Analysis of investment policies for the Port of Lisbon with a System Dynamics model

Analysis of investment policies for the Port of Lisbon
with a System Dynamics model
Adriano Miguel Pinhal dos Santos
Dissertation submitted in partial fulfillment of the
requirements for the degree of Master in
Naval Architecture and Engineering
Jury
President: Prof. Yordan Ivanov Garbatov
Advisor: Prof. João Pedro Bettencourt de Melo Mendes
Co-advisor: Prof. Carlos António Pancada Guedes Soares
Members: Prof. Tiago Alexandre Rosado Santos
November 2012
INSTITUTO SUPERIOR TÉCNICO
Universidade Técnica de Lisboa

iii
Acknowledgments
I would like to thank Prof. João Pedro Melo Mendes and Prof. Guedes Soares for
their guidance and teaching.

v
Resumo
Um modelo em Dinâmica de Sistemas foi construído com o intuito de estudar
políticas de gestão que possam conduzir a um aumento da quantidade de carga
movimentada no Porto de Lisboa.
Objectivos adicionais incluem a avaliação dos lucros e investimentos portuários
associados a cada uma das medidas estudadas, bem como das suas implicações para a
economia regional. Os impactos da actividade portuária no emprego, comércio e PIB
regionais foram utilizados como medida de avaliação dos efeitos associados a cada uma
das políticas de gestão analisadas.
Foram utilizadas técnicas econométricas com o objectivo de melhorar os
resultados do Modelo de Dinâmica de Sistemas.
Palavras-chave: Dinâmica de Sistemas, actividade portuária, políticas de
gestão, economia regional, técnicas econométricas

vii
Abstract
A System Dynamics model is developed in order to study management policies
that could lead to an increase in throughput in Port of Lisbon.
Additional objectives include assessing port profits and investments associated
with each management policy, as well as their implications to the regional economy.
The impact of the port activity on regional employment, trade and GDP is used to
measure the beneficial effects associated with each policy.
A check of whether the results obtained from the System Dynamics model might
be improved by using econometric techniques ruled against the latter.
Keywords: System Dynamics, port activity, management policies, regional
economy, econometric techniques

ix
Table of contents
Acknowledgments ...........................................................................................................iii
Resumo............................................................................................................................. v
Abstract........................................................................................................................... vii
List of tables ................................................................................................................... xii
List of figures ................................................................................................................ xiv
1. Introduction............................................................................................................... 1
1.1. Background...................................................................................................... 1
1.2. Study objectives and research questions.......................................................... 3
1.3. System Dynamics and port economics ............................................................ 4
1.4. Methodology.................................................................................................... 6
1.5. Scope................................................................................................................ 7
1.6. The structure of the study ................................................................................ 8
2. Model Construction................................................................................................... 9
2.1. Relation between port container cargo capacity and demand.......................... 9
2.1.1. Port demand ........................................................................................ 9
2.1.2. Port capacity.......................................................................................11
2.1.3. Growth and underinvestment archetype for containerized cargo ......11
2.1.4. Deriving the standard capacity utilization of container terminals .... 14
2.1.5. Stock and flow diagram for the relationship between containerized
cargo port capacity and demand.............................................................................. 16
2.2. Maximum capacity......................................................................................... 19
2.2.1. Maximum installed capacity ............................................................. 19
2.2.2. Limits to container terminals capacity growth.................................. 20
2.3. Limits to growth for port demand.................................................................. 21
2.4. Relation between containerized and break bulk cargo................................... 24
2.5. Pricing considerations.................................................................................... 27

x
2.5.1. Prices considered in the model.......................................................... 27
2.5.2. The issue of dwell time: influence in capacity and demand ............. 28
2.6. Port revenues, port profits and investment in capacity.................................. 32
2.7. Impact of deepening the access channels and berths on containerized cargo
port demand.................................................................................................................... 34
2.8. External factors affecting port demand.......................................................... 41
2.8.1. National investments in ports............................................................ 41
2.8.2. Regional trade ................................................................................... 45
2.9. Break Bulk Cargo........................................................................................... 47
2.10. Dry Bulk Cargo.............................................................................................. 47
2.10.1. Factors affecting dry bulk cargo port demand ................................ 47
2.10.2. Impact of deepening the access channels and berths on dry bulk
cargo port demand................................................................................................... 51
2.11. Impact of the port activity on the regional economy ..................................... 52
2.11.1. Impact of the port activity on regional GDP................................... 52
2.11.2. Impact of the port activity on regional employment....................... 54
3. Checking the System Dynamics model using econometric techniques .................. 56
4. Results..................................................................................................................... 61
4.1. Reproduction of historical data...................................................................... 61
4.2. Sensitivity analysis......................................................................................... 66
4.3. Management policies analysis ....................................................................... 69
4.3.1. Change in resources availability ....................................................... 69
4.3.2. Change in port prices ........................................................................ 74
4.3.3. Deepening port access channel and berths........................................ 75
4.3.4. Comparison between deepening the port access channel and
increasing the resource availability at container terminals ..................................... 77
5. Conclusions............................................................................................................. 80
I. ANNEX 1: Systems Thinking and System Dynamics............................................ 83

xi
1.1. Systems Thinking........................................................................................... 83
1.2. System Dynamics........................................................................................... 90
II. ANNEX 2: Model equations................................................................................... 97
Bibliography................................................................................................................. 109

xii
List of tables
1.1 - Model boundary........................................................................................................ 7
2.1 - Container terminal quay line capacity benchmarks ............................................... 15
2.2 - Container terminal quay line performance benchmarks ........................................ 15
2.3 - Port price groups .................................................................................................... 27
2.4 - Prices and dwell times used in the model .............................................................. 27
2.5 - Port authority prices (Container terminals) used in the model............................... 28
2.6 - Total costs per tonne of dry bulk cargo transported ............................................... 51
2.7 - Historical evolution of the regional GDP, Gross Added Value and Apparent Work
productivity ............................................................................................................. 54
3.1 - Generalized Least Squares test statistic ................................................................. 56
3.2 - Econometric model equations................................................................................ 57
3.3 - Econometric model System Dynamics equations .................................................. 57
3.4 - Econometric model relative errors. ........................................................................ 60
4.1 – Model results and relative errors for TEUs port throughput. ................................ 61
4.2- Model results and relative errors for break bulk cargo ........................................... 62
4.3 - Model results and relative errors for dry bulk cargo.............................................. 63
4.4 - Model results and relative errors for total cargo port throughput .......................... 64
4.5- Model results and relative errors for regional GDP................................................ 65
4.6 - Model results and relative errors for regional employment ................................... 66
4.7 - Parameters tested in sensitivity analysis ................................................................ 67
4.8 - Impact on total port throughput of a 10% reduction in standard terminal capacity
utilization levels ...................................................................................................... 69
4.9 - Impact on total port profits of a 10% reduction in standard terminal capacity
4.10 - Impact on total port investments of a 10% reduction in standard terminal capacity
4.11- Impact on regional employment of a 10% reduction in standard terminal capacity
4.12 - Impact on total port throughput of a 10% decrease in port prices considered in the
model....................................................................................................................... 74
4.13 – Change in the effect of port price on container cargo demand and terminals
capacity investments when terminal operators price is decreased by 10%............. 75

xiii
4.14 - Change in cargo throughput when access channel and berths are dredged to allow
an increase of 10% in ship’s draft............................................................................ 76
4.15- impact of deepening the access channel in port profits, port investments and
regional employment............................................................................................... 76
4.16 - Percentual change in relation to baseline scenario under the two management
options (container terminals resources availability and dredging).......................... 77

