Architecting flexible projects and systems using portfolios of real options a decision support framework and two case studies

i
DECISION SUPPORT FOR ARCHITECTING FLEXIBLE
PROJECTS AND SYSTEMS: AN EVOLUTIONARY
FRAMEWORK AND TWO CASE STUDIES

by
Stephen Xu ZHANG
(B. Eng., Nanyang Technological University)










A THESIS SUBMITTED
TO THE DIVISION OF ENGINEERING AND TECHNOLOGY
MANAGEMENT
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
AT THE
NATIONAL UNIVERSITY OF SINGAPORE
2010



ii

iii

ACKNOWLEDGEMENT
This dissertation as well as the research is not possible without the many people who
have helped me for years.
First, the gratitude belongs to my thesis adviser, prof. Vladan Babovic, who has provided
inspiration, direction, time, effort, feedback, catalyst, and support continuously throughout
the last four years. He knows what I need better than I do and provides more guidance than
I could expect.
Thanks also to prof. Hang for setting up the division of Engineering and Technology
Management and for his version and consistent effort, which helps and steers me through
the PhD.
Thanks are also due to the people and organizations that have provided funding support
for the research. The project from Defense Science and Technology Agency (DSTA) in
Singapore provided me with an opportunity to make explicit and to develop at some length
a real options approach that has been central to much of my research. Another project,
done in Singapore Delft Water Alliance (SDWA), permitted me to amend and expand the real
options approach through applying it to additional field in a water supply system. Not only
do I need to thank them for the generous provision of funding, but also the excellent
community for carrying out research projects.
In the course of carrying out the MDP case study, thanks to Maarten Keijzer for the
application of the multiple-objective evolutionary algorithms into the real options model. He
worked patiently with me to set up and make sense of the model immediately after long
flights from the Netherland. It went way beyond the call of duty. Thanks also to my other
partner in the project, Joost Buurman. It would be hard to imagine a more professional
partner.

iv
Thanks to Nguyen Tan Thai Hung at the National University of Singapore for the
development of the MATLAB codes for the water supply system case. His patience and
perseverance in working with me was admirable, and the case study would have taken much
longer, without working together with him.
Thanks are also extended to Joost, Rao Raghuraj, Sarah von Helfenstein, Onion, Yabin,
James, S. K. Ooi, Jianghang, Mark Fielding, and Mark Womersley for helping me in writing as
well as many valuable conversations. Without their help, this thesis would not have had any
chance of being completed by the same time frame at the same quality.
Finally, my gratitude goes out to my family. Without their love and support, especially
my mom and dad, I would have never made it to a Ph.D. It is unfortunate the thesis is
written in a language they know little about, yet it simply confirms again their unconditional
love and support on whatever I choose to do.




v
TABLE OF CONTENTS
ACKNOWLEDGEMENT III
TABLE OF CONTENTS V
SUMMARY IX
LIST OF TABLES XI
LIST OF FIGURES XIII
LIST OF ABBREVIATIONS XVII
1 INTRODUCTION 1
1.1

M

OTIVATION
1

1.1.1

Uncertainty and flexibility 1

1.1.2

Flexibility in the design of systems and projects: a casino example 3

1.1.3

Multiple threads of flexibility –their benefits and associated complexity 8

1.2

R
ESEARCH OPPORTUNITIES
9

1.2.1

Research gap 9

1.2.2

Research question 10

1.3

R
ESEARCH APPROACH
11

1.4

O
RGANIZATION OF THE THESIS
15

2 LITERATURE REVIEW 17
2.1

I
NTRODUCTION
17

2.2

D
ESIGN
18

2.3

U
NCERTAINTY
-A
KEY ISSUE IN PROJECT AND SYSTEM DESIGN

: 20

2.3.1

Means to reduce uncertainties 22

2.3.2

Means to cope with uncertainties 24

2.4

F
LEXIBILITY
27

2.4.1

Modularity 27

2.4.2

Balancing costs and benefits of flexibility 29

2.5

O
PTIONS AND REAL OPTIONS
–
FLEXIBILITY FORMALIZED

30

2.5.1

Options 30

2.5.2

Real options 32

2.6

R
EAL
O
PTIONS
V
ALUATION
35

2.6.1

Options Valuation 35

2.6.2

Real options valuation 40

2.7

D
ECISION ANALYSIS AND REAL OPTIONS FRAMEWORKS
48

2.7.1

Decisions 48

2.7.2

Decision analysis and real options 49

2.7.3

Real options frameworks 51

2.8

R
ESEARCH GAP ANALYSIS
54

2.8.1

Practical issues of real options related to system and projects design 54

2.8.2

How the current real options frameworks deals with those practical issues 56

2.8.3

The techniques used to develop the proposed framework 57

2.8.4

Evolutionary algorithm 57

2.9

S
UMMARY OF THE CHAPTER
62

3 PROPOSING A NEW REAL OPTIONS FRAMEWORK BY CONSOLIDATING AND
EXTENDING THE PREVAILING REAL OPTIONS PRACTICES BY A STEP OF OPTIMIZATION 63
3.1

T
HE PROPOSED REAL OPTIONS FRAMEWORK
63

3.2

S
TEP
1:

