The Role of Systems Thinking in Systems Engineering, Design and Management

Abstract

Systems thinking is a widely recognized and subscribed-to concept. Many benefits are ascribed to systems thinking and its result - the holistic solution. Yet, there is a wide range of opinion as to what systems thinking really is, and how its benefits can be realized in engineering practice. In fact, the concept of what constitutes a ‘system’ is wide and variable. The purpose of the paper is to draw together diverse perspectives of systems thinking useful in engineering, and to present a set of core concepts that are useful in the successful design and operation of engineered systems. These concepts will be illustrated with examples drawn from the author’s experience in teaching and research on engineered systems.

Full text

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The Role of Systems Thinking in Systems Engineering, Design and
Management
Chan Weng Tata
a Department of Civil & Environmental Engineering, National University of Singapore.
Email: ceecwt@nus.edu.sg
Abstract: Systems thinking is a widely recognized and subscribed-to concept. Many benefits are ascribed to
systems thinking and its result - the holistic solution. Yet, there is a wide range of opinion as to what systems
thinking really is, and how its benefits can be realized in engineering practice. In fact, the concept of what
constitutes a „system‟ is wide and variable. The purpose of the talk is to draw together diverse perspectives of
systems thinking useful in engineering, and to present a set of core concepts that are useful in the successful
design and operation of engineered systems. These concepts will be illustrated with examples drawn from the
speaker‟s experience in teaching and research on engineered systems.
Introduction
This talk is based on my own personal experience
about systems thinking – learning systems thinking,
and using it in consulting and research projects over
a span of a quarter of a century. It reflects the
experiences gained in teaching systems thinking in
modules at the National University of Singapore in
two engineering departments – the Department of
Civil and Environmental Engineering, and Division
of Engineering and Technology Management. The
modules take the macro perspective of systems,
especially the planning, design and strategy aspects,
rather than the analysis and details of particular
engineering components or specialized technical
disciplines. My research focusses on modelling,
simulating and optimizing various kinds of systems
in engineering and management, particularly civil
engineering systems.
I serve as the Program Manager for the M.Sc.
program in Systems Design and Management, a
cross-disciplinary program based on a rational uni-
fied process for developing engineering systems. In
addition, I am the co-Director of the NUS-JTC i3c,
referred to simply as the „I-cubed centre‟ – the
„Innovative industrial infrastructure‟ centre. This is a
joint initiative of faculty specialized in master
planning, architectural and engineering design, who
collaborate to innovate new solutions and design
thinking methods for the industrial city of the future.
I3c is where my research and practice come together
in real life projects in design studios with engi-
neering and urban planning students.
My presentation will cover - What is systems thin-
king; Foundational concepts; Systems engineering;
Soft Systems Methodology; Systems management;
and Adaptive systems.
What is Systems Thinking
Systems thinking is understood to mean different
things by different people. A system could mean a set
of physical parts that are part of a bigger whole e.g.
the structural system of a building, or the traction
control system of an automobile. We could speak of
natural or man-made physical systems. A system
could also refer to a set of activities, processes or
procedures e.g. a safety management system, or the
human blood circulatory system. It could refer to a
group of people, firms or organizations, or more
abstract concepts like political, religious or social
beliefs, rules and norms held by the people in these
groupings. Systems are therefore very pervasive, and
the word is used to refer to a set of things or concepts
that are related to each other, and which convey a
meaning, effect or outcome that could not be
achieved by any single part on its own. Systems
created by humans are put together to achieve a
purpose, whilst the purpose imputed to natural
systems serves man‟s view of the world and his
relationship with Nature. One aspect of systems
thinking is therefore about identifying the parts of a
whole, or the factors that are important to an
outcome. In this perspective, a systems view aims to
be comprehensive, and exhaustive in coverage.
However, systems thinking is also associated with
seeing the „big picture‟ or macro view of things, as
opposed to a more detailed microscopic view. In this
sense, systems thinking allows one to comprehend
how all the pieces fit together to explain a
phenomenon, or how all the parts act to produce the
intended effect. It is said that with the ability to „see
the forest from the trees‟, one is able to solve a
problem in a balanced, and holistic way, rather than
narrowly focusing only on one aspect of the problem.
