A Decision-Support System for Identifying the Best Contractual
Delivery Methods of Mega Infrastructure Developments
Moza T. Al Nahyan
1
, Yaser E. Hawas
2
, Mohammad S. Mohammad
3
and Basil Basheerudeen
3
1
Department of Managment, Abu Dhabi University, Abu Dhabi, U.A.E.
2
Department of Civil and Environmental Engineering, UAE University, Al Ain, U.A.E.
3
Roadway, Transportation, and Traffic Safety Research Center, UAE University, Al Ain, U.A.E.
Keywords: Decision Support System, Megaprojects, Delivery Methods, Fuzzy Logic Model.
Abstract: This article describes the Decision Support System (DSS) software for identifying the best contractual
delivery methods for megaprojects, based on the elements of risks, opportunities of investments and
project constraints. A fuzzy-based multi-criterion decision-making technique is used to develop the
DSS, to assist the client in the selection of the appropriate contractual delivery method. The system
accounts for the relative importance of the various stakeholders in the different project stages. The
system enables the client to depict his/her best choices (regarding project delivery methods and
stakeholder entities) that would likely provide the best environs for the project to succeed. With such
complicated system, the client can also investigate the specifics of the various project stages and study
the effects of enhancements or deficiencies of the stakeholder entities capabilities. The system was
developed and calibrated based on the results obtained from extensive surveys among key stakeholders
in the UAE.
1 INTRODUCTION
During the past years, there is an unprecedented
growth in the infrastructure development in the
Middle East, rendered by the looming mega capital
events such as Dubai Expo and Qatar FIFA World
Cup in 2020 and 2022 respectively. The fast pace
infrastructure expansion and lessen period for project
planning puts pressure on the client to make
appropriate decisions, specifically with the choice of
selection of the project delivery. It is well understood
that the proper selection of contractual delivery
method is a crucial factor in the project success
(Qiang et al., 2015) and is reliant on the owner’s
objectives, project requirements and project
performance objectives (Touran et al., 2009). In the
context of UAE construction industry, the inadequate
early planning and the slowness in client’s decision-
making process were identified amongst the
significant factors contributing to the construction
delays and affecting the project success (Faridi and
El-Sayegh, 2006). Based on the available project
information, the clients have inconclusive knowledge
of the influential factors, risks and constraints in the
early project planning and the decisions on the choice
of project delivery are built on the little understanding
of the possible project outcome. Each delivery
method is a specific systematic approach attempted
by the client with other stakeholder entities to design
the construction procedure comprehensively. This
includes the project scope definition, sequencing of
construction activities and engaging the
public/private entities for the successful completion
of the construction project (Khalil, 2002; Touran et
al., 2009; Chen et al., 2011; Al Nahyan, 2013). As per
the Construction Country Institute (CII), there are
three fundamental project delivery methods, which
includes the Design Bid Build (DBB), Design-Build
(DB) and Construction management at risk (CMR).
Later, Miller et al., (2000) established the additional
classes of delivery methods based on the source of
finance (Direct or Indirect) and the integration of
delivery (combined or segmented).
Although many practitioners adopt the most
common traditional delivery method – Design Bid
Build, no universally acknowledged project delivery
method suits for every construction project
requirement. Apart from the fundamental delivery
methods, the alternate methods are often overlooked
by the past researchers due to lack of familiarity and
Al Nahyan, M., Hawas, Y., Mohammad, M. and Basheerudeen, B.
A Decision-Support System for Identifying the Best Contractual Delivery Methods of Mega Infrastructure Developments.
DOI: 10.5220/0006694704070414
In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 407-414
ISBN: 978-989-758-298-1
Copyright
c
2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
407
their applicability in different sectors of the
construction industry. Some little research efforts
were devoted to the development of client’s advisory
or management systems for the large-scale
infrastructure projects. The primary aim of this
research is to bridge this research gap and deficiency
of tools to assist the client in making vital decisions
on how to execute the project. A front-end expert
system is developed to assist the client/owner in the
choice of selection of Contractual Delivery Method
(CDM) based on the evaluation of the multiple
criteria influencing the project delivery selection.