xiv
List of figures
1.1 - Share of maritime trade in Portuguese external trade volume ................................. 2
1.2 - Port of Lisbon's share in regional trade volume....................................................... 2
1.3 - Share of maritime trade in national and regional external trade volume ................. 2
1.4 – Total throughput at the Port of Lisbon..................................................................... 3
2.1 - General causal loop diagram archetype for growth and underinvestment............. 12
2.2 - Container throughput at the Port of Lisbon............................................................ 12
2.3 - Growth and underinvestment archetype for the container terminals at Port of
Lisbon.................................................................................................................... 13
2.4 - Growth and underinvestment archetype for container terminals ship-to-apron
transfer capacity. ................................................................................................... 16
2.5 - Stock and flow diagram for Port of Lisbon's container terminals ship-to-apron
transfer capacity growth and underinvestment archetype..................................... 17
2.6 - Stock and flow diagram for Port of Lisbon's container terminals maximum
installed capacity. .................................................................................................. 20
2.7 - Stock and flow diagram for Port of Lisbon's container terminals physical limit to
expansion. ............................................................................................................. 21
2.8 - Limits to growth archetype causal loop diagram. .................................................. 21
2.9 - Limits to growth in port demand............................................................................ 22
2.10 – Stock and flow diagram for limits to growth in port demand. ............................ 22
2.11 - Causal loop diagram for the maximum containerized cargo port demand........... 23
2.12 – Stock and flow diagram for maximum containerized cargo port demand........... 24
2.13 – Success to the successful archetype causal loop diagram. .................................. 25
2.14 – Success to the successful archetype for the relationship between containerized
and break bulk cargo. ............................................................................................ 25
2.15 - System dynamics structure for the success to the successful archetype concerning
the relationship between containerized and break bulk cargo .............................. 26
2.16 – Stock and flow diagram for the influence of storage price in containers dwell
time........................................................................................................................ 30
2.17 – Stock and flow diagram for the influence of dwell time in storage capacity. ..... 31
2.18 – Stock and flow diagram for the impact of port costs on port demand................. 32
2.19 - Container throughput and revenues evolution for the Port of Lisbon.................. 33
2.20 - Causal loop diagram for port revenues and investments...................................... 33

xv
2.21- Stock-and-flow diagram for port revenue and investments. ................................. 34
2.22 - Causal Loop Diagram for the Deepening of the Access Channels and Berths. ... 36
2.23 – Stock and flow diagram for the impact of deepening the access channels and
berths at the Port of Lisbon................................................................................... 37
2.24 - Relation between the ship's draft and cargo capacity ......................................... 38
2.25 - Voyage costs as a function of ship capacity and distance travelled ..................... 39
2.26 - Stock-and-flow diagram for the variables affecting containerized cargo
throughput growth rate.......................................................................................... 40
2.27 - Historical evolution of investments in ports and roads........................................ 41
2.28 - Investments in Port of Lisbon. ............................................................................. 42
2.29 – Success to the successful archetype’s causal loop diagram for national port
industry.................................................................................................................. 42
2.30 – Stock and flow diagram for the national transportation industry's success to the
successful archetype.............................................................................................. 43
2.31 – Stock and flow diagram for the ship-to-apron transfer capacity. ........................ 45
2.32 - Causal loop diagram for the relation between containerized cargo port throughput
and regional trade.................................................................................................. 45
2.33 - Stock and flow diagram for the relation between port throughput and regional
trade....................................................................................................................... 46
2.34 - Stock and flow diagram for all the variables considered to affect containerized
cargo throughput growth rate................................................................................ 46
2.35 – Comparison between historical evolution of dry bulk cargo throughput at the Port
of Lisbon and Baltic Dry Index............................................................................. 48
2.36 - Comparison between historical evolution of dry bulk cargo throughput at the Port
of Lisbon and Regional GDP................................................................................ 48
2.37 - Regression of dry bulk cargo throughput and Baltic Dry Index. ......................... 49
2.38 - Regression of dry bulk cargo throughput and regional GDP. .............................. 49
2.39 – Stock and flow diagram for the relation between Baltic Dry Index and Dry Bulk
Cargo Port Demand............................................................................................... 49
2.40 – Relation between bulk carriers DWT and draft................................................... 51
2.41 - Annual percentual variation in world GDP, exports and imports......................... 53
2.42 - Regional GDP and trade growth between 1995 and 2010 .................................. 53
2.43 - Correlation between regional GDP and trade volume for the region of Lisbon .. 53

xvi
2.44 – Stock and flow diagram for the interaction between regional trade and regional
GDP. ...................................................................................................................... 54
2.45 – Stock and flow diagram for regional full-time equivalent employment ............. 55
3.1 - Stock-and-flow diagram for the econometric model ............................................. 58
3.2 – Stock and flow diagram for the modifications introduced in the original System
Dynamics model with the econometric data ......................................................... 58
3.3 - Econometric model results..................................................................................... 59
4.1 - Model results for TEUs port throughput................................................................ 61
4.2 - Model results for break bulk cargo port throughput .............................................. 62
4.3 - Model results for dry bulk cargo port throughput.................................................. 63
4.4 - Model results for total cargo port throughput ........................................................ 64
4.5 - Model results for regional GDP ............................................................................. 65
4.6 - Model results for regional employment ................................................................. 66
4.7 - Model sensitivity to standard container terminal capacity utilization.................... 67
4.8 - Model sensitivity to standard break bulk terminal capacity utilization ................. 68
4.9 - Model sensitivity to standard break bulk terminal capacity utilization ................. 68
4.10 - Impact on total port throughput of a 10% reduction in standard terminal capacity
utilization levels. ................................................................................................... 70
4.11 - Impact on total port profits of a 10% reduction in standard terminal capacity
utilization levels .................................................................................................... 71
4.12 - Impact on total port investments of a 10% reduction in standard terminal capacity
4.13 - Impact on regional employment of a 10% reduction in standard terminal capacity
4.14 - Total port throughput for baseline scenario, decreasing container terminals
standard capacity utilization and deepening the access channel........................... 78
4.15 - Port profits for baseline scenario, decreasing container terminals standard
capacity utilization and deepening the access channel. ........................................ 78
4.16 - Port investments for baseline scenario, decreasing container terminals standard
capacity utilization and deepening the access channel. ........................................ 79
4.17 - Regional employment for baseline scenario, decreasing container terminals

xvii
4.18 - Cumulative investment for baseline scenario, decreasing container terminals

1
1. Introduction
1.1. Background
The interdependency between trade and GDP has been studied elsewhere (e.g.
Stopford, 2009). The observation of historical data on GDP and trade seems to support
some of the main principles of international trade theory, namely, that there is a strong
correlation between trade and GDP. So, it can be concluded that, in an open economy,
international trade plays a pivotal role in economic growth.
In Portugal, the share of international trade in GDP increased from 32 percent in
1995 to 38 percent in 2008, well above OECD average, which rose from 19 percent in
1995, to 29 percent in 2008, and slightly below that of EU-27, which increased from 29
percent in 1995 to 41 percent in 2008 (OECD, 2012). Between 2000 and 2010, maritime
trade value maintained its 30 percent share in total external trade. The maritime trade
share in terms of volume was higher in this period, around 65 percent, which is
explained by the lower value of sea transported goods when compared to commodities
transported by airplane, train or truck (INE, 2000-2010).
Portugal can be qualified as an open economy (has a degree of openness of about
60 percent). Because maritime trade accounts for 30 percent of the international trade
value, it can be concluded that seaports play a decisive role in national economic
growth. This conclusion applies to the region and Port of Lisbon. The degree of
openness of Lisbon’s economy is even higher than the national average, with a value of
approximately 75 percent. Port of Lisbon´s share in regional trade volume amounts to
approximately 25 percent (INE, 2012).
The Port of Lisbon is the leader maritime port in Portugal in terms of container
throughput, and ranks third in terms of total cargo throughput, behind ports of Sines and
Leixões. This is explained mostly by the importance liquid bulk cargo has in these two
ports.
The influence of Port of Lisbon is primarily felt in the region of Lisbon, where the
origin and destination of some 70% of its cargo is located. However, the Port of Lisbon
extends its influence to outside the region of Lisbon, moving cargo to or from regions
such as Azores.
At the national level, maritime international trade share amounted to
approximately 65% of the total Portuguese international trade in the 2000-2010 period
(see figure 1.1).