Holistic solutions, that address several issues
Chan, Weng Tat; The role of systems thinking in systems engineering, design and management; Proceedings of 5th European Asian
Civil Engineering Forum (EACEF-5), 15-18 September, 2015; Surabaya, Indonesia; Djwantoro Hardjito, Antoni, I. Muljadi (eds.); pp.
29-35. {keynote}

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simultaneously in an effective way, are said to be
preferred to locally optimized fixes.
Engineering and allied technical disciplines tend to
use the word systems in the first sense – to refer to a
collection of parts that have been systematically put
together into a purposeful whole. In doing so, the
second meaning of systems – as in systemic - that
emphasizes the whole rather than the parts, seems
to have been forgotten.
Foundational Concepts
The following are some of the key concepts central to
systems thinking. Some will be instantly recogni-
zable, yet others will not be so familiar. The
implication of adopting these concepts will influence
the way systems engineers approach problems, as
well as design and implement technical solutions
that are required to work in complex environments.
Boundary. The system boundary is a notional line
that separates things that are considered part of the
system, and everything else which forms its environ-
ment. Systems can be open or closed depending on
whether there is exchange of material, energy or
information across the boundary.
Part-whole structure. The parts of a system can be
organized into sub-systems, and these in turn, can be
organized into larger sub-systems in a bottom-up
fashion until the top-level system is reached. It
implies that systems can be decomposed into
increasingly smaller and specific parts.
Function and behavior: Man-made, engineered sys-
tems are designed for a purpose, which is achieved
by incorporating parts and components with the
required functions.Functions transform their inputs
into outputs, and creates change. The changes that
result when functions work together result in the
behaviour observed in the system.
Figure 1 shows a „white box‟ view of an idealized
system operating in its environment. The figure
emphasizes the boundary separation between the
system and its environment, the hierarchical arra-
ngement of the parts of the system, and interactions
between the different parts of the system, as well as
between the system and its environment. If one
obscures all the internal details of the system, and
focuses on the interactions between the system and
other actors in its environment, we would get the
„black box‟ view of the system.
Non-functional properties: The functional behaviour
of a system arises the parts of a system working
together. However, the interaction between parts
also leads to non-functional system characteristics
like safety, reliability, and other so-called system
‘ilities’ which cannot be attributed to the function of
any part or component, but arise from the interac-
tion between parts of a system.
Figure 1. Systems Hierarchy
Determinism: This relates to the relation between
cause-and-effect - it says that the cause is sufficient
to determine the effect. However, this may be too
simplistic except in the case of carefully designed
mechanisms since the level of the effect, or its
nature, may also depend on other factors i.e. the
cause is necessary, but not sufficient to explain the
effect observer. The implication of non-determinism
for systems thinking is that, the parts of a system
influence each other‟s behaviour, and a system‟s
behaviour is influenced by its environment.
Feedback and system dynamics: Another aspect of
system behaviour is the directionality of cause-and-
effect. Cause can determine effect, but effect can in
turn influence the cause subsequently if there is
feedback in the system. The existence of positive and
negative feedback loops, along with delays in these
loops, makes the repertoire of systems more exten-
sive, and more complex than what is available from
a collection of simple mechanisms that produce one-
way cause-and-effect changes.
Analysis vs synthesis: In systems analysis, the
system is broken down into increasing levels of
detail, whether it is by function or physical compo-
nent, and an attempt is made to understand the
system by knowing details of the parts. However, the
opposite occurs with synthesis – the behaviour of the
whole is determined by relating a part to a larger
whole, and understanding behaviour in its wider
context. Analysis employs reductionist thinking,
where complexity is reduced to simple cause-and-
effect, whereas synthesis employs expansionist thin-
king. Analysis accumulates knowledge, but it is
synthesis which confers understanding.
Conceptual models: Since everything can be consi-
dered a system to be analysed further, or part of a
larger system, it can be said that systems thinking is
about building conceptual models that explain the
complexity of the real world in terms of structure,
and behaviour.

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Adaptation & learning: Systems thinking wouldn‟t
be complete if it did not provide the means of
modelling how people adapt to a changing world,
and learn to improve their performance by acquiring
and using knowledge from their past actions and
experiences. The most important thing about learn-
ing is to have a model, whether explicit or implicit, of
how change occurs in the world, and to be willing to
reconsider our basic assumptions of change, cause-
and-effect, in our model.