F
RAMING

65

3.2.1

Define system and its objective(s) 65



vi
3.2.2

Identify and quantify uncertainties 67

3.3

S
TEP
2:

D
ESIGN
71

3.3.1

Generate alternative designs 71

3.3.2

Identify and generate options 71

3.3.3

The complexity associated with multiple interacting real options, their costs, benefits
and exercising conditions 75

3.4

S
TEP
3:

C
HOICES
76

3.4.1

Simulation-based valuation methods 77

3.4.2

Issues with using multiple real options in project design 81

3.4.3

How the prevailing real options frameworks deal with the issues of multiple interacting
real options and varying exercising conditions 84

3.4.4

Issue of the representation of the value of real options from multiple perspectives 85

3.4.5

Specific methodological challenges to be further addressed 85

3.5

S
TEP
3
E
:

T
HE EXTENDED OPTIMIZATION STEP
86

3.5.1

Overview of the extended step 86

3.5.2

The mechanism of the proposed framework 87

3.5.3

The partitioning or determination of the exercising conditions of real options 95

3.5.4

Sensitivity Analysis 100

3.5.5

The focus on searching for better design solutions 100

3.6

T
HE PROPOSED FRAMEWORK SUPPORTS DECISION MAKERS
101

3.7

T
HEORETICAL EVALUATION OF THE FRAMEWORK
103

3.8

S
UMMARY
108

4 CASE EXAMPLE: MARITIME DOMAIN PROTECTION (MDP) SYSTEM IN MALACCA
STRAIT 111
4.1

I
NTRODUCTION
111

4.2

B
ACKGROUND OF
MDP

S
YSTEM
114

4.2.1

MDP system description 114

4.3

M
ODEL OF
MDP
SYSTEM
119

4.3.1

Uncertainties 120

4.3.2

Options of interest 122

4.3.3

Valuation of real options 123

4.3.4

Optimization by evolutionary algorithms 127

4.4

R
ESULTS
131

4.4.1

Project value of a fixed design using deterministic & Monte Carlo simulations 135

4.4.2

Project value based on the hand-picked real options approach 135

4.4.3

Project value based on the “EA. selecting real options without exercising rules”

approach 136

4.4.4

Project value based on the “EA. selecting real options together with exercising rules”
approach 138

4.4.5

Real option values in detail using a Value-At-Risk graph 140

4.4.6

Sensitivity of the value of the real option to the level of the uncertainty 141

4.5

S
UMMARY AND DISCUSSIONS
142

5 CASE EXAMPLE: SINGAPORE WATER SUPPLY MANAGEMENT SYSTEM 145
5.1

B
ACKGROUND
147

5.1.1

Problem statement and approach 147

5.2

S
YSTEM DESCRIPTION
148

5.3

A
PPLICATION OF THE REAL OPTIONS APPROACH
152

5.3.1

An overview of the real options model of the water supply system: 152

5.3.2

Objective measures 153

5.3.3

Uncertainties 155

5.3.4

Options of interest 161

5.3.5

Modeling of the water supply system without and with the real options 164

5.3.6

The optimization and selection of option exercising conditions 169

5.4

R
ESULTS
170



vii
5.4.1

Sensitivity analysis 173

5.4.2

Determination of option exercising conditions 180

5.4.3

Discussions 184

5.5

S
UMMARY
187

6 DISCUSSIONS AND CONCLUSIONS 189
6.1

A
FORMAL METHODOLOGICAL EVALUATION OF THE PROPOSED FRAMEWORK
189

6.2

D
ISCUSSIONS
193

6.3

R
ESEARCH QUESTIONS REVISITED AND CONTRIBUTIONS
196

6.4

L
IMITATIONS
199

6.4.1

Boundary of real options 199

6.4.2

The identification, nurturing and maintenance of real options 199

6.5

F
UTURE
W
ORK
201

6.5.1

The application of the framework 201

6.5.2

The improvement of framework 202

6.6

S
UMMARIZING STATEMENTS
204

REFERENCES 205



viii



ix
SUMMARY
The proper decision support to design flexible projects considering downstream
decisions under uncertainties is critical yet challenging. While flexibility provides important
leverage against uncertainty, decision making that involves the planning and exercising of
multiple elements of flexibility under uncertainty is complex, and due to the complexity,
organizations often fail in practice to follow a well-structured, accountable and reproducible
decision-making process for assessing and selecting flexible projects.
This study inherits the prevailing real options practices in framing downstream decisions
as real options and establishing the cause-and-effect relationships between flexibility and
project value under uncertainty. The study extends that with an optimization step to
develop a framework that can handle portfolios of interacting real options in a
combinational design space more effectively than humans alone can with bounded
rationality. The framework borrows and integrates simulation-based options valuation
methods, decision analysis techniques and evolutionary algorithms to search for flexible
solutions more effectively.
The resulting flexible solutions consist of an initial design and a portfolio of real options
with their exercising conditions to adapt according to the manners the future unfolds - in
contrast to the common real options practices which primarily aim to derive the option
value. Evaluated and exemplified through two real-life cases, the evolutionary framework
compares favorably with the traditional fixed design approach and delivers considerable
improvements over the prevailing real options practices. In the MDP case study, it is found