2
Figure 1.1 - Share of maritime trade in Portuguese external trade volume [source: INE
(2000-2010)]
From 2000 to 2010, the share of Port of Lisbon in regional trade oscillated around
a mean value of 25% (see figure 1.2).
Port of Lisbon’s share in regional trade is smaller than that of maritime trade as a
whole in national external trade (see figures 1.2 and 1.3).
Figure 1.2 - Port of Lisbon's share in Lisbon trade volume [source: INE (2000-2010)].
Figure 1.3 - Share of maritime trade in national and regional (Lisbon) external trade
volume [source: INE (2000-2010)].
Shipping is the less expensive common method of transportation (Stopford, 2009).
But while this mode of transportation represents about 65 percent of the trade volume at
a national level, Port of Lisbon’s throughput represents only about 25 percent of the
0%
20%
40%
60%
80%
100%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Other
Maritime Trade
0%
20%
40%
60%
80%
100%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Other
Port of Lisbon
00%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Portugal Lisbon
Maritime trade
Total trade

3
regional trade volume. So, there is some margin for the share of maritime transportation
associated with the Port of Lisbon to grow in this region. This growth could well
represent an important factor in decreasing the cost of transportation and, as a
consequence, an important factor for economic growth. The money that is currently
being spent in trucking transportation could be diverted to investments in infrastructure
improvement, say. Also, it should be noted that, in terms of value, Lisbon’s imports and
exports account for around 45% of the total national external trade (INE, 2012).
Also, a noticeable decrease in throughput growth rate in Port of Lisbon was
observed in the years between 1995 and 2010 (see figure 1.4). Between 2005 and 2006,
port throughput decreased and afterwards began to oscillate around a mean value of
about 12 250 tons.
Figure 1.4 – Total throughput at the Port of Lisbon [1 000 tons] [source: APL ( 2012)].
1.2. Study objectives and research questions
The objective of this study is to assess management policies that could lead to an
increase in Port of Lisbon’s throughput and profit. The research questions are:
Why has throughput growth rate decreased in Port of Lisbon over the years
between 1995 and 2010, and particularly since 2005?
What are the most effective and efficient management policies to increase Port of
Lisbon’s throughput and its profitability?
9 500
10 000
10 500
11 000
11 500
12 000
12 500
13 000
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Portthroughput[1000tons]

4
Port of Lisbon follows a landlord model, and, as such, there are several actors
involved in Port management as a whole, with port operators holding the most
important decisions at operational and tactical level, while Port authority and
Government have a more decisive role at a strategic management level. However, this
does not necessarily mean that a strategic level policy has a higher leverage than a
decision taken at the tactical or even operational level. In fact, some of the most crucial
decisions affecting port capacity, for example, are under the control of port operators.
This leads to a secondary research question:
What management decisions have a higher leverage degree in terms of affecting
port capacity, throughput and profitability?
1.3. System Dynamics and port economics
System Dynamics methodology constitutes an effective approach for studying the
impact of port management policies because it allows the direct testing of several
alternatives, it allows the incorporation of experts opinion in the model itself, and a
System Dynamics model is easily modified in order to account for changes in the
socioeconomic environment (i.e., an unexpected change in GDP, employment, etc).
One of the main objectives of a System Dynamics model is to act as a
communication tool, with all stakeholders having a say on the nature of the causal
relations used in the model. It is only when the causal relations are clearly understood
and discussed that confidence in the model and its results can be built.
For these reasons, System Dynamics appears to be an appropriate tool to answer
the research questions posed in the previous section.
The System Dynamics approach has been used by several authors to study
maritime ports and to identify management policies affecting cargo throughput and
profitability.
Castillo et al. (2004) consider GDP the decisive factor affecting port demand,
and use GDP as an external input to their model (i.e., GDP does not depend on port
throughput). Another external input is investments in port capacity, which they consider
independent of port price and port demand. They use regional employment as the main
indicator to evaluate the impact of port activity on regional economy. However, like
Park et al. (2010), they consider the ship’s (or cargo) decision to enter a port to depend

5
on the cost difference between the port under study and some other competing port. This
seems reasonable if only one competing port is considered or if the average cost for the
competing modes of transportation is known, since port choice would depend on a
simple comparison between only two prices. However, Port of Lisbon cannot be
considered to have only one competing port, nor is the average cost for the competing
modes of transportation and competing ports known. So, to this end, instead of using the
cost difference between competing ports or modes of transportation, a formulation
which is equivalent to the economics measure known as elasticity of demand is used
(for details on port elasticity of demand, see Talley (2009)).
Carlucci et al. (2009) also use GDP as the decisive factor affecting port demand
to study the impact of port investments in regional economy and assess the implications
of management policies. But contrary to Castillo et al. (2004), they consider the
feedback relationship between port throughput and GDP (i.e., GDP acts as an input for
port demand, but port demand also acts as an input for GDP) in a model using the
relation between port throughput, revenues and investment costs to derive port profits.
Feng et al. (2008) also considered the feedback relation between GDP and port demand.
There are System Dynamics models for maritime ports built under the assumption that
economic growth is the only factor affecting port demand (Feng et al., 2008; Carlucci et
al., 2009). However, no System Dynamics model considers the quality of the service
provided by the port to be a factor of choice for that port, much less to be a major
determinant of port demand. Yet, port stakeholders and maritime economics authors
agree that port price and the quality of the service provided are just as important, with
quality of service being particularly important in the container industry and port price
more important in the bulk industry (Stopford, 2009; Talley, 2009; Kolanovic et al.,
2011).
Kolanovic et al. (2011: 495) define port service quality as “a product or service
that satisfies customer requirements and expectations”. They consider port service
quality to be composed of five factors: port accessibility, port reliability, port
functionality, port information availability, and port flexibility. Each of these factors can
be further decomposed into minor ones such as: quality of sea rail and road access;
quay, yard and gate capacity; customs service quality; etc. Concerning port capacity,
Talley (2009) defines the shipping capacity of a supply chain network (in the case of
containerized cargo) as: “the maximum number of containers (TEUs) that shippers can

6
ship from their export sites to their consignee sites in the chain for a given time period.
This capacity will be constrained by the chain’s link or node with the smallest shipping
capacity” (pp 82). So, port capacity is regarded as the capacity of the element which has
the smallest capacity within the port (quay, yard, gate, pilotage, customs, road and rail
access, etc).
1.4. Methodology
A basic introduction to Systems Thinking (ST) and System Dynamics (SD) is
presented in Annex I. There, the basic components of the Systems Thinking approach
are introduced, namely, the notion of archetype, causal loop diagram (CLD), reinforcing
and balancing loops, etc. The same is done for the System Dynamics approach, namely,
stock and flow diagrams and its notation. Henceforth, causal loop diagrams and stock
and flow diagrams are presented assuming the reader has knowledge of the basic
notions presented in Annex I.
The approach followed in the course of this work is a combination of the Systems
Thinking (ST) and System Dynamics (SD) approaches presented in Annex I. For
example, a special emphasis was put on identifying the structure of port competitiveness
through the use of archetypes (ST), which was required to build a simulation model,
with its respective stocks and flows (SD). The approach followed can be presented as:
1. Defining the problem in terms of dynamic behaviours, representing relevant
variables as graphs over time;
2. Identify the structure responsible for those behaviours (generally with the use of
archetypes);
3. Mapping independent stocks or accumulations (levels) in the system's structure
and their inflows and outflows (rates);
4. Formulating a behavioural model capable of reproducing, by itself, the dynamic
problem of concern (computer simulation);
5. Deriving understandings and applicable policy insights from the resulting
model;
The translation of the Systems archetypes causal loop diagrams (ST) into the
corresponding stock and flow diagrams (SD) followed the approach proposed by
Bourguet-Díaz and Pérez-Salazar (2003).

7
1.5. Scope
One of the main objectives of this work is to study the factors affecting port
capacity (as well as throughput and profitability, as mentioned in the previous section).
Capacity will be one of the key variables considered in the model. In the real world
most ports will have a limit to their capacity, often imposed by physical constraints
(usually as a consequence of their location within a urban area). This limit is important
for the Port of Lisbon, which is located at the heart of a densely populated urban area.
This limit is considered in the model. However it was considered that there is still some
margin for capacity growth, as, for example, the project for the expansion of the
Alcântara Container Terminal (TCA) demonstrates.
Containerized and dry bulk are considered strategic cargoes for Port of Lisbon
(CONSULMAR, 2007). Liquid bulk is not considered to be a strategic cargo for Port of
Lisbon’s port authority. As such, it was used in the model as an external input. Break
bulk cargo is also not a strategic cargo for that port, but due to its deep interrelation with
containerized cargo, it was treated as an endogenous variable.
Table 1.1 presents the model boundary. Physical limits to port expansion were
considered as an exogenous parameter.
Table 1.1 - Model boundary.
Endogenous Exogenous
o Port capacity
o Containerized cargo throughput
o Dry bulk cargo throughput
o Break bulk cargo throughput
o Port investments
o Port prices
o Regional GDP
o Regional Trade
o Service quality standards
o Physical limits to port expansion
o Liquid bulk cargo throughput
The time horizon for the model simulation spans from 1995 to 2010. The main
criterion adopted for the choice of the time horizon was whether or not that same time
horizon was sufficient for the dynamical behaviour of the variables under study to
become clear. In other words, for the key variables to exhibit some behaviour typically
associated with certain system archetypes (for a deeper discussion on system archetypes
see Annex I). Also, it was not possible to obtain data prior to 1995 for the relevant
variables.