2 REVIEW OF EXISTING CDM
SELECTION MECHANISM
The appropriate selection of contractual delivery
method is fundamental to improving the performance
of infrastructure projects. Many researchers
addressed the effectiveness of the different delivery
methods, while some identified the suitable selection
criterion for specific project requirements and owner
priorities, whereas others attempted different
decision-making mechanism for the contractual
delivery selection. Gordon (1994) utilized flowcharts
to choose the best contracting method, which allows
the client to prioritize amongst the list of significant
factors provided without weights.
Konchar and Sanvido (1998) proposed specific
criteria (both quantitative and qualitative) to
investigate the effectiveness of DB, DBB, and CMR.
The effectiveness of conventional contractual
methods was investigated across the different project
objectives and owner preferences, (El Sayegh, 2008),
though it only highlighted the criterion set for the
client to rank their preferences. Skitmore (2001)
estimated the utility factors to set priority ranking
(multi-attribute technique) for different project
delivery alternatives, but fail to reflect the subjective
judgment of public/private entities involved.
Analytical Hierarchy Process (AHP) is a multi-
criterion decision-making mechanism applied to the
suitable selection of delivery method by many
practitioners (Mahdi and Alreshaid, 2005; Touran et
al., 2009). AHP requires massive dataset of
indicators, and inaccuracy arises with the imprecise
perception of the experts/professionals in the
industry. AHP is sensitive and can lead to varying
decisions on situations with a higher degree of
uncertainty (Kordi and Brandt, 2012). A summary of
the literature listing the selection mechanism of
contractual delivery methods is provided in Table 1.
3 RESEARCH APPROACH
The selection of contractual method is a complicated
decision-making process and substantially varies
with the project characteristics and the owner
objectives. Moreover, the uncertain nature and the
inherent complexities associated with the increasing
size of the infrastructure projects makes it even more
difficult for the owner in the decision-making
process. However, it is arduous for the client to obtain
information (quantitative or qualitative) on the
alternate delivery methods confining to the diverse
project requirements and owner needs. Moreover, the
project delivery selection is governed by multiple
factors constituting the project characteristics, owner
needs, preferences and risk factors. It is appealing to
adopt the Multi-Criteria Decision Making (MCDM)
approach to model the multi-dimensional and
complex interface of the factors governing the
Table 1: Literature listing of factors governing the project.
Author and Year
Contractual Delivery Selection Mechanism
(i) Khalil (2002) ,Mahdi & Alreshaid (2005)
:
Analytical Hierarchy Process & Pairwise Comparison
(ii) Mafakheri (2007)
:
Interval Analytical Hierarchy Process & Rough Set Theory
(iii) Alhamzi & McCaffer (2000) ,Touran et al. (2009)
:
Analytical Hierarchy Process (AHP) and Weighted Matrix
(iv) Ribeiro (2001), Yoon et al. (2016)
:
Case-based Reasoning (CBR) and Decision Tree
(v) Chen et.al (2011)
:
ANN and Data Envelopment Analysis Bound Variable
(vi) Kumaraswamy and Dissanayaka (2001)
:
Expert-based Advisory System
(vii) Ratnasabapathy and Rameezdeen (2007)
:
Multi-Attribute Utility Technique using Delphi Method
(viii) Oyetunji and Anderson (2006)
:
Multi-Attribute Rating Technique with Swing Weights
(ix) Chan (2007), Mostafavi and Karamouz (2010)
:
Fuzzy Set Theory- Relationship & Evaluation model
Fuzzy Multi-Attribute Decision Making: TOPSIS
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
408
selection of project delivery alternatives. Based on
the predefined criterion of multiple factors, a fuzzy-
based MCDM is used in the assessment of the
contractual delivery alternatives for the large-scale
infrastructure projects. The relative weights of the
multiple criteria are estimated for the project delivery
alternatives using the linguistic values represented by
the fuzzy numbers. An aggregate measure of weights
is evaluated for the suitability of delivery alternatives
depending on the client preference and project goals.