that designing multiple real options into the MDP system using the proposed framework can
increase the system value by 13% beyond that from the prevailing real options practices. In
the other case study, it is found that the developments and incorporations of innovative


x
water technologies for the water supply system of Singapore are the dominant solutions
from multiple perspectives.
The proposed decision support framework facilitates the exploration, analysis,
optimization, and selection of solutions effectively in a combinatorial solution space – the
essence of a design process- and allows the decision process regarding multiple threads of
interacting flexibility to be within the bounds of reasonable effort on the part of the decision
maker. Therefore, the framework can be used to search flexible designs with portfolios of
real options, free up design space, and permit human experts to focus on the more creative
process of generating design alternatives to harness more from flexibility.
It changes fundamentally the design process by improving the otherwise obscure
understanding of flexibility, systemizing the design process regarding flexibility in project
design, and facilitating the leverages from flexibility and the ex ante planning of downstream
decisions. This framework is especially helpful for large-scale innovation projects, which are
usually accompanied by huge technological and market uncertainty and complex
downstream decisions.


xi
LIST OF TABLES
T
ABLE
2-1

D

EFINITIONS AND CLASSIFICATIONS OF UNCERTAINTIES
-
ADAPTED FROM
D
E
L
AURENTIS AND
M
AVRIS
(2000) 20
T
ABLE
2-2

A
CHRONICLE OF THE DEVELOPMENT OF REAL OPTIONS VALUATION METHODS
41
T
ABLE
3-1

C
OMPARISONS OF SEVERAL DESIGN APPROACHES CONCERNING FLEXIBILITY BY DESIGN SPACE SETS
AND DESIGN VALUES
104
T
ABLE
4-1

F

ORMULATION OF THE
MDP
SYSTEM DESIGN PROBLEM BY DIFFERENT DESIGN APPROACHES
119
T
ABLE
4-2

I
NPUT PARAMETERS FOR THE REAL OPTIONS MODEL IN THE
MDP
APPLICATION
133
T
ABLE
4-3.

A
SUMMARY OF DESIGN APPROACHES AND THEIR RESULTING DESIGN SPACES
134
T
ABLE
4-4.

T
HE PROJECT VALUES BASED ON SEVERAL DIFFERENT DESIGN APPROACHES
134
T
ABLE
5-1

L
IST OF UNCERTAINTIES MODELLED BY
GBM
IN THE WATER SUPPLY SYSTEM OF
S
INGAPORE
159
T
ABLE
5-2

L
IST OF UNCERTAINTIES MODELLED BY SCENARIOS IN THE WATER SUPPLY SYSTEM OF
S
INGAPORE
160
T
ABLE
5-3

B
ENEFITS OF REAL OPTIONS IN TERM OF ITS IMPROVEMENTS ON THE WATER SUPPLY SYSTEM OF
S
INGAPORE
172
T
ABLE
5-4

T
HE EXPECTED OPTION VALUE OF DESALINATION
-
BY
-
REGASIFICATION WHEN IT IS EXERCISED
SHOULD ITS COST BE LOWER THAN A PREDEFINED MONETARY THRESHOLD
. 181
T
ABLE
5-5

T
HE EXPECTED OPTION VALUE OF DESALINATION
-
BY
-
REGASIFICATION WHEN IT IS EXERCISED
SHOULD ITS COST BE LOWER THAN A FRACTION OF THE COST OF
NEW
ATER OR DESALINATION BY
RO
181
T
ABLE
5-6

P
ARAMETERS FOUND BY
GA

IN A SET OF FUNCTIONS THAT DEFINE THE EXERCISING CONDITIONS OF
DESALINATION
-
BY
-
REGASIFICATION OVER
NEW
ATER
182
T
ABLE
5-7

P
ARAMETERS FOUND BY
GA
IN A SET OF FUNCTIONS THAT DEFINE THE EXERCISING CONDITIONS OF
DESALINATION
-
BY
-
REGASIFICATION OVER DESALINATION BY
RO 182


xii


xiii

LIST OF FIGURES
F
IGURE
1-1

A
REAL OPTION TO EXPAND AN OFFICE BUILDING IN DOWNTOWN
C
HICAGO
(G
UMA
2008).

T
HE
INITIAL DESIGN
(
A
)
CONTAINS A REAL OPTION TO EXPAND VERTICALLY DEPENDING ON THE DEMAND TO
THE BUILDING
(
B
) 3
F
IGURE
1-2

A
FLEXIBLE DESIGN WITH TWO OPTIONS TO EXPAND AND THEIR EXERCISING CONDITIONS

5
F
IGURE
1-3

T
HE TWO OPTIONS TO EXPAND PLUS THE OPTION TO CONTINUE AS
-
IS FORM THREE MUTUALLY
EXCLUSIVE EXERCISING REGIONS DEFINED BY THEIR EXERCISING CONDITIONS IN A DECISION NODE
7
F
IGURE
1-4