8
1.6. The structure of the study
Chapter 2 presents the construction of the simulation model.
Chapter 3 presents a validation check of the System Dynamics model using an
econometric model.
Chapter 4 presents the results from the System Dynamics model.
Chapter 5 presents the conclusions of this study and directions for future
research.

9
2. Model Construction
Chapter 2 presents the model construction and is organized in the following
manner:
Sections 2.1. to 2.7 describe the factors affecting port demand and that are under
the direct control of the port (i.e, the port authority or the port operators) .
Section 2.8 describes the model construction to account for the external factors
affecting containerized cargo port demand, namely, regional trade and national
investments in ports.
Sections 2.9 and 2.10 describe the model construction for break bulk cargo and
dry bulk cargo, respectively. Whenever the factors affecting these cargoes were
considered to be the same as for the containerized cargo, the model construction was not
described again. Only when factors affecting port demand were considered to be
different from those presented for the containerized cargo were they described.
Section 2.11 describes the model construction for the impact of the port activity
on the regional economy. This impact was assessed in terms of regional employment
and GDP.
2.1. Relation between port container cargo capacity and demand
2.1.1. Port demand
Talley (2009) defines Generalized Port Price as the sum Port Price + Ocean
Carrier Port Time Price + Inland Carrier Port Time Price + Shipper Port Time Price. The
Port Price corresponds to prices charged by the port for its services (for example,
wharfage, berthing, pilotage and cargo handling services); the Ocean Carrier Port Time
Price corresponds to the time-related costs incurred by ocean carriers while their ships
are in port (including ship depreciation, fuel, and labour costs); the Inland Carrier Port
Time Price corresponds to the time-related costs incurred by inland carriers (truck or
rail) while their vehicles are in port (including vehicle depreciation, fuel, and labour
costs); and the Shipper Port Time Price corresponds to the time-related costs incurred by
shippers while their shipments are in port (including inventory costs such as insurance,
obsolescence and depreciation costs).
So, the Generalized Port price can be split into two groups, prices related to time
and prices directly attributable to port charging practices. Port time (including ship,

10
vehicles, and cargo turn-around time) is a measure of the quality of service provided by
the port.
Talley (2009) then defines a freight port’s throughput function as the relationship
between the demand for the port’s throughput services by its freight users and the
generalized port price (per unit of throughput) incurred by these users, i.e., port
throughput = f(Generalized Port Price). Port throughput depends on prices charged by
the port (including port authority and terminal operators) and the quality of service
provided by the port, which is treated as a general port property from the point of view
of the ocean carrier, inland carrier or shipper. There are many port performance
indicators that can be used to assess the quality of service provided by the port:
 vehicle/ship turn-around times;
 vehicle/ship loading and unloading service rates;
 cargo turnaround time;
 channel reliability (the percent of time that the port’s channel is open to
navigation);
 berth reliability (the percent of time that the port’s berth is open to the
berthing of ships);
 entrance gate reliability (the percent of time that the port’s entrance gate is
open for vehicles);
 departure gate reliability (the percent of time that the port’s departure gate is
open for vehicles);
 channel accessibility (the percent of time that the port’s channel adheres to
authorized depth and width dimensions);
 berth accessibility (the percent of time that the port’s berth adheres to
authorized depth and width dimensions);
 probability of cargo loss in port;
 probability of cargo damage in port;
 probability of ship damage in port;
 probability of ship property loss in port;
 probability of vehicle damage in port;
 probability of vehicle property loss in port;
 port prices.

11
Each stakeholder will value more one specific indicator (for example, an ocean
carrier will typically be more concerned with ship turnaround time, while a shipper will
focus his attention on cargo turn-around time, and an inland carrier may consider
vehicle turn-around time to be more important). So, in order to appreciate port demand
as a function of the quality of service provided by the port, a more general concept of
service quality is needed. For this reason, the quality of service provided by the port is
appreciated in a general sense, as a relation between port demand and port capacity. In
other words, service quality depends directly on resource availability.
2.1.2. Port capacity
A port’s capacity is defined by its throughput bottleneck under normal working
conditions (Drewry Research, 2010). That is, under normal working conditions, the
port’s capacity is the lowest throughput value among:
 ship-to-apron transfer capacity;
 apron-to-storage transfer capacity;
 yard storage capacity;
 storage-to-inland transport transfer capacity;
 inland transport processing capacity.
Port capacity will be assessed for each of these five components. However, when
the term port capacity (or terminal capacity) is used without reference to a particular
component, it will mean the lowest value of throughput among the five components.
2.1.3. Growth and underinvestment archetype for containerized cargo
Following the approach discussed in section 1.4., system archetypes were used
to build the model. In the particular case of the relation between containerized cargo
port capacity and demand, the archetype growth and underinvestment was used.
In the growth and underinvestment archetype, growth reaches a limit that could be
avoided or postponed if capacity investments were made (Senge, 1990). Instead, as a
result of policies or delays in the system, demand (or performance) degrades, limiting
further growth. This leads to further withholding of investments or even reductions in
capacity, causing even worse performance. The causal loop diagram for this archetype is
represented in figure 2.1.

12
Figure 2.1 - General causal loop diagram archetype for growth and underinvestment.
This appears to be the case when it comes to investments in container terminals
capacity at the port of Lisbon. The historical data on containers moving through this
port illustrate the situation (figure 2.2).
Figure 2.2 - Container throughput at the Port of Lisbon [1 000 TEUs] [source: APL
(2012)].
The volume of containerized goods moving through the port of Lisbon had been
steadily increasing since, at least, 1995. However, in 2004, the number of containers
loaded and unloaded stopped increasing, even decreased slightly. Afterwards, annual
container throughput began to oscillate around a mean value of slightly more than 500
thousand TEUs.
The decrease in container movement coincided with the decision of
Mediterranean Shipping Company (MSC) to move the operation of its regular container
0
100
200
300
400
500
600
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Containerthroughput[1000TEUs]
Growth effort Demand
Impact of limiting
factor
Capacity
Perceived need to
invest in capacity
Investment in
capacity
Performance
standard
+
+
+
-
+
-+
+
+
R1 B2
B3

13
line from port of Lisbon to port of Sines (APL, 2006). The general perception among
port stakeholders was that the installed capacity at the port of Lisbon was no longer able
to satisfy the needs of that transportation agent because, as usage increased, port service
quality began to degrade.
Recently, the terminal previously used by MSC made efforts to increase its
capacity. But the legal permission to start the necessary works has, to date, not yet been
given, which is another typical feature of the growth and underinvestment archetype -
the delay in building extra capacity. This delay may result either from delays in
perceiving and accepting the need for investment, from the time to actually build the
needed extra capacity, or, more commonly, from a mix of these and other legal or
political impediments. Figure 2.3 illustrates the growth and underinvestment archetype
for a container terminal.
Figure 2.3 - Growth and underinvestment archetype for the container terminals at Port
of Lisbon.
Shipping industry makes use of economies of scale. After a base operation is set,
the natural tendency is for that base to grow, which is represented by the reinforcing
loop R1: the higher the containerized cargo port throughput, the higher the
containerized cargo throughput growth rate (and vice-versa), and so forth until it meets
some kind of limit.
Containerized cargo port throughput uses installed container terminals capacity.
The more capacity is used, the lesser the container terminals resources availability. In
turn, the lesser the container terminals resources availability (a measure of the quality
Containerized cargo
throughput growth
rate
Containerized cargo
port throughput
Container terminals
capacity utilization
Container terminals
resources availability
Perceived need to invest
in container terminals
capacity
Standard container
terminals capacity
utilization
Investment in
container terminals
capacity
Container
terminals capacity
+
+ +
-
+
+ -
+
+
-
R1 B2
B3