Systems Thinking in Systems Engineer-
ing
Systems engineering. The International Council on
Systems Engineering (INCOSE) defines systems
engineering as „an interdisciplinary approach and
means to enable the realization of successful sys-
tems‟. [1] It is a systematic process of realizing
technical systems from needs, to requirements,
concept, design and the eventual realized product.
This definition envisions the creation of technology-
based systems as solutions that fit within larger
assemblies of technical systems to accomplish goals
and deliver services that could not be done by a
single system alone. Society requires systems that
are safe, reliable, efficient and cost effective to
operate, and which can work as part of larger sys-
tems in a trustworthy manner. Technical complexity
arises either from the way that system is intended to
work in its environment, or the way in which is
developed through interdisciplinary collaboration
between teams of many different technical spe-
cialists.
Systems engineers have a unique role among the
many technical disciplines involved in the creation of
such complex engineered systems. It is said that the
systems engineer is the only role / person in the
project team that: (1) places importance on seeing
the problem and its solution from a wide variety of
useful perspectives; (2) focusses on delivering a well-
balanced cost-effective technical solution, rather
than delivering technology for its own sake; and (3)
bridges the gap between different traditional tech-
nical disciplines and specialist disciplines in safety,
reliability, constructability etc.
Vee model of systems development. In order to
collaborate together in an interdisciplinary way,
engineers and specialists working on a system must
share a vision of how the system will be developed
and coordinated, as well as a means of communi-
cating concepts, requirements, issues, and design
solutions. The Vee model, shown in Figure 2, is one
such commonly used model of depicting the steps in
systems development; the process is depicted in an
idealized way to emphasize particular points like: (a)
orderly hierarchical problem definition and develop-
ment of design detail; (b) progressive integration of
partial solutions into larger wholes, and ultimately
the complete system itself; (c) existence of technical
reviews to serve as gates between different stages;
(d) the intimate relationship between design and
testing, verification and validation of the design.
In reality, the systems development effort is more
complex, and involves iteration between different
stages, as well as recursive execution of more
fundamental problem solving steps at different levels
of system abstraction and definition.
Figure 2. Vee model of systems development
Model-based Systems Engineering (MBSE) is an
approach to doing systems engineering using a set of
systems concepts for modelling systems. These
models enable engineers to communicate and
address the different systems-level concerns during
the entire development life-cycle. Instead of using
information that may not be coordinated across the
different project documents, MBSE uses a system
model that is described through a set of interrelated
diagrams. Changes to the system, facilitated through
the view of the system in a particular diagram, can
be reliably propagated to related parts of the system,
and other diagrams. Systems Modelling Language
(SysML) [2], is the specialized language used to do
MBSE. With SysML, it will become increasingly
possible to support and automate many aspects of
the systems engineering effort with some form of
intelligent computer assistant.
Soft Systems Methodology
Technology is pervasive in human society, and is
incorporated into our human created systems to
solve problems and deliver services. Even so, we
must not forget that the root of our problems and
needs arise from human activities in society–
„human activity systems‟. Focusing on technology
alone, and not getting the purpose, goal and
requirements for systems right often leads to short
term, ineffective and costly solutions that need to be
different levels of system abstraction and definition.
Figure 2. Vee model of systems development
commons.wikimedia.org

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reworked. Peter Checkland and co-workers devised
Soft Systems Methodology (SSM) [3] as a way to
„inquire about a problematic situation‟, and to model
the way people interact and use technical systems
within systems of human activity to accomplish their
goals. The model of a socio-technical system includes
the key stakeholders, the assumptions and worl-
dview that make the human activity meaningful,
and the means by which humans accomplish their
goals. This is all captured in the acronym CATWOE,
which stands for Customers, Actors, Transformation,
World view, Owner, and Environment. Soft systems
models emphasize the human dimension of systems,
especially human activity, rather than the tech-
nological means used in these activities. Unlike
many „hard‟ systems models seen in Operations
Research and Industrial Systems Engineering, soft
systems models do not assume deterministic out-
comes, nor make any commitment to a particular
view of causality in the situation. The emphasis is on
building up understanding of increasing parts of the
whole from observation, rather than accumulating
detailed knowledge about an ever decreasing part of
the whole. In effect, SSM focusses on the „system-in-
use‟ and the hidden assumptions behind it, rather
than the system as espoused. System engineers also
use SSM to work with stakeholders on the concept of
use for technical systems, as well as the key system
requirements for a successful solution.