Also, to reflect the influence of managerial and
coordinated action of project stakeholders on the
decision process, the system requires a qualitative
judgment of weights of the project stakeholders
amongst the different project stages (Al Nahyan,
2013; Hawas and Al Nahyan, 2017). It is feasible to
adopt a human intuitive judgment process to capture
the vagueness in the selection procedure and hence a
fuzzy-based approach is adopted to characterize the
factors influencing the contractual delivery in mega
infrastructure projects. The fuzzy based modeling is
more appealing in the decision making of large scale
projects, characterized with complex interfaces and
higher uncertainties.
Based on the thorough review of the literature, the
critical factors influencing the selection process are
grouped into three categories; Risk, Constraints, and
Opportunities. The indicators of the listed categories
are assessed qualitatively using a questionnaire
survey amid the industry professionals and used to
validate the developed model structure and weight
factors. Based on the qualitative inputs of the system
users, the indicators of the risk elements,
opportunities and constraints are used collectively to
estimate a qualitative measure (L, M, H) for each
element separately. Such qualitative measures are
then compared with decision matrix, to rank the
alternative project delivery methods, where highest
index score refers to the best suitable method of
project delivery. The paper addresses the model
structure devised to aid the client/owner in the
selection of the most appropriate project delivery
methods.
4 MODEL STRUCTURE
The model software was developed in C#.net using an
Integrated Development Environment (IDE),
specifically the Microsoft Visual Studio®.NET. The
system uses a MySQL database to store the input,
output values, and the processed information. It is
compatible with Microsoft Windows Operating
System and needs installation of Microsoft.Net
framework 4.5 or higher to run the program. Besides,
it uses FuzzyTech® generated runtime files to
implement fuzzy logic.
As shown in Figure 1, the system evaluates
elements of risks, opportunities of investments and
constraints on delivery. Each category has elements
defined by a set of indicators. For example, in the risk
category, the Technical Risk is evaluated by the
indicators of technical competence of employees,
established technical feasibility study, work
breakdown structure, design quality and design
completion.
Constraints indicators
levels assessed by decision
maker
Institutional Constraints
Performance Constraints
Financial Constraints
Organizational Constraints
Risk indicators levels
assessed by decision maker
Technical Risk
Institutional Risk
Project Management Risk
Country's Economic Risk
Financial Risk
Opportunities indicators
levels assessed by decision
maker
Institutional Transparency
Governmental Policies
Return on Investments
Decision Support System
Figure 1: Elements and indicators of MCDM based DSS.
The developed Decision Support System (DSS)
ensures a toolkit to assist the client or owners in
evaluating the multiple factors based on
predetermined or flexible criteria to select the best
suitable contractual delivery method. The DSS has
three components: the input interface, the fuzzy rule-
based processing (granular) core, and the output
interface. The input and output components are
designed to provide the user with an interactive
Graphical User Interface (GUI). Hence, it allows the
users to interact easily through graphical icons and
visual indicators. The input space offers flexibility to
the end-users to define or prioritize the mega-project
attributes, essential in identifying the best delivery
methods.
The granular fuzzy core is designed using
FuzzyTech® software which is a runtime file used in
the fuzzy calculations. The linguistic values of the
indicators (as input by the user) are very low, low,
medium, high, and very high. Some indicators are
binary as Yes or No. Figure 2 shows a schematic
representation of the working process for the
Technical Risk element, where the user selects the
fuzzy input term from a combo box. Before passing
the values on to the granular core, the fuzzy user-
input terms are ‘defuzzified’ into numeric values by
the system. The system collects the defuzzified inputs
and processes them further through subsequent
fuzzification process, before firing the rule base
blocks. The system intermediately reports a numeric
value and corresponding fuzzy term for each element
A Decision-Support System for Identifying the Best Contractual Delivery Methods of Mega Infrastructure Developments
409
(e.g., Technical Risk). In the calculations, the
processing core relies on the built-in correlation
values and signs (positive or negative) defined for
every composition in the fuzzy rule-base. The
developed DSS model structure has three stages;
configuration, computation, and output and reports.