T
HE AREA IDENTIFIED FOR THIS RESEARCH IN TERM OF NUMBERS OF REAL OPTIONS AND THEIR
EXERCISING CONDITIONS
9
F
IGURE
1-5

C
ONSOLIDATING THE PREVAILING REAL OPTIONS FRAMEWORK AND EXTENDING IT TO DEVELOP
THE PROPOSED REAL OPTIONS FRAMEWORK
(
TO BE ELABORATED IN CHAPTER
3) 12
F

IGURE
2-1

M
APPING THE MULTIDISCIPLINARY LITERATURE ASSOCIATED WITH THE RESEARCH QUESTION OF
THIS STUDY
17
F
IGURE
2-2

T
HE GOAL OF DESIGN AND PEOPLE INVOLVED
–
FROM
R
OSS
(2006) 19
F
IGURE
2-3

C
HANGES IN COST
,
KNOWLEDGE
,
AND MANAGEMENT LEVERAGE OF A SYSTEM IN ITS LIFECYCLE
–
ADAPTED FROM

F
ABRYCKY AND
B
LANCHARD
(1991) 26
F
IGURE
2-4

C
OSTS AS A FUNCTION OF THE DEGREE OF FLEXIBILITY
–
ADAPTED FROM
F
ABRYCKY AND
B
LANCHARD
(1991)
AND
S
CHULZ ET AL
.

(2000) 30
F
IGURE
2-5

T
HE OPTION VALUE OF A CALL OPTION

31
F
IGURE
2-6
THE
8-
STEP REAL OPTIONS FRAMEWORK PROPOSED IN
M
UN
(2006),
WITH THE PERMISSION
FROM THE AUTHOR
52
F
IGURE
2-7

A
SIMPLE FLOWCHART OF AN EVOLUTIONARY ALGORITHM
59
F
IGURE
3-1

T
HE PROPOSED FRAMEWORK
-
CONSOLIDATING THE REAL OPTIONS PRACTICES BY THE
3-
STEP

PROCESS OF
S
IMON AND EXTENDING IT BY A STEP OF OPTIMIZATION
65
F
IGURE
3-2

O
PTIMIZATION BASED ON MULTIPLE OBJECTIVES YIELDS A
P
ARETO
F
RONT
66
F
IGURE
3-3

R
EPRESENTING REAL OPTIONS RELATED TO A WATER TECHNOLOGY IN AN INFLUENCE DIAGRAM
(
EACH CIRCLE WITHIN A STATE REPRESENTS THE OPTION TO CONTINUE IN THAT STATE
) 73
F
IGURE
3-4

F
IXED DESIGNS VS

.
FLEXIBLE DESIGNS ON PARETO GRAPHS
74
F
IGURE
3-5

V
A
R
GRAPH OF THE VALUE OF THE FIXED DESIGN AND THE FLEXIBLE DESIGN WITH REAL OPTIONS
OPTIMIZED BY
GA

–
FROM
Z
HANG AND
B
ABOVIC
(2010
A
) 80
F
IGURE
3-6

V
ALUE OF SYSTEMS UNDER UNCERTAINTIES
.

T
HE VALUE OF A FIXED SYSTEM IS DEPICTED IN GRAPH
(
A
)
WHEN THE REALIZATION OF DEMAND IS GENERALLY UPWARD AND IN GRAPH
(
C
)
WHEN THE
REALIZATION OF DEMAND IS GENERALLY DOWNWARD
.

G
RAPH
(
B
)
AND
(
D
)
DEPICT HOW FLEXIBLE
SYSTEMS WITH REAL OPTIONS COPE WITH THE CHANGES IN DEMAND IN STAGES
83
F
IGURE
3-7

T
HE FLOWCHART OF THE PROPOSED REAL OPTIONS FRAMEWORK INTEGRATING GENETIC
ALGORITHMS
,

M
ONTE
C
ARLO SIMULATIONS
,
AND DECISION TREE TECHNIQUES
87
F
IGURE
3-8

A
N EXAMPLE OF MULTIPLE OBJECTIVE OPTIMIZATIONS WHICH FORM A
P
ARETO FRONT
93
F
IGURE
3-9

A
DECISION TREE WITH OPTIONS TO EXPAND
,
CONTINUE
,

AND ABANDON
-
ADAPTED FROM
B
RANDAO AND
D
YER
(2005) 96
F
IGURE
3-10

(
A
)

A
SCHEMATIZATION OF THE EVOLUTION OF UNCERTAINTIES ALONG TIME
.

(
B
)

A

SCHEMATIZATION ON PARTITIONING THE DECISION SPACE
(
SIMULTANEOUSLY IDENTIFY THE
CONFIGURATIONS TO USE AND THEIR EXERCISING CONDITIONS

) 97
F
IGURE
3-11

T
RANSFORMATIONS OF A CLASSIfiCATION TREE TO A DECISION TREE
–
FROM
B
RYDON AND
G
EMINO
(2008) 99
F
IGURE
3-12

R
ELATIONSHIPS OF SETS OF DESIGN SPACE FROM DIFFERENT DESIGN APPROACHES CONCERNING
FLEXIBILITY
(A,

B,

C,

D,
AND
E) 106

F
IGURE
4-1.

P
IRACY AND TERRORISM INCIDENTS
.