14
of service provided by the terminal), the less shippers will perceive the port as an
attractive one, and containerized cargo port throughput will decrease. This relationship
is depicted by the balancing loop B2.
However, if ports have the possibility of adjusting its capacity to demand, they
always do. If performance degrades, port authorities or port operators will perceive
(either through direct observation or through complaints from clients and other
stakeholders) the need to invest in extra capacity in order to cope with demand. The
higher the container terminals capacity utilization levels, the higher the perceived need
to invest in container terminals capacity.
The perception of the need to invest comes from a comparison between current
service quality and a standard service quality, and triggers, as a consequence, the
investment in container terminals capacity. Finally, the higher the container terminals
capacity, the lower the container terminals capacity utilization. The balancing loop B3
illustrates the relationship between capacity utilization and port capacity (i.e., the
change in port capacity through investment).
2.1.4. Deriving the standard capacity utilization of container terminals
In order to use the growth and underinvestment archetype, it is necessary to derive
a standard capacity utilization to which current capacity utilization will be compared
with the ensuing investment decisions made.
In the case of containers, standard capacity utilization levels can be inferred
from port performance and capacity benchmark data, presented in tables 2.1 and 2.2.
These data were obtained by Drewry Research (2010) through surveys on a significant
number of ports. The referred study includes data on capacity and performance
benchmarks.
From tables 2.1 and 2.2, a performance (or demand) benchmark (as a fraction of
installed capacity) for container terminals can be calculated as a ratio between achieved
performance and capacity benchmark. The values chosen were taken as an
approximation of those for 2009. The mixed arrival schedule, with regulated tariff, high
berth occupancy and common user facility was chosen as the type of port activity that
more closely resembles that of Port of Lisbon. Concerning performance benchmarks,
Port of Lisbon was considered to be more similar to Southern Europe ports than to those
of Northern Europe.

15
Table 2.1 - Container terminal quay line capacity benchmarks (TEU/m of quay/ annum)
[source: Drewry Research (2010)]
Port activity main
characteristics
Large terminal with
more than 1,000 metres
of quay line
Medium terminal with
between 500 and 1,000
metres of quay line
Small terminal with
between 250 and 500
metres of quay line
Mixed arrival schedule,
competition
encouraged, free-market
tariff, gateway port
1,200 1,000 800
Mixed arrival schedule,
regulated tariff, high
berth occupancy,
common user facility,
gateway port
1,500 1,200 1,000
Tightly scheduled ship
arrival, low priority
given to competition
policy, high
transshipment activity
1,700 1,600 1,300
Table 2.2 - Container terminal quay line performance benchmarks (TEU per metre of
quay per annum) for Southern Europe [source: Drewry Research (2010)]
Year 2007 2008 2009
Number of terminals
sampled
47 48 48
Sample total
Throughput (TEU) 29,363,607 28,584,445 25,422,647
Quay length (meters) 48,346 48,910 48,824
Sample average
Average throughput per
terminal
624,758 595,509 529,638
Average quay length
per terminal
1,029 1,019 1,017
Performance data
TEU per meter of quay
per annum
607 584 521
The selected data are:
achieved performance = 500 [TEU/m/a]
capacity benchmark = 1000 [TEU/m/a]
capacity utilization benchmark = 0.5 [-]
In other words, the standard capacity utilization level dictates that capacity should
approximately double demand.
However, it was mentioned before that port capacity is split in five components
(ship-to-apron transfer capacity, apron-to-storage transfer capacity, yard storage
capacity, storage-to-inland transport transfer capacity, and inland transport processing
capacity). The causal loop diagram represented in figure 2.3 is further developed as in

16
figure 2.4, representing the same archetype for container terminals ship-to-apron
transfer capacity.
Figure 2.4 - Growth and underinvestment archetype for container terminals ship-to-
apron transfer capacity.
The other four components of container terminal capacity would represent 8 extra
balancing loops (B2 and B3 would be the same), while B1 is common to all five
components of terminal capacity. Since the causal loop diagrams for the other four
components are essentially the same as for ship-to-apron transfer capacity, they are not
represented here.
2.1.5. Stock and flow diagram for the relationship between containerized cargo
port capacity and demand
The translation of the growth and underinvestment archetype structure from a
causal diagram (Systems Thinking) to a System Dynamics stock and flow diagram is
represented in figure 2.5. The other four components of container terminal capacity are
not shown in the stock and flow diagram since they are essentially represented in the
same way and the reasoning behind their equations is similar.
The auxiliary variable container terminals ship-to-apron transfer capacity
utilization is calculated as a simple fraction relating containerized cargo port throughput
and container terminals ship-to-apron transfer capacity:
CONTAINER TERMINALS SHIP-TO-APRON TRANSFER CAPACITY UTILIZATION =
Containerized Cargo Port Throughput/Container Terminals Ship-to-Apron Transfer Capacity [Units: Dmnl]
The parameter standard container terminals ship-to-apron transfer capacity
utilization is derived from the performance and capacity benchmarks presented in tables
Containerized cargo
throughput growth
rate
Containerized cargo
port throughput
Container terminals
ship-to-apron
transfer capacity
utilization
Container terminals
Perceived need to invest
in container terminals
ship-to-apron transfer
capacity
Standard container
terminals
ship-to-apron
transfer capacity
utilization
Investment in
container terminals
ship-to-apron
transfer capacity
Container terminals
ship-to-apron
transfer capacity
+
+ +
+
+ -
+
+
-
R1 B2
B3
Container terminals
-
+

17
2.1 and 2.2 (note that this capacity utilization level was used for all the five components
of container terminals capacity):
STANDARD CONTAINER TERMINALS SHIP-TO-APRON TRANSFER CAPACITY UTILIZATION=
STANDARD CONTAINER TERMINALS CAPACITY UTILIZATION [Units: Dmnl]
STANDARD CONTAINER TERMINALS CAPACITY UTILIZATION=0.5 [Units: Dmnl]
Figure 2.5 - Stock and flow diagram for Port of Lisbon's container terminals ship-to-
apron transfer capacity growth and underinvestment archetype.
The auxiliary variable perceived need to invest in container terminals ship-to-
apron transfer capacity is calculated as a fractional difference between container
terminals capacity utilization and standard container terminals capacity utilization:
PERCEIVED NEED TO INVEST IN CONTAINER TERMINALS SHIP-TO-APRON TRANSFER
CAPACITY = (CONTAINER TERMINALS SHIP-TO-APRON TRANSFER CAPACITY UTILIZATION -
STANDARD CONTAINER TERMINALS SHIP-TO-APRON TRANSFER CAPACITY UTILIZATION) /
STANDARD CONTAINER TERMINALS SHIP-TO-APRON TRANSFER CAPACITY UTILIZATION [Units:
Dmnl]
The auxiliary variable investment in container terminals ship-to-apron transfer
capacity makes use of one of Vensim’s built-in functions to model delays (in this case, a
third order delay), in order to account for the fact that the decision to invest in container
terminals capacity is not immediate (this delay is one of the typical features of the
growth and underinvestment archetype). The author has admitted that a decision to
invest in terminal capacity takes 5 years to be made, including the time for the investors
to make the decision as well as the conclusion of the legal process and due authorization
to start the capacity building work itself:
INVESTMENT IN CONTAINER TERMINALS SHIP-TO-APRON TRANSFER CAPACITY =
DELAY3(PERCEIVED NEED TO INVEST IN CONTAINER TERMINALS SHIP-TO-APRON TRANSFER
CAPACITY,5) [Units: euros ]
Containerized
Cargo Port
Throughputcontainerized cargo
throughput growth rate
CONTAINER TERMINALS
RESOURCES AVAILABILITY
CONTAINER TERMINALS
SHIP-TO-APRON
TRANSFER RESOURCES
AVAILABILITY
CONTAINER TERMINALS
SHIP-TO-APRON
TRANSFER CAPACITY
UTILIZATION
Container
Terminals
Ship-to-apron
Transfer Capacitycontainer terminals
capacity growth rate
PERCEIVED NEED TO INVEST
IN CONTAINER TERMINALS
SHIP-TO-APRON TRANSFER
CAPACITY
STANDARD CONTAINER
TERMINALS SHIP-TO-APRON
TRANSFER CAPACITY
UTILIZATION
INVESTMENT IN
CONTAINER TERMINALS
SHIP-TO-APRON
TRANSFER CAPACITY
STANDARD CONTAINER
TERMINALS CAPACITY
UTILIZATION