Systems Management
Systems thinking can be employed in the develop-
ment of management systems, in particular the
Safety and Health Management System (SHMS)
popular in the construction industry. A SHMS is a
collection of processes and activities, instituted by a
project organization, for the purpose of assuring the
safety and health of the people working on the
worksite. Its scope encompasses risk identification;
work and methods planning; provision and mana-
gement of the adequate resources for safe work;
training; preparation for handling emergencies; and
accident investigation among other things.
Due to the comprehensive nature and broad scope of
the activities undertaken by such a system, it is not
mandatory for all projects nor can smaller firms
implement a system in its entirety for every project.
As with many management systems, there are
international standards defining the requirements
for the documentation and implementation of the
processes in the system. Even so, there is no con-
sensus on how to audit the implementation of a
safety management system or assess its perfor-
mance. In Singapore, worksites with a contract sum
above S$30 million must perform regular audits of
the SHMS, using a standardized checklist and
scoring scheme called the Construction Safety Audit
Scoring System (ConSASS). [4] The standardized
questions and rubrics used in ConSASS improve the
reproducibility of audit scores, and help to remove
some elements of subjectivity in the assessment.
However, real progress in construction safety can
only be achieved if the capability of the construction
firms, particularly the smaller and medium sized
ones, to manage safe worksites is improved in a
systematic and systemic way.
Zhang et al. [5] takes a process view of a SHMS and
proposes a model called ConSASS-2D that is better
suited for process and system capability develop-
ment. The different activities and processes defined
in safety standards are organized into key process
areas that address different goals and concerns. The
processes in each of these areas show increasing
levels of capability, depending on the quality of their
outcomes they achieve, as well as the features and
attributes they possess. The process capability levels
are patterned after the Capability Maturity Model
(CMM) [6], first developed by the Software Engi-
neering Institute (SEI) at Carnegie-Mellon Univer-
sity for software development but now widely used
for other service and process oriented systems as
well.
Where ConSASS-2D departs from conventional
capability frameworks is to define a second dimen-
sion for the development of management systems.
The maturity dimension defines how well processes
work together to determine the quality of outcomes
produced by the system as a whole. ConSASS-2D
defines 4 maturity levels for SHMS (defined as
performed, managed and quantitatively managed,
and optimizing levels) so that firms can progressively
develop their systems by adding new processes or
increasing the capability of existing processes. In this
way, small firms can start out with the core
processes defined in the most basic level of system
maturity, and develop plans to develop the safety
management system to increasing levels of maturity
(much like the case in human development). Figure
3 shows the two dimensional framework of
ConSASS-2D comprising capability and maturity
level definitions.
Adaptive Systems
Intelligent systems learn to adapt their behaviour
and improve their performance through experience,
which comes from interaction and feedback with the
environment. Systems thinking can also be employ-
ed in creating intelligent and adaptive systems. One
approach is based on evolution in natural systems
and is called genetic algorithms (GA) [7]. A popu-
lation of string-like structures receives feedback from

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its environment about the fitness of each individual
in the population. At the same time, these indivi-
duals undergo selection, and transformations
through crossover and mutation. Crossover is the
mechanism whereby the good traits of individuals
are propagated throughout the population. Selection
ensures that fitter individuals have more chance at
reproducing themselves, and propagating their traits
within the evolving population.
Figure 3. Capability-Maturity dimensions of ConSASS-2D
In systems terms, individuals are composed of genes
which express different traits. These traits influence
fitness as determined by the environment in which
the individuals are placed. Interaction between the
environment and the individuals leads to selection
pressure, which in turn determines how the string
structures evolve to produce fitter individuals. The
system therefore comprises a population of string-
like structures, the environment that determines
fitness, and transformation operations that operate
over the population. This generic system can be
adapted and mapped to many kinds of planning,
scheduling and design problems, thus leading to its
popularity as a way to optimize plans, schedules and
designs. In Chan et al. (1996) [8], each individual in
the population pool represents a possible way to
schedule the construction activities in a project. The
different ordering possibilities determines the
resource usage profile as well as the total project
duration. Better schedules result in fewer instances
of resource usage exceeding resource availability,
and shorter project durations.