These stages are described in detail hereafter.
4.1 Stage 1 – Configuration
The configuration stage is the one where the user
inputs the essential information to the system. Once
the configuration is completed through a GUI
interface, the data entries are saved to a database
where they can be retrieved, or re-edited whenever
necessary for further calculations. Figure 3 shows the
schematic representation of the user interaction with
the system at the configuration stage. As discussed
earlier and as shown in Figure 2, the indicators’
values are processed through the fuzzy system and
outputs are stored in the database. The various input
modules for the end user are outlined hereafter.
4.1.1 Project Stages and Stakeholders
Each project has a clear set of stages with a distinct
set of activities (in each stage) that take the project
from the concept idea to its implementation. The
project activities in each stage are significant enough
to contribute to the overall success of the project. The
process of directing and controlling a typical mega-
project development from start to finish is divided
into 6 stages: Planning, Scoping, Design, Scheduling,
Tendering, & Construction. Besides, a stakeholder
can be an individual or entire organizations who can
affect or get affected by the project implementation
or outcome of a project. It does not matter whether
the project affects them negatively or positively. They
can be internal or external to the organization. Based
on previous reviews, the default stakeholders
considered in the system development are 1) Clients /
Sponsors, 2) Government Agencies, 3) Project
Managers, 4) Consultants, 5) Contractors. The system
allows the user to modify the various stakeholder
groups, edit, add or remove.
The stakeholder engagement varies amongst the
different stages of the project, and it is captured in the
decision mechanism by estimating their importance
levels as shown in Figure 4. Each stakeholder can
have different levels of importance in different
project phases. The system scales the importance
value from 0 to 9, where 0 and 9 represent no
importance and profoundly influential, respectively.
The default values of the relative significance of
specific stakeholders in the different stages of project
cycle are determined using surveys of expertise in
large-scale projects. The user, however, can edit and
adjust these importance levels.
4.1.2 Contractual Delivery Methods
The contractual delivery is a sequence or a process by
which a construction project is comprehensively
designed and constructed. It includes the project
initiation, scope definition, organization of designers,
constructors and various consultants, sequencing of
design and construction operations, execution of
design and construction, and closeout (Project
Delivery Systems for Construction, 2004). The model
covers all possible contractual delivery alternatives to
rank them based on the criteria provided by the user.
The user has the option to add more delivery methods
(if needed). The system accounts for Design-Bid-
Build (DBB), Design-Build (DB), Performance-
Based Maintenance Contracts (PBMC), Construction
Management at Risk (CMR), Design-Build with
Warranty (DBW), Design-Build-Operate-Maintain
(DBOM), Design-Build-Finance-Operate (DBFO),
Build-Own-Operate-Transfer (BOOT), Design-
Build-Finance-Operate-Maintain (DBFOM),
Alliance Contracting (AC), and Build-Own-Operate
(BOO) (Miller et al., 2000).
4.1.3 Decision Matrix
The default values of the Decision Matrix are
constructed based on thorough literature review and
data surveys. Each row in Figure 5 represents a
specific delivery method. The Low, Medium and
High column represent its corresponding values for
each delivery method based on the chosen element
from the combo box list. For instance, Figure 5
represents the mapping values of Technical Risk as
the chosen element. Further, Low, Medium, and High
show the suitability scores for specific delivery
methods. The entry of 1 indicates the suitability of the
delivery method (raw entity), and 0 indicates the non-
suitability. As shown in Figure 5, the conventional
Design-Bid-Build (DBB) method may be suitable for
projects where the technical risk is either low or
medium, but not suitable when technical risk is high.