18
The flow container terminals ship-to-apron transfer capacity growth rate uses
Vensim’s DELAY3 function, this time to take into account the time it takes to conclude
the work (i.e., increasing quay length, acquiring more cranes, etc.). The assumption is
that capacity building work takes 1.5 years to conclude:
container terminals ship-to-apron transfer capacity growth rate = DELAY3(INVESTMENT IN
CONTAINER TERMINALS SHIP-TO-APRON TRANSFER CAPACITY,1.5) [Units: thousand
TEUs/(Year*Year)]
As mentioned previously, levels of congestion affect what port users perceive as
the quality of service provided by the port. So, port demand growth rate (port
throughput growth rate) is considered to depend on the terminal resources availability,
which in turn depends on congestion levels:
CONTAINER TERMINALS SHIP-TO-APRON TRANSFER RESOURCES AVAILABILITY =
0.5/CONTAINER TERMINALS SHIP-TO-APRON TRANSFER CAPACITY UTILIZATION [Units: Dmnl]
The auxiliary variable container terminals resources availability is a function of
the resources availability of ship-to-apron transfer services, apron-to-storage transfer
services, yard storage service, storage-to-inland transport transfer service, and inland
transport processing service. It is considered to be the lowest value for the resources
availability for these five components (thus determining the choke-point resource
availability):
CONTAINER TERMINALS RESOURCES AVAILABILITY = MIN(MIN(MIN(MIN(CONTAINER
TERMINALS APRON-TO-STORAGE TRANSFER RESOURCES AVAILABILITY, CONTAINER
TERMINALS SHIP-TO-APRON TRANSFER RESOURCES AVAILABILITY), CONTAINER TERMINALS
STORAGE RESOURCES AVAILABILITY), CONTAINER TERMINALS STORAGE-TO-INLAND
TRANSPORT TRANSFER RESOURCES AVAILABILITY), CONTAINERIZED CARGO INLAND
TRANSPORT PROCESSING RESOURCES AVAILABILITY) [Units: Dmnl]
The modeling of apron-to-storage transfer capacity, yard storage capacity, storage-
to-inland transport transfer capacity, and inland transport processing capacity is not
described here because it is very similar to ship-to-apron transfer capacity. Annex II
contains the complete equations of the model.
The flow containerized cargo throughput growth rate is a function of container
terminals resources availability and containerized cargo port throughput itself. Note
that this is a step-by-step description of the model, so, naturally, containerized cargo
throughput growth rate is affected by other factors besides container terminals resources
availability and containerized cargo port demand, but those factors will be added later as
the equation for this variable is expanded). The use of the function DELAY3 in this
context is justified because port demand takes some time to adjust to port service

19
quality (appreciated here in terms of congestion levels, i.e., resources availability). It
was assumed that this adjustment takes 1 year to be made:
containerized cargo throughput growth rate = DELAY3(CONTAINER TERMINALS RESOURCES
AVAILABILITY,1)* Containerized Cargo Port Throughput [Units: thousand TEUs/(Year*Year)]
Both containerized cargo port throughput and container terminals ship-to-apron
transfer capacity are levels, and as such, are modeled as integrals:
Containerized Cargo Port Throughput = INTEG(containerized cargo throughput growth rate, 275)
[Units: thousand TEUs/Year]
Container Terminals Ship-to-Apron Transfer Capacity = INTEG(container terminals ship-to-apron
transfer capacity growth rate, 350) [Units: thousand TEUs/Year]
Separating container terminals capacity into five different components allows the
possibility of using different values for each component. For example, transshipment at
port of Lisbon represents less than 5% of the cargo, which means that storage-to-inland
transport transfer service, and inland transport processing service capacities must be
able to cope with the demand for the ship-to-apron transfer services, apron-to-storage
transfer services, and yard storage services, and so their capacities must be similar.
However, should transshipment cargo become important, storage-to-inland transport
transfer service, and inland transport processing service capacities would not need to be
as high as the other three capacity components.
The component storage capacity has a specific behavior that differs from the
other capacities and which will be examined in detail when the modeling process for
containers dwell time and pricing is discussed. This specific behavior and its
consequences can only be captured by separating the various terminal capacities.
2.2. Maximum capacity
2.2.1. Maximum installed capacity
An important consequence of separating terminal capacity in five distinct
components is the possibility of one having a higher capacity than another. To prevent a
situation of “wasted capacity” (since real capacity is imposed by the lowest value), the
model assumes that investments will be made in the other components to bring their
capacity up to the highest existing value. This is done by comparing actual installed
capacity for each component with the maximum installed capacity and closing the gap
through investments.

20
Figure 2.6 - Stock and flow diagram for Port of Lisbon's container terminals maximum
installed capacity.
The equation for maximum container terminals installed capacity is:
MAXIMUM CONTAINER TERMINALS INSTALLED CAPACITY = MAX(MAX(MAX(MAX("Container
Terminals Apron-to-Storage Transfer Capacity", "Container Terminals Ship-to-Apron Transfer
Capacity"),Container Terminals Storage Capacity),"Container Terminals Storage-to-Inland Transport
Transfer Capacity"),Containerized Cargo Inland Transport Processing Capacity) [Units: thousand
TEUs/Year ]
To prevent “wasted capacity”, if a given component’s capacity is already equal to
the highest installed capacity, investments are multiplied by 1 (no increase), whereas if
there is a difference, investments are multiplied by 2:
INVESTMENT IN CONTAINER TERMINALS SHIP-TO-APRON TRANSFER CAPACITY =
DELAY3("PERCEIVED NEED TO INVEST IN CONTAINER TERMINALS SHIP-TO-APRON TRANSFER
CAPACITY",5)*(IF THEN ELSE( "Container Terminals Ship-to-Apron Transfer Capacity"< MAXIMUM
CONTAINER TERMINALS INSTALLED CAPACITY, 2 , 1)) [Units: euros ]
2.2.2. Limits to container terminals capacity growth
Most ports eventually meet a limit to its expansion. This is particularly true for a
port surrounded by a city, such as the Port of Lisbon. In order to model this aspect, a
limit was imposed on the several components of terminal capacity, through the usage of
an auxiliary variable limits to container terminals capacity imposed by physical
constraints. It was assumed that this physical constraint is met at the 1.5 million TEUs
capacity. Figure 2.7 shows the corresponding stock and flow diagram.
<Container Terminals
Ship-to-Apron Transfer
Capacity>
Apron-to-Storage Transfer
Capacity>
Storage Capacity>
Storage-to-Inland Transport
Transfer Capacity>
<Containerized Cargo
Inland Transport
Processing Capacity>
MAXIMUM CONTAINER
TERMINALS INSTALLED
CAPACITY

21
Figure 2.7 - Stock and flow diagram for Port of Lisbon's container terminals physical
limit to expansion.
This limit is modeled with an if then else structure:
container terminals ship-to-apron transfer capacity growth rate = IF THEN ELSE("Container
Terminals Ship-to-Apron Transfer Capacity">LIMITS TO CONTAINER TERMINALS CAPACITY IMPOSED
BY PHYSICAL CONSTRAINTS,0,IF THEN ELSE(DELAY3("INVESTMENT IN CONTAINER TERMINALS
SHIP-TO-APRON TRANSFER CAPACITY",1.5)*200<0,0,DELAY3("INVESTMENT IN CONTAINER
TERMINALS SHIP-TO-APRON TRANSFER CAPACITY",1.5)*200)) [Units: thousand TEUs/(Year*Year)]
LIMITS TO CONTAINER TERMINALS CAPACITY IMPOSED BY PHYSICAL CONSTRAINTS =
1500 [Units: thousand TEUs/Year ]
2.3. Limits to growth for port demand
In a limits to growth situation, growing actions initially lead to success, which
encourages even more of these actions. Over time, however, the success itself causes the
system to encounter limits, which slows down improvements in results. As the success
triggers the limiting action and performance declines, the tendency is to focus even
more on the initial growing actions (Senge, 1990). Figure 2.8 shows the general causal
loop diagram for this archetype.
Figure 2.8 - Limits to growth archetype causal loop diagram.
Container
Terminals
Ship-to-apron
LIMITS TO CONTAINER
TERMINALS CAPACITY IMPOSED
BY PHYSICAL CONSTRAINTS
Efforts Performance Limiting Action
Constraint
++
+
-
+
R1 B2

