlier experiences and provides advice
based on this memory.
- The DSS has a cross- sector character
(interconnectivity) and has sections as energy,
waste, rubble, water, sanitation, nutrition,
medical advice.
- The DSS has access to inclusive, structured data.
VI. CONCLUSIONS AND
RECOMMANDATIONS
As the recent cases show [2], applying
innovative technology in post-disaster emergency
operations, including shelter aid, is already a fact.
SMS is for instance used broadly to provide
information for the beneficiaries or to locate the
missing persons. The task is to implement innovative
technologies in an appropriate way.
The various available artificial intelligent
systems and the applicability of each system in
different situations have been structured in many
publications [3]. In case of design tools, the necessity
of efficient communication among various disciplines
in collaborative (design)- teams has resulted in
development of tools as SCAFFOLD. The conclusion
of developing and testing this tool was that the
implementation of computational tools can contribute
to more efficiency in communication and more
transparency in the architectural design process [4].
In decision making (when choosing post-disaster
shelter items), as well as designing post-disaster
shelters, a more integral viewpoint and constantly
tuned data, technology and information until valid

and usable results are obtained, results in better
decisions or designs [5] In the specific case of post-
disaster emergency shelters, an integral approach
from clothing and sanitation to water, energy and
construction results in more tailored solutions, that
can save time, costs and as a result save more lives.
As for instance in cold climates providing
hats/winter-caps can result in less need for heating in
the shelter. The first step in shelter relief is to design
an integral combination of the available preferably
local solutions.
This research project can be summarized in a set
of characteristics for a Decision Support System for
post- disaster emergency shelter aid. The optimal
DSS for post- disaster emergency shelters has the
ability to provide uniform advice.
The test in this research project indicates that
users including relief specialists, the beneficiaries and
designers need to trust the DSS and to believe in
accuracy and reliability of the advice provided. In
addition to participation of the users in the creation of
the DSS, a DSS that will be used by the involved
parties needs a flexible framework and a reliable and
secure infrastructure.
An adaptive way of thinking by the relief
specialists is needed to provide on-demand post-
disaster shelters. The pipelines that are currently set
up and are seen as a routine are to be re-thinked. In
addition relief specialists may need time to get used
to a new approach. This can be seen as a reason why
this approach may have little chance of being
implemented as a Decision Support Tool. The tool as
a training tool may have a chance, as the DSS can
make a selection from the existing solutions.
A different approach for the DSS to be more
accepted can be to make a selection of existing
shelters based on relevance to the situation: ranking
in the needed specifications, the priorities of the users
as indicated in earlier steps, Fig. 3. From the most
suitable shelters the cheapest or the fastest options
can be selected. The cheapest and the fastest
solutions will be specified by the amount of time and
the difference in price. The user can choose the
desired balance between time saving and optimal
pricing using this information.
A set of rules has to be created in addition to the
current rules. The system has to check the percentage
of requirements that a shelter meets according to the
selection rules. If a shelter fits for 99%, and if no
solution fits for the full 100%, or if the shelter is
significantly cheaper than the one with the full 100%
then the shelter will appear in the solutions screen.
When the user places the cursor over the percentage,
the requirements that cannot be met are displayed.
The user can decide whether these are too important
to ignore or to accept the shortcomings of a certain
shelter. Figure 3 illustrates an example of how the
ranking system operates. As the system provides the
possibility to return to earlier steps, the user can
choose to return to step 8 and adjust the needed
shelter specifications or continue and follow the
advice provided by ranking.
Fig.3
In both cases of choosing from the existing
solutions or designing on-demand shelters, The DSS
needs the trust of the users in terms of data accuracy,
data security, securing objectivity and transparency
as well as infrastructure reliability and accessibility.
REFERENCES
[1] UNDRO. (1987), Shelter after disaster.
[2] ALNAP, CaLP and LMMS. (2012). A
review session on new technologies in the
aid sector will be jointly hosted by ALNAP,
CaLP and LMMS, London U.K, 29 March
2012.
[3] Negeitsky, M. (2004). Artificial
Intelligence; a guide to intelligent systems.
[4] Donker, P. (1999). SCAFFOLD Structuring
communicaton in the architectural forum for
one-line design.
[5] Lohman, F. A. B. (1999). The Effectiveness
of Management Information-A design
approach to contribute to organizational
control.
[6] Humanitarian charter and minimum standard
in humanitarian response. (2009).Sphere
handbook. numbers

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