This paper presents and applies a simulation-based methodology to assess the value of flexible decentralized engineering systems design (i.e., the ability to flexibly expand the capacity in multiple sites over time and space) under uncertainty. This work differs from others by analyzing explicitly the tradeoffs between economies of scale (EoS)—which favors designing large capacity upfront to reduce unit cost and accommodate high anticipated demand—and the time value of money—which favors deferring capacity investments to the future and deploying smaller modules to reduce unit cost. The study aims to identify the best strategies to design and deploy the capacity of complex engineered systems over time and improve their economic lifecycle performance in the face of uncertainty by exploiting the idea of flexibility. This study is illustrated using a waste-to-energy (WTE) system operated in Singapore. The results show that a decentralized design with the real option to expand the capacity in different locations and times improves the expected net present value (ENPV) by more than 30% under the condition of EoS  α  = 0.8 and discount rate λ   = 8%, as compared to a fixed centralized design. The results also indicate that a flexible decentralized design outperforms other rigid designs under certain circumstances since it not only reduces transportation costs but also takes advantage of flexibility, such as deferring investment and avoiding unnecessary capacity deployment. The modeling framework and results help designers and managers better compare centralized and decentralized design alternatives facing significant uncertainty. The proposed method helps them analyze the value of flexibility (VOF) in small-scale urban environments, while considering explicitly the tradeoffs between EoS and the time-value of money.

References

References
1.
de Neufville
,
R.
, and
Scholtes
,
S.
,
2011
,
Flexibility in Engineering Design
,
MIT Press
,
Cambridge, MA
.
2.
de Neufville
,
R.
,
2008
, “
Lecture Notes
,”
ESD.71: Engineering Systems Analysis for Design
,
Massachusetts Institute of Technology
,
Cambridge, MA
.
3.
Rapoza
,
K.
,
2014
, “
Real Estate Oversupply Becoming Bigger Problem for China
,” Forbes.com.
4.
Manne
,
A. S.
,
1967
,
Investments for Capacity Expansion: Size, Location, and Time-Phasing
,
MIT Press
.
5.
Ahmed
,
S.
,
2002
, “
Semiconductor Tool Planning Via Multi-Stage Stochastic Programming
,”
International Conference on Modeling and Analysis in Semiconductor Manufacturing
, pp.
153
157
.
6.
Martínez-Costa
,
C.
,
Mas-Machuca
,
M.
,
Benedito
,
E.
, and
Corominas
,
A.
,
2014
, “
A Review of Mathematical Programming Models for Strategic Capacity Planning in Manufacturing
,”
Int. J. Prod. Econ.
,
153
, pp.
66
85
.
7.
Trigeorgis
,
L.
,
1996
,
Real Options: Managerial Flexibility and Strategy in Resource Allocation
,
MIT Press
.
8.
Cardin
,
M.-A.
,
2014
, “
Enabling Flexibility in Engineering Systems: A Taxonomy of Procedures and a Design Framework
,”
ASME J. Mech. Des.
,
136
(
1
), p.
011005
.
9.
de Neufville
,
R.
,
Scholtes
,
S.
, and
Wang
,
T.
,
2006
, “
Real Options by Spreadsheet: Parking Garage Case Example
,”
J. Infrastruct. Syst.
,
12
(
3
), pp.
107
111
.
10.
Cardin
,
M.-A.
,
Ranjbar Bourani
,
M.
, and
de Neufville
,
R.
,
2015
, “
Improving the Lifecycle Performance of Engineering Projects With Flexible Strategies: Example of On-Shore LNG Production Design
,”
Syst. Eng.
,
18
(
3
), pp.
253
268
.
11.
de Neufville
,
R.
,
2000
, “
Dynamic Strategic Planning for Technology Policy
,”
Int. J. Technol. Manage.
,
19
(
3
), pp.
225
245
.
12.
Hreinsson
,
E. B.
,
2000
, “
Economies of Scale and Optimal Selection of Hydroelectric Projects
,”
International Conference on Electric Utility Deregulation and Restructuring and Power Technologies
(
DRPT 2004
), London, Apr. 4–7, pp.
284
289
.
13.
Luss
,
H.
,
1982
, “
Operations Research and Capacity Expansion Problems: A Survey
,”
Oper. Res.
,
30
(
5
), pp.
907
947
.
14.
Li
,
S.
, and
Tirupati
,
D.
,
1994
, “
Dynamic Capacity Expansion Problem With Multiple Products: Technology Selection and Timing of Capacity Additions
,”
Oper. Res.