22
A limits to growth scenario is encountered in every system possessing a given
capacity and a demand for that capacity. Given the right conditions, demand will tend to
rise until maximum operational capacity is met. Such is the situation, for example, in a
port, where there is an installed capacity, which acts as a limit for port demand.
Port demand (throughput) tends to increase until a limiting capacity is met
because maritime transportation makes use of economies of scale. So, the more the
quantity of goods a given shipping company moves through a port, the more there will
be a tendency for that quantity to increase, as the cost per unit cargo lowers.
Figure 2.9 presents the causal diagram for the limits to growth situation applied in
the context of port demand, while figure 2.10 presents the corresponding stock and flow
formulation.
Figure 2.9 - Limits to growth in port demand.
Figure 2.10 – Stock and flow diagram for limits to growth in port demand.
At any given time, the amount of cargo making use of the port can never be
greater than the installed port capacity, so maximum containerized cargo port demand is
a function of container terminals capacity:
MAXIMUM CONTAINERIZED CARGO PORT DEMAND = CONTAINER TERMINALS CAPACITY
[Units: thousand TEUs/Year ]
Containerized cargo
Containerized cargo
port throughput
Containerized cargo
gap fraction
Maximum
containerized cargo
port demand
Container
terminals capacity
+
+
+
-
R1 B2
+
+
MAXIMUM
CONTAINERIZED CARGO
PORT DEMAND
CONTAINERIZED
CARGO GAP FRACTION
containerized cargo
<CONTAINER
TERMINALS
CAPACITY>
Containerized
Cargo Port
Throughput

23
The auxiliary variable containerized cargo gap fraction limits demand growth rate
through a comparison between current containerized cargo port throughput and
maximum containerized cargo port demand:
CONTAINERIZED CARGO GAP FRACTION = (MAXIMUM CONTAINERIZED CARGO PORT
DEMAND- Containerized Cargo Port Throughput)/MAXIMUM CONTAINERIZED CARGO PORT DEMAND
[Units: Dmnl]
Finally, containerized cargo throughput growth rate depends on current port usage
levels (containerized cargo port throughput) and the limiting action of gap fraction and
the delay previously seen in the growth and underinvestment archetype:
Containerized Cargo Port Throughput = DELAY3((CONTAINER TERMINALS RESOURCES
AVAILABILITY),1)*Containerized Cargo Port Throughput*DELAY3(CONTAINERIZED CARGO GAP
FRACTION,0.1)+ALLOCATION OF RESOURCES TO CONTAINERIZED CARGO INSTEAD OF BREAK
BULK CARGO [Units: thousand TEUs/(Year*Year)]
Figure 2.11 illustrates the causal loop diagram for the Port of Lisbon, taking into
account the limits to growth archetype for port demand. Starting with the balancing loop
(B5), the more the containerized cargo port throughput, the less the containerized cargo
gap fraction (i.e., the less the difference between port demand and capacity). And the
less the containerized cargo gap fraction, the less the containerized cargo port
throughput.
Figure 2.11 - Causal loop diagram for the maximum containerized cargo port demand.
Maximum containerized cargo port demand is not an external input. It is an
endogenous variable, determined by container terminals capacity, making maximum
containerized cargo port demand part of a reinforcing loop (R4).
Containerized cargo
Containerized cargo
port throughput
+
+
R1
Container terminals
+
Container terminals
-
Container terminals
+
+
B2
Containerized cargo
gap fraction
-
+
B5
Perceived need to invest in
container terminals
ship-to-apron transfer capacity
+
Investment in container
terminals ship-to-apron
transfer capacity
+
Container terminals
capacity
+
-
B3
Container terminals
capacity
+
Maximum containerized
cargo port demand
+
+
R4

24
The more the container terminals capacity, the more the maximum containerized
cargo port demand, and as a consequence, the bigger the fractional difference between
maximum containerized cargo port demand and actual containerized cargo port
throughput (containerized cargo gap fraction), the greater the containerized cargo port
throughput will be. In turn, the more the containerized cargo port throughput, the more
the container terminals ship-to-apron transfer capacity utilization. And the more the
capacity utilization, the more the investment in port capacity, and hence, the more the
port capacity itself. Finally, the greater the port capacity, the greater the maximum port
demand, so closing the reinforcing loop (R4).
Figure 2.12 depicts the stock and flow diagram for the port of Lisbon, taking into
account the limits to growth archetype for containerized cargo port demand.
Figure 2.12 – Stock and flow diagram for maximum containerized cargo port demand.
2.4. Relation between containerized and break bulk cargo
In a success to the successful situation, two or more individuals, groups, projects,
initiatives, etc. are competing for a limited pool of resources to achieve success. If one
of them starts to become more successful (or if historically already more successful)
than the others, it tends to garner more resources, thereby increasing the likelihood of
continued success. Its initial success justifies devoting more resources while robbing the
other alternatives of resources and opportunities to build their own success, even if the
Containerized
Cargo Port
CONTAINERIZED
CARGO GAP FRACTION
MAXIMUM
CONTAINERIZED CARGO
PORT DEMAND
CONTAINER TERMINALS
RESOURCES
AVAILABILITY
CONTAINER TERMINALS
CONTAINER TERMINALS
CAPACITY UTILIZATION
Container
Terminals
Ship-to-apron
INVESTMENT IN CONTAINER
TRANSFER CAPACITY<Bargaining Power of
Maritime Transportation>
PERCEIVED NEED TO INVEST IN
CONTAINER TERMINALS
CAPACITY
STANDARD CONTAINER
TRANSFER CAPACITY
UTILIZATION
STANDARD CONTAINER
TERMINALS CAPACITY
UTILIZATION
CONTAINER
TERMINALS
CAPACITY

25
others are superior alternatives (Senge, 1990). Figure 2.13 represents the causal loop
diagram for the success to the successful archetype.
Figure 2.13 – Success to the successful archetype causal loop diagram.
The historical evolution of general cargo maritime transport (with the break bulk
cargo losing market share in favor of containerized cargo, year after year) corresponds
to a typical example of success to the successful archetype in maritime transport
industry. This situation is illustrated in figure 2.14 (loops R1 and R2). The more the
containerized cargo port throughput, the more the allocation of resources to
containerized cargo instead of break bulk cargo, and the more the allocation of
resources to containerized cargo instead of break bulk cargo the more the containerized
cargo port throughput, in this way closing the reinforcing loop R1.
Figure 2.14 – Success to the successful archetype for the relationship between
containerized and break bulk cargo.
Going through the reinforcing loop R2 and starting with allocation of resources to
containerized cargo instead of break bulk cargo, the higher the value of this variable,
the less the break bulk cargo port throughput, and finally, the more the break bulk cargo
port throughput, the less the allocation of resources to containerized cargo instead of
Success of A Success of B
Allocation of
Resources to A
Instead of B
Resources to A Resources to B
+
+
+
-
+
-
R1 R2
Allocation of resources to
containerized cargo instead of
break bulk cargo
Containerized Cargo
Port Throughput
Break Bulk Cargo
Port Throughput
-+
+ -
R1 R2