S
OURCE
:

IMO

(2007). 112
F
IGURE
4-2

T
ANKER
“L
IMBURG
”
HIT BY AN TERRORISM ACT NEAR
Y
EMEN IN
2002
WHILE SHIPPING OIL TO
M

ALAYSIA FOR
P
ETRONAS
115
F
IGURE
4-3.

SEDP
FLOW DIAGRAM
.

S
OURCE
:

NPS

(2005). 116


xiv
F
IGURE
4-4.

MDP

O
VERARCHING

M
ODELING
P
LAN
.

S
OURCE
:

NPS

(2005).

T
HE
MDP

O
VERARCHING
M
ODELING
P
LAN PICTORIALLY SHOWS AN INTEGRATED MODEL THAT TRANSFORMS INPUTS FROM
INDIVIDUAL SYSTEM PERFORMANCE AND COST MODELS INTO THE DESIRED PERFORMANCE MEASURE
OUTPUTS
. 118
F
IGURE
4-5

B
RANCHING OF A TRINOMIAL DECISION TREE
123
F
IGURE
4-6

A
HANDPICKED REAL OPTIONS TRINOMIAL SCENARIO TREE OF
3
STAGES
(
R
2007
,
R
2010
,
AND R
2013

REPRESENT THE DEGREE OF TERRORISM RISK IN YEAR
2007,

2010,
AND
2013
RESPECTIVELY
) 124

F
IGURE
4-7

A
FLOWCHART OF THE VALUATION SUB
-
MODULE OF THE REAL OPTIONS MODEL IN THE
MDP

APPLICATION
126
F
IGURE
4-8

A
FLOWCHART OF THE REAL OPTIONS MODEL AS APPLIED IN THE
MDP
APPLICATION
128
F
IGURE
4-9

(
A
)

A

SCHEMATIZATION OF THE EVOLUTION OF UNCERTAINTIES ALONG TIME
,

(
B
)

A

SCHEMATIZATION ON PARTITIONING THE DECISION SPACE
(
IDENTIFYING CONFIGURATIONS TO USE AND
SETTING UP THE EXERCISING CONDITIONS
) 130
F
IGURE
4-10

A
N EXAMPLE OF REFINING VALUE BY A
M
ONTE
C
ARLO SIMULATION WITH A LARGER SAMPLE
131
F
IGURE
4-11

GA

SELECTING REAL OPTIONS IN A TRINOMIAL SCENARIO TREE
(
THE PARTS IN RED DENOTE
DECISION VARIABLES OPTIMIZED BY
GA

–
IN THIS CASE ALL THE SYSTEM CONFIGURATIONS IN THE
13

DECISION NODES IN THE
3
STAGES
) 137
F
IGURE
4-12

GA
SELECTING BOTH THE REAL OPTIONS AND THEIR EXERCISING CONDITIONS
(
PARTS IN RED
DENOTE DECISION VARIABLES OPTIMIZED BY
GA

–
IN THIS CASE THE CONFIGURATIONS AND THE DEGREE
OF RISK TO EXERCISE THE CONFIGURATIONS
) 139
F

IGURE
4-13

A

V
A
R
GRAPH OF THE SYSTEM VALUE OF THE FIXED DESIGN AND THE FLEXIBLE DESIGN WITH REAL
OPTIONS SELECTED BY
GA 141
F
IGURE
4-14

T
HE AVERAGE VALUE OF REAL OPTIONS WITH RESPECT TO THE UNCERTAINTY REGARDING THE
TERRORISM LEVEL
142
F
IGURE
5-1
THE WATER RESOURCE OPERATING STRATEGY THE USUAL WAY
(
A
)
AND THE WATER RESOURCE
OPERATING STRATEGY THE INTEGRATED WAY WITH FLEXIBILITY
(
B

) 148
F
IGURE
5-2

S
INGAPORE HAS THE
2
ND HIGHEST POPULATION DENSITY IN THE WORLD BUT ALSO HAS
14

RESERVOIRS IN THE CITY
-
STATE
(Y
OUNG
2008) 150
F
IGURE
5-3

T
HE CONTRIBUTIONS TO THE WATER SUPPLY SYSTEM BY THE FOUR TAPS AT THEIR MAXIMUM
CAPACITIES IN YEAR
2006

(H
UNG
2009) 150
F

IGURE
5-4

A
REAL OPTIONS MODEL OF THE WATER SUPPLY SYSTEM TAKING ACCOUNT OF UNCERTAINTIES
AND FLEXIBILITIES
152
F
IGURE
5-5

R
EAL OPTIONS RELATED TO A WATER TECHNOLOGY REPRESENTED IN AN INFLUENCE DIAGRAM
162
F
IGURE
5-6

T
HE EVOLUTION OF THE WATER SUPPLY SYSTEM ACCORDING TO THE EVOLUTION OF
UNCERTAINTIES
166
F
IGURE
5-7

T
HE FLOWCHART TO DETERMINE WHICH TAP TO ADJUST IN A
3-
TAP SYSTEM

167
F
IGURE
5-8.

T
HE FLOWCHART TO DETERMINE WHICH TAP TO ADJUST IN A
4-
TAP SYSTEM
168
F
IGURE
5-9

T
HE FLOWCHART TO DETERMINE WHICH TAP TO ADJUST IN A
4.5-
TAP SYSTEM
169
F
IGURE
5-10

T
HE SENSITIVITY OF THE EXPECTED FINANCIAL VALUE OF
NEW
ATER AND DESALINATION
-
BY
-

REGASIFICATION TOWARDS POLITICAL PARAMETERS
.