4.1.4 Priority Weights and Indicators
The user can set a priority weight for each element
and group/category of elements (risks, opportunities,
and constraints) based on his knowledge and
understanding. Easy to use GUI interface facilitates
the user to set the ratio for the priority. For example,
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
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User
Technical
Competence of
employee
(Very High)
Established
Technical Feasibility
Study
(Yes)
Work Breakdown
Structure(WBS)
(Very High)
Design Quality
(Very High)
Level of Design
Completion
(High)
Competence
(0.9)
(MaxValue : 1)
TechFS
(1)
(MaxValue : 1)
WBS
(90)
(MaxValue : 100)
Design Quality
(90)
(MaxValue : 100)
Design Completion
(0.9)
(MaxValue : 1)
Competence
TechFS
WBS T_Risk_1
RB_Tech
Design Quality
Design Completion T_Risk_2
RB_Design
T_Risk_1
T_Risk_2 Technical Risk
RB_Overall_Tech_Risk
Overall Technical Risk
User input
System converts the fuzzy terms to numeric levels
based on the MaxValue for each indicator
Fuzzy Inference Engine Rule Block
Input values to fuzzy engine
Defuzzified output value from fuzzy engine
Figure 2: Estimation of technical risk from user input using fuzzy inference engine rule block.
Indicators
Stakeholders
Importance of Stakeholders
Phases
Delivery Methods
Decision Matrix
Priority Weightage
Fuzzy Inference Engine
User (from Client group)
User Input
Database
Figure 3: Configuration stage – user input and fuzzy calculation.
as shown in Figure 6, the priority weight ratio for
categories of Risks, Constraints, and Opportunities
are assigned as 2:1:1, where Risk takes 50% priority,
and Constraints and Opportunities take 25% each.
Similarly, the user preferences can be input to all the
categories and the corresponding elements identified
in the mulita criterion decision making.
The inputs for indicators require the domain
knowledge of the expert or decision maker. Each
element of risk has its indicators to identify its overall
risk value, which in turn becomes a part of the overall
project’s risk. The indicator values then passed onto
the fuzzy engine. The fuzzy rule-based inference
units are used to estimate the corresponding element
risks based on the input values of its indicators. The
fuzzy inference block is validated using the
qualitative judgment of experienced practitioners.
Figure 4: Configuration of the importance levels of
stakeholders involved in megaprojects.
A Decision-Support System for Identifying the Best Contractual Delivery Methods of Mega Infrastructure Developments
411
Figure 5: Configuration of decision matrix of every
contractual delivery alternative for technical risk element.
Figure 6: User-defined priority preferences for the
categories and elements.
For example, the Return On Investment (ROI)
element identified amongst the Opportunity category
determines the likeliness of private companies to
invest in public infrastructure projects. The ROI is
affected by two indicators; the capital asset and level
of profit. The ROI improves with the decrease in
capital assets and increases by level of profit and vice
versa. The users set their preferences using linguistic
terms provided in a drop-down menu.
4.2 Stage 2 – Computation
The system user/client provides inputs of importance
rating of stakeholders in different project stages,
priority weights of different categories such as Risk,
Constraints and Opportunities and their respective
elements and lastly, the qualitative judgement of the
identified indicators. A rule-based fuzzy multi-
criterion decision-making (MCDM) model is
recognized to derive the consolidated effects of the
multiple factors. The fuzzy rules are established with
the aid of expert knowledge and understanding of
distinct factors influencing the CDM selection.
Further, the fuzzy model estimates are integrated with
the decision matrix to derive an absolute index for
every delivery method (Al Nahyan et al., 2018). The
DSS normalize the absolute index (to a value between
0-1) and compares the values of different CDM,
where the CDM corresponding to highest index
defines the best suitable delivery method for the user
inputs. Figure 7 shows the schematic representation
of the computation process of the user-defined input
data. In addition, the client/user have the flexibility to
run the program considering all the delivery methods
and stakeholders or evaluate by choosing a specific
delivery method and stakeholder group/entities for
the specific project requirements. Based on the user
Figure 7: Working process at computation stage.
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
412
selection of potential stakeholder groups/entities,
different stakeholder entities combination are
generated.