,
42
(
5
), pp.
958
976
.
15.
Ryan
,
S. M.
,
2004
, “
Capacity Expansion for Random Exponential Demand Growth With Lead Times
,”
Manage. Sci.
,
50
(
6
), pp.
740
748
.
16.
Geng
,
N.
,
Jiang
,
Z.
, and
Chen
,
F.
,
2009
, “
Stochastic Programming Based Capacity Planning for Semiconductor Wafer Fab With Uncertain Demand and Capacity
,”
Eur. J. Oper. Res.
,
198
(
3
), pp.
899
908
.
17.
Mittal
,
G.
,
2004
, “
Real Options Approach to Capacity Planning Under Uncertainty
,”
Master's thesis, Massachusetts Institute of Technology
,
Cambridge, MA
.
18.
de Neufville
,
R.
,
Scholtes
,
S.
, and
Wang
,
T.
,
2006
, “
Valuing Options by Spreadsheet: Parking Garage Case Example
,”
ASCE J. Infrast. Syst.
,
12
(
2
), pp.
107
111
.
19.
Dixit
,
A. K.
,
1994
,
Investment Under Uncertainty
,
Princeton University Press
,
Princeton, NJ
.
20.
National Environmental Agency
,
2013
, “
Solid Waste Management
,” http://www.nea.gov.sg/energy-waste/waste-management/refuse-disposal-facility
21.
RIS International Ltd.
,
2005
,
Feasibility of Generating Green Power Through Anaerobic Digestion of Garden Refuse From the Sacramento Area
,
RIS International Ltd.
23.
National Environment Agency
,
2011
, “
Environmental Protection Division Report
,” http://www.nea.gov.sg/docs/default-source/annual-reports/epd-2011.pdf?sfvrsn=2
24.
IUT Global Pte Ltd.
,
2006
, “
9.5 MW Food Waste Based Grid Connected Power Project Implemented by IUT Singapore Pte Ltd
,”
Clean Development Mechanism
,
IUT Global Pte. Ltd.
,
Singapore
.
26.
Hu
,
J.
, and
Cardin
,
M.-A.
,
2015
, “
Generating Flexibility in the Design of Engineering Systems to Enable Better Sustainability and Lifecycle Performance
,”
Res. Eng. Des.
,
26
(
2
), pp.
121
143
.
27.
Bai
,
R.
, and
Sutanto
,
M.
,
2002
, “
The Practice and Challenges of Solid Waste Management in Singapore
,”
Waste Manage.
,
22
(
5
), pp.
557
567
.
28.
Evangelisti
,
S.
,
Lettieri
,
P.
,
Borello
,
D.
, and
Clift
,
R.
,
2014
, “
Life Cycle Assessment of Energy From Waste Via Anaerobic Digestion: A UK Case Study
,”
Waste Manage.
,
34
(
1
), pp.
226
237
.
29.
Lim
,
R.
, and
Ng
,
K.
,
2011
, “
Recycling Firm IUT Global Being Wound Up
,”
The Business Times
,
Marshall Cavendish Business Information
,
Singapore
.
30.
Mikaelian
,
T.
,
Rhodes
,
D. H.
,
Nightingale
,
D. J.
, and
Hastings
,
D. E.
,
2012
, “
A Logical Approach to Real Options Identification With Application to UAV Systems
,”
IEEE Trans. Syst. Man Cybernet., Part A
,
42
(
1
), pp.
32
47
.
31.
Cardin
,
M.-A.
,
Kolfschoten
,
G. L.
,
Frey
,
D. D.
,
de Neufville
,
R.
,
de Weck
,
O. L.
, and
Geltner
,
D. M.
,
2013
, “
Empirical Evaluation of Procedures to Generate Flexibility in Engineering Systems and Improve Lifecycle Performance
,”
Res. Eng. Des.
,
24
(
3
), pp.
277
295
.
32.
Lin
,
J.
,
de Neufville
,
R.
,
de Weck
,
O. L.
, and
Yue
,
H. K. H.
,
2013
, “
Enhancing the Value of Oilfield Developments With Flexible Subsea Tiebacks
,”
J. Pet. Sci. Eng.
,
102
, pp.
73
83
.
33.
Martin
,
J. D.
, and
Simpson
,
T. W.
,
2006
, “
A Methodology to Manage System-Level Uncertainty During Conceptual Design
,”
ASME J. Mech. Des.
,
128
(
4
), pp.
959
968
.
34.
Spall
,
J. C.
,
2003
,
Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control
,
Wiley
,
Hoboken, NJ
.
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