T
HE VALUE OF THE
NEW
ATER IS MARKED ON THE
LEFT VERTICAL AXES IN EACH OF THE
9
GRAPHS
,
WHILE THE VALUE OF THE DESALINATION
-
BY
-
REGASIFICATION IS MARKED ON THE RIGHT VERTICAL AXES IN EACH OF THE
9
GRAPHS
.

I
N ROW
1,
THE
HORIZONTAL AXIS REPRESENTS THE PROBABILITY OF
1961
AGREEMENT TO LAPSE
.

I

N ROW
2,
THE
HORIZONTAL AXIS REPRESENTS THE PROBABILITY OF
1962
AGREEMENT TO BE CANCELLED OR HALVED IN
CAPACITY
.

I
N ROW
3,
THE HORIZONTAL AXIS PRESENTS THE PREMIUM OF IMPORTING WATER
. 175
F
IGURE
5-11

T
HE SENSITIVITY OF THE POLITICAL RISK OF
NEW
ATER AND DESALINATION
-
BY
-
REGASIFICATION
TOWARDS POLITICAL PARAMETERS
.

T

HE VALUE OF THE
NEW
ATER IS MARKED ON THE LEFT VERTICAL
AXES IN EACH OF THE
9
GRAPHS
,
WHILE THE VALUE OF THE DESALINATION
-
BY
-
REGASIFICATION IS
MARKED ON THE RIGHT VERTICAL AXES IN EACH OF THE
9
GRAPHS
.

I
N ROW
1,
THE HORIZONTAL AXIS
REPRESENTS THE PROBABILITY OF
1961
AGREEMENT TO LAPSE
.

I
N ROW
2,
THE HORIZONTAL AXIS

xv
REPRESENTS THE PROBABILITY OF
1962
AGREEMENT TO BE CANCELLED OR HALVED IN CAPACITY
.

I
N
ROW
3,
THE HORIZONTAL AXIS PRESENTS THE PREMIUM OF IMPORTING WATER
. 177
F
IGURE
5-12

T
HE SENSITIVITY OF THE SOCIOECONOMIC RISK OF
NEW
ATER AND DESALINATION
-
BY
-
REGASIFICATION TOWARDS POLITICAL PARAMETERS
.

T
HE VALUE OF THE

NEW
ATER IS MARKED ON THE
LEFT VERTICAL AXES IN EACH OF THE
9
GRAPHS
,
WHILE THE VALUE OF THE DESALINATION
-
BY
-
REGASIFICATION IS MARKED ON THE RIGHT VERTICAL AXES IN EACH OF THE
9
GRAPHS
.

I
N ROW
1,
THE
HORIZONTAL AXIS REPRESENTS THE PROBABILITY OF
1961
AGREEMENT TO LAPSE
.

I
N ROW
2,
THE
HORIZONTAL AXIS REPRESENTS THE PROBABILITY OF
1962

AGREEMENT TO BE CANCELLED OR HALVED IN
CAPACITY
.

I
N ROW
3,
THE HORIZONTAL AXIS PRESENTS THE PREMIUM OF IMPORTING WATER
. 179
F
IGURE
5-13

T
HE OPTIMAL EXERCISING COST OF DESALINATION
-
BY
-
REGASIFICATION
,
BELOW WHICH
DESALINATION
-
BY
-
REGASIFICATION IS PREFERRED OVER
NEW
ATER IN CASE OF AN EXPANSION
183
F

IGURE
5-14

T
HE OPTIMAL EXERCISING COST OF DESALINATION
-
BY
-
REGASIFICATION
,
BELOW WHICH
DESALINATION
-
BY
-
REGASIFICATION IS PREFERRED OVER DESALINATION BY
RO
IN CASE OF AN EXPANSION
184





xvi


xvii
LIST OF ABBREVIATIONS
3-tap The three taps of supplying water by means of catchment, desalination, and

import before recycling was introduced
4-tap The four taps strategy of water supply in Singapore by means of catchment,
desalination, recycling, and import
4.5-tap The water supply system in Singapore if desalination by LNG regasification is
used in addition to catchment, desalination (by Reverse Osmosis), import,
and recycling
C3I Command and control, communications and intelligence (C3I)
DA Decision Analysis
DCF Discounted Cash Flow
DEEP Desalination Economic Evaluation Program
DSTA Defense Science and Technology Agency
EA Evolutionary algorithm
GA Genetic Algorithm
GBM Geometric Brownian Motion
GP Genetic Programming
IWRM Integrated Water Resource Management (IWRM)
LNG Liquefied Natural Gas
MAD Market Asset Disclaimer
MDP Maritime Domain Protection
NEWater The name for the recycled water in Singapore
NPS Naval Postgraduate School
NPV Net Present Value
PSR Punggol/Serangoon Reservoir


xviii

PUB Public Utility Board
PV Present Value
RO Reverse Osmosis

SDWA Singapore Delft Water Alliance
VaR Value at Risk
WMD Weapons of Mass Destruction