4.3 Stage 3 – Output and Reports
At the end stage, the best suitable delivery methods
are documented in output report based on the
estimated index standardized values. The developed
system displays two reports as indicated in Figure 7.
It enables the decision maker more options for
making decisions by switching among the reports. As
shown in Figure 8, report type I enables the user to
identify the top recommended project delivery
methods and stakeholder entities that are likely to
achieve best project performance and success (ranked
by their standardized indices). Besides, Report type II
enables the user to optimize the selection of the best
stakeholder entities for a specific delivery method as
seen in Figure 9. For instance, for the DBB, who are
best stakeholder entities that are likely to achieve the
highest project success and suitability.
Figure 8: Report Type I Sample.
Figure 9: Report Type II Sample.
5 SUMMARY
A fuzzy-based multi-criterion decision-making
technique is used to develop the DSS, to assist the
client in the selection of the appropriate project
delivery method. The software helps to identify the
best and rank the project delivery alternatives based
on project requirements, stakeholders involved, and
potential elements of risks, investment opportunities,
and constraints. This application can be adapted
easily to preferences/priority requirements of the user
owing to the dynamic computational structure, and it
can be modified easily by the user using GUI
interface. The model structure reflects the intuitive
judgment of experienced construction industry
professionals, as the model is validated using the
qualitative information collected from different
stakeholder expertise. The input interfaces are easily
managed and necessarily does not require substantial
data inputs in the selection process.
All the elements of risks, opportunities and
constraints were identified throughout the literature
review. Also, extensive surveys with various
stakeholders (more than 150 surveys) of mega-
projects have assisted in identifying the system
elements as well as the indicators. Such surveys were
also used to calibrate the fuzzy relations (strength and
sign) between the indicators and their corresponding
element assessment.
The existing AHP models requires extensive data
inputs and fails to account the megaproject expertise
perspectives and the managerial influence of
stakeholders resulting in imprecise estimates
Notably, less efforts are witnessed in development of
client advisory system, particularly in the large scale
civil infrastructure construction. Currently, the DSS
model serves as a standalone application and further
enhancements can be recognized to operate the
system remotely The DSS offers the flexibility to
account for the user’s preferences of
adding/removing project delivery methods, elements
of risks, opportunities, and constraints, indicators,
weights, stakeholder groups, and entities.
Nonetheless, not to overload, the user with new inputs
each time he/she uses the system, default values are
stored for ease of retrieval and editing. Finally, the
system enables the client to depict his best choices
(regarding project delivery methods and stakeholder
entities) that would likely provide the best environs
for the project to succeed. With such complicated
system, the client can also investigate the specifics of
the various project stages and study the effects of
enhancements or deficiencies of the stakeholder
entities capabilities (as reflected by indicators).
A Decision-Support System for Identifying the Best Contractual Delivery Methods of Mega Infrastructure Developments
413
ACKNOWLEDGEMENTS
The authors would like to acknowledge the research
project funds (Grants # 31R064 and 21R018) by the
UAE University and the UAE Ministry of Higher
Education and Scientific Research.
REFERENCES
Alhazmi, T. and McCaffer, R., 2000. Project procurement
system selection model. Journal of Construction
Engineering and management, 126(3), pp.176-184.
Al Nahyan (2013) “Management of transportation
infrastructure projects in the United Arab Emirates”.
Ph.D. Dissertation, Monash University, Australia.
Al Nahyan, Hawas, Y.E., Raza, M., Al Jassmi, H., Maraqa,
M., Thonnamthodi, B., Mohammad, M.S., (2018) “A
Fuzzy-based Decision Support System for Ranking the
Delivery Methods of Mega Projects," International
Journal of Managing Projects in Business, Vol.11,
Issue 1 (In press).
Chan, C.T., 2007. Fuzzy procurement selection model for
construction projects. Construction Management and
Economics, 25(6), pp.611-618.
Chen, Y. Q. et al., 2011. ‘Project delivery system selection
of construction projects in China’, Expert Systems with
Applications. Elsevier Ltd, 38(5), pp. 5456–5462.