The GA was again employed to optimize production
schedules, but this time in the context of the
production of precast members in a factory. The
basic model was formulated in Chan et al. (2001) [9],
and extended to handle more production constraints
from the factory floor [10], as well as site coordi-
nation constraints imposed by the project schedule
[11] [12].
The problem of optimizing a maintenance pro-
gramme for a road network under budget cons-
traints was considered in Chan et al. (1994) [13]. The
individuals in the population pool represent different
possible maintenance programmes, and each is
evaluated for its effect on the performance of the
network over a study period. This basic optimization
model was extended in different ways. Chan et al.
(2001) [14] developed more a more computational
efficient model to handle constraints on desired
pavement performance, as well as constraints on
equipment availability and budget. Chan et al.
(2003) [15] formulated a model for optimizing the
maintenance budget allocations between different
road districts for a pavement network. The issue was
the best allocation of limited budget resources given
different maintenance priorities among the districts.
A search algorithm based on genetic algorithms was
used to determine the best allocation. However, this
work also marked the beginning of experimentation
with using an agent-based approach in the design of
optimal plans and schedules.
Intelligent artificial agents are software abstractions
of intelligent entities capable of sensing, messaging,
reasoning and taking actions in the pursuit of goals.
In agent-based models (ABM) [16], the actions and
interactions between autonomous agents result in
the emergence of system behaviour and properties.
Planners and designers are interested in represent-
ing problems as agent-based models because of the
realism it affords, and the ability to incorporate
phenomenon like learning, adaptation, competition
and strategy. Policy issues and strategy can be
studied by varying the design of the agents, their
interactions, or the environment in which they
operate. Being computational in nature, ABMs are
very adaptable, thus making it possible to address a
wide range of problems that would be difficult to
formulate and solve using mathematical modelling.
In the Chan et al. (2003) article cited above, agents
were used to represent the different districts, each
responsible for a portion of the pavement network.
The districts interacted with a central authority on
budget proposals as well as overall network perfor-
mance levels. However, the districts also negotiated
between themselves over the use of specialized
equipment.
Agents are again used to devise good strategies for a
problem involving the determination of competitive
pricing by freight forwarders in the logistics domain.
The problem is first described and formulated as a

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multi-level game involving freight forwarders, cargo
owners and shipping carriers [17]. Freight forwar-
ders compete among themselves for the business of
cargo owners, and must fulfil their obligations by
contracting for cargo space from shipping carriers.
The system is modelled from the perspective of the
freight forwarder as the „man-in-the-middle‟ – taking
prices from cargo owners, and costs from shipping
carriers. Solving the model using game theory gave
key insights into how prices were determined by the
level of competition among freight forwarders, and
the price sensitivity of cargo owners.
However, the game theory formulation imposes
restrictions on the number of players in the model,
as well as the assumption of full information avai-
lability to all players. The effect of learning on prices
could not be determined except under final equili-
brium conditions. These restrictions were removed
when an ABM was created for the same problem
situation, and different adaptation strategies were
incorporated into the freight forwarder agents based
on reinforcement learning [18]. Previously, only the
final equilibrium prices could be computed. With the
computation ABM model, time trajectories of all the
system variables can be recorded and studied.
Research in the use of ABM in engineering is still in
its early stages but shows a lot of potential for the
study of policy, strategic and operational decisions in
the area of transportation, safety, and construction
management.
Conclusion
Systems thinking is conceptualizing real world
phenomenon as models. A model is a set of interre-
lated concepts, expressed in some language that
captures some aspects of reality of interest. In
systems thinking, models are characterized by the
assumptions made about: (1) purpose; (2) the nature
of cause-and-effect; (3) certainty and determinism;
(4) inclusion of human and social concerns; and (5)
presence of feedback, adaptation and learning.
Systems thinking is now more important than ever
in the development of innovative solutions for socio-
technical systems. Soft systems methodologies have
proven popular to identify stakeholder‟s, their roles,
capabilities and concerns, elicit requirements, formu-
late use cases and innovate solutions.
Increasingly, the focus of systems engineering (in the
large sense of the word) is on delivering capability for
people and society to accomplish its purpose and
goals, and not just on delivering technology.