1
1 INTRODUCTION
“Randomness fills the world and our life with charms of the unknown and provides us with all
kinds of opportunities. … By ‘seizing the opportunity,’ we actually mean to snatch the
randomness.”
- Deyi Li and Yi Du in “Artificial Intelligence with Uncertainty”
1.1 Motivation
1.1.1 Uncertainty and flexibility
Managers of projects and systems tend to adopt a single scenario for the future, come
up with a fixed design, and compute a single performance measure for a project. Ex ante
design decisions are based on forecasts that are “always” wrong (Ascher 1978, de Neufville
2000). If reality departs in one manner or another from what was originally anticipated –
which is usually the case (Savage 2000), especially innovative projects, the fixed design ends
up in suboptimal configurations regardless of how sound it is designed technically.
Seikan Tunnel between Honshū and Hokkaidō and Iridium satellite telephone system
have given us classical examples (Takashima 2001, de Weck et al. 2004) of how technically-
superb innovation projects, failed under uncertainties. While the failures arise for many
reasons, they can be attributed to a large extent to the fact that the analysis either has not
incorporated risks and uncertainty into the process, or has failed to create plans to address
the issue of uncertainty in the design and management process.
To address uncertainty in projects, the conventional notion that risk is undesirable and
must therefore be minimized is inadequate (Trigeorgis 2005). What we can do when faced
with uncertainty is more than risk reduction, we can also incorporate flexibility so that
downstream value-maximizing decisions can be made to change the flexible design, to

reduce losses in case of downward risks and exploit upward opportunities arisen from


2
uncertainty. For instance, projects with flexibility properly incorporated can have their
subsystems or project modules changed to exploit later shifts in market or to make use of
new technologies to be developed in future.
Multiple sources of flexibility exist in the design and management of systems and
projects. Such flexibilities related to systems and projects are specifically known as “real
options” by an analogy with financial options which are purely contractual in monetary
terms (Myers 1984, Trigeorgis 1996, Copeland and Antikarov 2001). Both financial options
and real options are rights but not obligations to take certain actions at some point of time.
Real options are valuable, as they embody flexibilities to enable downstream
management decisions for increasing the values of the projects. Having real options would
always be advantageous − if they were free. Yet, incorporating flexibility into a project
involves costs, because it may involve extra capabilities, because it may involve making
investments in smaller stages and missing out on economies of scale, or because it may
cause delays and causes losses of potential benefits. The questions thus are: what is the
value of each of the different forms of flexibility that might be added to the project? And
which ones justify their costs? This is the essential task of real options analysis.
Real options analysis has been widespread, covering various types of real options, such
as the option to temporarily shut-down (Brennan and Schwartz 1985), option to continue or
discontinue a series of investments (Majd and Pindyck 1987), option to stage a plan (Alvarez
1999, Benaroch 2002), option to switch (Baldwin and Ruback 1986, Kulatilaka 1993) and
option to increase/decrease capacity (Chou et al. 2007). Real life projects often involve
combinations of such options.
Recently, emerging interest in applying real options analysis to evaluate and design
innovative projects and systems is observed (de Neufville 2003). Researchers at MIT studied
the design and management of the communication satellite systems and pointed out that if

3
they were prepared to deal with uncertainty with real options to be deployed in stages
according to the demands, the bankruptcies of those systems, such as Iridium and Global-
star, could have been avoided (de Weck et al. 2003). Zhao and Tseng (2003) studied the real
option of sizing the foundation of a parking garage so that additional floors can be added at
a later date if a large demand materializes. The value of the parking garage with the extra
sizing therefore includes not only its present value, but also the value associated with the
option to expand the floor, which is found to be significant. Guma (2008) did a similar study
for a real option to expand an office building (Figure 1-1).

Figure 1-1 A real option to expand an office building in downtown Chicago (Guma 2008). The
initial design (a) contains a real option to expand vertically depending on the demand to the
building (b)

1.1.2 Flexibility in the design of systems and projects: a casino example
Let’s use a simple and general case of building an infrastructural system with
flexibility/options to expand as an example to illustrate the concepts and issues of using real
options in real-world projects. Suppose the problem at hand is to build a new casino. A key
problem in designing the casino is that the number of customers the casino will attract in
future (i.e. the realized demand of the casino) is uncertain.
On one hand, a capacious casino may not have its cost recovered if the realized demand
is smaller, and on the other hand, a smaller compact casino will have to forgo the