El-Sayegh, S.M., 2008. Evaluating the effectiveness of
project delivery methods. Journal of Construction
Management and Economics, 23(5), pp.457-465.
Faridi, A. S. and El-Sayegh, S. M., 2006. ‘Significant
factors causing delay in the UAE construction
industry’, Construction Management and Economics,
24(11), pp. 1167–1176.
Gordon, C.M., 1994. Choosing appropriate construction
contracting method. Journal of construction
engineering and management, 120(1), pp.196-210.
Hawas Y.E., Al-Nahyan M.T. (2017) A Fuzzy-Based
Approach to Estimate Management Processes Risks. In:
Meier A., Portmann E., Stoffel K., Terán L. (eds) The
Application of Fuzzy Logic for Managerial Decision
Making Processes. Fuzzy Management Methods.
Springer, Cham.
Jato-Espino, D., Castillo-Lopez, E., Rodriguez-Hernandez,
J. and Canteras-Jordana, J.C., 2014. A review of
application of multi-criteria decision making methods
in construction. Automation in Construction.
Khalil, M. I. Al, 2002. ‘Selecting the appropriate project
delivery method using AHP’, International Journal of
Project Management, 20(6), p. 464.
Konchar, B. M., Sanvido, V. and Members, A., 1998
‘Comparison of U. S. Project Delivery Systems’,
124(December), pp. 435–444.
Kordi, M., & Brandt, S. A., 2012. Effects of increasing
fuzziness on analytic hierarchy process for spatial
multicriteria decision analysis. Computers,
Environment and Urban Systems. 36(1), 43–53.
Kumaraswamy, M.M. and Dissanayaka, S.M., 2001.
Developing a decision support system for building
project procurement. Building and Environment, 36(3),
pp.337-349.
Loforte Ribeiro, F., 2001. Project delivery system selection:
a case-based reasoning framework. Logistics
Information Management, 14(5/6), pp.367-376.
Mafakheri, F., Dai, L., Slezak, D. and Nasiri, F., 2007.
Project delivery system selection under uncertainty:
Multicriteria multilevel decision aid model. Journal of
Management in Engineering, 23(4), pp.200-206.
Mahdi, I. M. and Alreshaid, K., 2005. ‘Decision support
system for selecting the proper project delivery method
using analytical hierarchy process (AHP)’,
International Journal of Project Management, 23(7),
pp. 564–572.
Miller, J.B., Garvin, M.J., Ibbs, C.W. and Mahoney, S.E.,
2000. Toward a new paradigm: Simultaneous use of
multiple project delivery methods. Journal of
Management in Engineering, 16(3), pp.58-67.
Mostafavi, A. and Karamouz, M., 2010. Selecting
appropriate project delivery system: Fuzzy approach
with risk analysis. Journal of Construction Engineering
and Management, 136(8), pp.923-930.
Oyetunji, A.A. and Anderson, S.D., 2006. Relative
effectiveness of project delivery and contract strategies.
Journal of Construction Engineering and Management,
132(1), pp.3-13.
Project Delivery Systems for Construction. Associated
General Contractors of America, Arlington, Va., 2004.
Qiang, M. et al., 2015. ‘Factors governing construction
project delivery selection: A content analysis’,
International Journal of Project Management. Elsevier
Ltd and Association for Project Management and the
International Project Management Association, 33(8),
pp. 1780–1794.
Ratnasabapathy, S. and Rameezdeen, R., 2010. A decision
support system for the selection of best procurement
system in construction. Built-Environment Sri Lanka,
7(2).
Touran, A. et al., 2009. ‘Decision Support System for
Selection of Project Delivery Method in Transit’,
Transportation Research Record: Journal of the
Transportation Research Board, 2111(1), pp. 148–157.
Yoon, Y., Jung, J. and Hyun, C., 2016. Decision-making
Support Systems Using Case-based Reasoning for
Construction Project Delivery Method Selection:
Focused on the Road Construction Projects in Korea.
The Open Civil Engineering Journal, 10(1).
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
414