Systems engineers have a variety of models to choose
from, and a good systems thinker (systems engineer)
will be familiar with several of these. The examples
of systems thinking presented have been those I
have used in teaching and research. Knowing which
one to use depends on knowledge and experience,
and mastering systems thinking is a lifetime‟s quest.
I wish you all great success in your systems thinking
journey!
References
1. INCOSE, "What is Systems Engineering?,"
[Online]. Available: http://www.incose.org/About
SE/WhatIsSE. [Accessed September 2015].
2. Wikipedia, “Systems Modeling Language,”
[Online]. Available: https://en.wikipedia.org/wiki/
Systems_Modeling_Language. [Accessed Sep-
tember 2015].
3. Wikipedia, “Soft systems methodology,” [Online].
Available: https://en.wikipedia.org/wiki/Soft_sys-
tems_methodology. [Accessed September 2015].
4. Ministry of Manpower Singapore, “Submit a
ConSASS audit,” [Online]. Available: http://
www.mom.gov.sg/workplace-safety-and-health/
safety-and-health-management-systems/submit-
a-consass-audit. [Accessed September 2015].
5. Zhang, J. and Chan, W.T., “Developing a cons-
truction safety management system,” in Model-
ing Risk Management in Sustainable Construc-
tion, D. D. Wu, Ed., Springer Berlin Heidelberg,
2011, pp. 139-144.
6. Wikipedia, “Capability Maturity Model,”
[Online]. Available: https://en.wikipedia.org/wiki/
Capability_Maturity_Model. [Accessed Septem-
ber 2015].
7. Wikipedia, “Genetic algorithm,” [Online]. Avai-
lable: https://en.wikipedia.org/wiki/Genetic_algo-
rithm. [Accessed September 2015].
8. Chan, W.T., Chua, D. and Kannan, G., “Cons-
truction Resource Scheduling with Genetic
Algorithms,” J. Constr. Eng. Management, vol.
122, no. 2, p. 125–132, 1996.
9. Chan, W.T. and Hu, H., “An application of
genetic algorithms to precast production sche-
duling,” Computers & Structures, vol. 79, no. 17,
pp. 1605-1616, 2001.
10. Chan, W.T. and Hu, H., “Constraint programm-
ing approach to precast production scheduling,”
Journal of Construction Engineering and
Management, vol. 128, no. 6, pp. 513-521, 2002.
11. Chan, W.T. and Zeng, Z., “Coordinated pro-
duction scheduling of prefabricated building
components,” in Construction Research Con-
gress: Wind of Change: Integration and Innova-
tion, Honolulu, Hawaii, US, 2003.
12. Chan, W.T. and Zeng, Z., “Rescheduling precast
production with multiobjective optimization,” in
Computing in Civil Engineering, Cancun,
Mexico, 2005.

The 5th International Conference of Euro Asia Civil Engineering Forum (EACEF-5)
35
13. Chan, W.T., Fwa, T.F. and Tan, C.Y., “Road-
maintenance planning using genetic algorithms.
I: Formulation,” Journal of Transportation
Engineering, vol. 120, no. 5, pp. 693-709, 1994.
14. Chan, W.T., Fwa, T.F. and Hoque, K.Z., “Cons-
traint handling methods in pavement main-
tenance programming,” Transportation Research
Part C: Emerging Technologies, vol. 9, no. 3, pp.
175-190, 2001.
15. Chan, W.T., Fwa, T.F. and Tan, J.Y., “Optimal
fund-allocation analysis for multidistrict high-
way agencies,” Journal of Infrastructure Sys-
tems, vol. 9, no. 4, pp. 167-175, 2003.
16. Wikipedia, “Agent-based model,” [Online]. Avai-
lable: https://en.wikipedia.org/wiki/Agent-based_
model. [Accessed September 2015].
17. Qin, H. and Chan, W.T., “A Game-Theoretic
Approach for Freight Forwarders‟ Pricing Stra-
tegy Design,” in Proceedings of the Transpor-
tation Research Board 94th Annual Meeting,
Washington DC, United States, 2015.
18. Qin, H. and Chan, W.T., “Freight Forwarder‟s
Pricing Strategy Incorporating Learning from
Repeated Transactions,” in Proceedings of the
Transportation Research Board 94th Annual
Meeting, Washington DC, United States, 2015.