4
opportunity to gain customers and profits in case the realized demand is bigger. One means
to deal with this dilemma is to develop a flexible design with real options. The flexible design
starts with a small building and strengthened footings and columns to enable possible future
expansions. In doing so, the owner of the casino acquires real options- rights but not

obligations- to expand.
The strengthened footings and columns involve extra costs, which are the costs of the
options to expand. Due to the extra option costs, the flexible design is less lean and more
expensive compared to a fixed design at a targeted level of capacity (as shown in Figure 1-2).
The yellow line represents a fixed design, and the solid blue line represents a flexible design
with options to expand. Both of the designs have a targeted capacity of C
0
. At C
0
, the flexible
design yields a lesser NPV than the fixed design does, and the difference in NPV is due to the
cost of real options. The NPV is assumed in this example to follow a simple function with
respect to the capacity in two segments: 1) the NPV is linearly proportional to the capacity
before the design reaches it full capacity, and 2) the NPV is capped and unchanged once the
design reaches its full capacity.
Each of the two dotted lines represents the flexible design having a real option exercised
(Figure 1-2). Practically, designers may conceive many real options, but at the moment, let’s
suppose there are only two real options to expand to two different levels of capacity and
analyze them. One real option is to expand the capacity from C
0
to C
1
, and the other option
is to expand the capacity from C
0
to C
2
(C
2
> C

1
). To further simplify the matter, no option is
available to expand the capacity from C
1
to C
2
.
So in principle if the realization of capacity is high, option A might be exercised, and if
the realization is very high, option B may be exercised. But exactly how high is considered
high and how high is very high? In another words, what are the conditions to exercise the
two real options?


5

Figure 1-2 A flexible design with two options to expand and their exercising conditions

Given that the decision makers have a right but not an obligation to expand the flexible
design, they will only choose to do so if it increases the project value. In this case, if the
decision makers only have option A, they will exercise it if the capacity is larger than E
01

(where the blue line of initial design and the dotted line of option A intersect) in Figure 1-2.
This changes the targeted capacity of the flexible design from C
0
to C
1
. Similarly, if the
decision makers only have option B, option B should be exercised when capacity reaches E
02

(where the blue line of initial design and the dotted line of option B intersect) to expand the
capacity from C
0
to C
2
.
However, if the designers have both option A and option B, it is better to exercise option
B if the realized demand is above E
012
, and it is it is better to use option A if the realized
demand falls between E
01
to E
012
(Figure 1-2). The exercising conditions when two options
are available are different from those when either of them is available individually. This
clearly shows that, where multiple real options are present, the exercising conditions of real
options cannot be set in isolation.
Even though the casino example is simplistic, it illustrates a few fundamental issues
about real options.
Demand
NPV of Casino
C
C
0
0
C
C
1

1
C
C
2
2
E
E
01
01
E
E
02
02
E
E
012
012
Fixed design
Initial flexible design
Design after expansion
Cost of options
Option A to
expand to C
1
Option B to
expand to C
2
Demand
NPV of Casino
C

C
0
0
C
C
1
1
C
C
2
2
E
E
01
01
E
E
02
02
E
E
012
012
Fixed design
Initial flexible design
Design after expansion
Cost of options
Option A to
expand to C
1

Option B to
expand to C
2


6
First, unlike a financial options, whose exercising conditions are straightforward (a
financial option is exercised if the price of underlying asset is more than the sum of its strike
price and option cost written on a contract), real options are exercised to increase the value
of the underlying asset –a real-life project, and their exercising conditions are not
contractual, but need to be defined so as to best increase the value of the underlying project.
In this case, even though there are only two real options -both being expansion options,
and many simplifying assumptions are made, it can be seen that there is optimality in setting
the exercising conditions of the real options. The exercising conditions should be set so that
the project value will be maximized. This is easy sometimes (as in this case where linear
convex functions are assumed), but more likely to be complicated (as in most real world
design cases which exhibit non-linearity and non-convexity) and deserves more careful
studies.
Second, the exercising conditions when multiple real options are present simultaneously
are different from the exercising conditions when each of them is present separately. One
reason for this is that the exercising regions, defined by the exercising conditions, could be
mutually exclusive -only one real option can be exercised in a scenario. In the casino
expansion example, the decision makers have in fact three options: two options to expand
and an option to continue as-is. Only one option will be exercised at any single scenario in a
decision mode (as illustrated in Figure 1-3). The exercising conditions of the three real
options partition all the scenarios (i.e. the realized demand in the casino example) into three
mutually exclusive exercising regions. Additionally, the combination of all the exercising
regions should be exhaustive. No scenarios should be left out without a decision (to remain
as-is is considered as a conscious decision).

7

Figure 1-3 The two options to expand plus the option to continue as-is form three mutually
exclusive exercising regions defined by their exercising conditions in a decision node

Mutual exclusivity and exhaustiveness are some of the most typical relationships formed
by the exercising conditions of real options. However the relationships of the options
exercising conditions are context dependent and could be highly intricate and convoluted.
For instance, real options can be sequential; e.g. the results of an R&D option may enable
the options to produce related products.
Real options and their exercising conditions stand or fall together. Exercising a wrong
real option and exercising a right option in a wrong condition are equally undesirable by the
same token. A flexible solution comprises of both the options and their exercising strategies,
and to maximize the design value, both parts of the solutions (the options and the exercising
conditions) must be analyzed and selected simultaneously to take account of the
interactions.
However, this poses a heavy load upon our planning and decision-making capability,
when the number of real options considered is increasingly large and the assumptions about
the real options interactions are less simplified. As real options models approximate the
reality of project design better, the web of real options and interactions becomes larger,
Time
Demand
Big
expansion
Small
expansion
Continue
A Decision
Node

Time
Demand
Big
expansion
Small
expansion
Continue
A Decision
Node