26
break bulk cargo, in this way closing the reinforcing loop R2. The two reinforcing loops
(R1 and R2) that compose this archetype work to increase containerized cargo
throughput at the expenses of break bulk cargo.
The stock and flow diagram for the success to the successful archetype relating
break bulk and containerized cargo is represented in figure 2.15.
Because the units for break bulk cargo port throughput is ton and the units for
containerized cargo port throughput is TEU, a comparison between both requires the
auxiliary variable average tons per teu.
The equations for this archetype are:
ALLOCATION OF RESOURCES TO CONTAINERIZED CARGO INSTEAD OF BREAK BULK
CARGO = 0.0005*(CONTAINERIZED CARGO PORT THROUGHPUT IN TONS-Break Bulk Cargo Port
Throughput) [Units: Dmnl]
containerized cargo throughput growth rate= ALLOCATION OF RESOURCES TO
CONTAINERIZED CARGO INSTEAD OF BREAK BULK CARGO [Units: thousand TEUs/(Year*Year)]
Containerized Cargo Port Throughput =INTEG(containerized cargo throughput growth rate, 275)
[Units: thousand TEUs/Year ]
break bulk cargo throughput growth rate = IF THEN ELSE(Break Bulk Cargo Port Throughput
<=0,0,- ALLOCATION OF RESOURCES TO CONTAINERIZED CARGO INSTEAD OF BREAK BULK
CARGO) [Units: thousand tons/(Year*Year) ]
Break Bulk Cargo Port Throughput =INTEG(break bulk cargo throughput growth rate, 525) [Units:
thousand tons/Year ]
AVERAGE TONS PER TEU =10 [Units: tons/TEU]
CONTAINERIZED CARGO PORT THROUGHPUT IN TONS = Containerized Cargo Port
Throughput * AVERAGE TONS PER TEU[Units: thousand tons/Year ]
Figure 2.15 - System dynamics structure for the success to the successful archetype
concerning the relationship between containerized and break bulk cargo
Break Bulk Cargo
Port Throughput
ALLOCATION OF RESOURCES TO
CONTAINERIZED CARGO
INSTEAD OF BREAK BULK CARGO
Containerized
Cargo Port
break bulk cargo
AVERAGE TONS
PER TEU
CONTAINERIZED CARGO
PORT THROUGHPUT IN
TONS

27
2.5. Pricing considerations
In this study, port prices were split in two major groups: those under the
responsibility of the Port Authority and those assignable to port operators. Also, prices
that have a direct effect both on port capacity and demand were separated from those
whose direct effect is felt only on port demand (although indirectly they also have an
effect in port capacity as explained below). Table 2.3 presents the price groups
considered.
Table 2.3 - Port price groups
Pricing body Direct Impact on Demand Direct Impact on Capacity
Port Operators
Yes (prices for loading and
unloading cargo, berthing,
storage prices, etc.)
Yes (storage prices)
Port Authority
Yes (Pilotage Fees, Port
Fees, etc.)
No
2.5.1. Prices considered in the model
The prices used in the model took into consideration those publicized by terminal
operators and port authority. The dwell times considered were obtained from the Port of
Lisbon strategic and development plan (CONSULMAR, 2007). Table 2.4 shows the
values for the referred prices and dwell times.
Table 2.4 - Prices and dwell times used in the model [source: CONSULMAR (2007)].
Average Share of
container terminal
Average Dwell
Time
[days/TEU]
Storage fee (as a
function of
average Dwell
Time)
[euros/TEU]
Loading/unloading
of TEU to/from
ship
[euros/TEU]
Loading/unloading
of TEU to/from
truck
[euros/TEU]
Alcântara
Container
Terminal
46.20% 5 2.08 113.74 27.81
Santa Apolónia
Container
Terminal
39.31% 6 1.38 106.9884 22.4556
Lisbon
Multipurpose
Terminal
14.49% 3.5 0 115.84 30.82
Weigthed average 5.2 1.50 111.39 26.14
Port authority prices, also obtained from the Port of Lisbon strategic and
development plan, are shown in table 2.5.

28
Table 2.5 - Port authority prices (Container terminals) used in the model[source:
CONSULMAR (2007)].
Port entrance fee
[euros/TEU]
Port operations
fees
[euros/TEU]
Weigthed average 6.39 74.90
Taking these values into account, the components of port price for container
terminals that were used in the model were the following:
REFERENCE CONTAINERS STORAGE PRICE = 1.50 [Units: euros /TEU]
REFERENCE CONTAINER TERMINAL OPERATORS PRICE = 111.39 + 26.14 [Units: euros
/TEU]
REFERENCE PORT AUTHORITY CONTAINERS PRICE = 6.39 + 74.90 [Units: euros /TEU]
2.5.2. The issue of dwell time: influence in capacity and demand
Yard or warehouse capacity can be viewed from a static or a dynamic perspective.
From a static perspective, capacity is the quantity of cargo units (tons or TEUs) that can
be stored at any given moment, with no concern for the time factor. However, when
assessing capacity from a dynamic point of view, the notion of dwell time is particularly
important, i.e., the average time goods remain at port facilities (container yard,
warehouses, etc.) before being either loaded on to a ship or picked up by a drayage firm
(usually by truck, but possibly also by train).
Dally and Maquire (1983) proposed a formula for the calculation of container
yard annual capacity:
CTEU=
TGS×H×DPA×W
D×Bf
Where the parameters are:
CTEU = TEU capacity per year [TEUs/year]
TGS = TEU ground slots [-]
H = Average stacking height of the containers [-] (TGS × H is the yard static capacity)
DPA = Working days per year [days]
W = number of working slots in container yard, expressed as a proportion (0 ≤ W ≤ 1) [-]
D = Dwell time per TEU [days]
Bf = Buffer storage factor to account for peak demand [-] (Bf ≥ 1)
So, for example, if a given facility has a static capacity of 1000 TEUs and an
average dwell time of 5 days (a typical figure), the dynamic capacity (per year) would
be calculated as:

29
ynamic capacity=
(days per year) 1000 (static capacity)
(dwell time)
= 000 T Us year
The same principle is valid for liquid and dry bulk cargoes, as well as for break
bulk cargo.
A brief examination of the formula proposed by Dally and Maquire (1983) for
container yard annual capacity is sufficient to conclude that yard or warehouse capacity
is heavily dependent on cargo dwell time. If, for example, cargo dwell time was to halve
its value, capacity would immediately be doubled, due to the inverse relation between
the two variables.
So, a general formula relating terminal capacity and cargo dwell time can be
written as shown:
CTEU =
f(x)
D
Where the parameters are:
CTEU/tons = terminal capacity expressed in TEUs or Tons per unit of time
D = Dwell time per TEU or Ton expressed in units of time
x = A vector of parameters dependent of the type of terminal under consideration
(containers, liquid or dry bulk cargo, break-bulk cargo)
From the point of view of the terminal operators, dwell time is essentially
determined by the price the terminal operators charge for having the cargo stored at
their premises. But notice that port demand is affected not only by capacity but also,
although generally to a lesser extent, by price. And this price includes the storage fees
charged by the terminals to cargo owners or their representatives.
Capacity is greatly determined by cargo dwell time. In turn, cargo dwell time is
primarily a function of the prices charged by terminal operators for having merchandise
stored at their premises. And prices charged by terminal operator will add to total port
costs which will influence port demand.
A change in dwell time affects only yard capacity, having no direct influence on
quay or gate capacity. A mere change in dwell time will have no impact in terminal
capacity (and as a consequence in port capacity), since this corresponds to the lesser of
quay, yard and gate capacity. The assumption is that an increase in yard capacity
through a change in dwell time brings investments in quay and gate capacity in order to
cope with that yard or warehouse capacity increase. Figure 2.16 represents the stock and
flow diagram for the influence of storage price in containers dwell time.

30
Figure 2.16 – Stock and flow diagram for the influence of storage price in containers
dwell time.
The auxiliary variable containers dwell time will change as a consequence of a
comparison made by cargo owners between current containers storage price and a
reference containers storage price. In other words, if containers storage price increases,
then, containers dwell time should be expected to decrease, while if containers storage
price decreases, containers dwell time should increase as it becomes cheaper to use port
yard or warehouse facilities as a buffer for stock keeping.
The magnitude of the change in dwell time depends on the fractional change in
containers storage price:
CONTAINERS STORAGE PRICE = 1.50 [Units: euros /TEU]
CONTAINERS STORAGE PRICE FRACTIONAL DIFFERENCE = (CONTAINERS STORAGE
PRICE-REFERENCE CONTAINERS STORAGE PRICE)/REFERENCE CONTAINERS STORAGE PRICE
[Units: Dmnl]
EFFECT OF STORAGE PRICE ON CONTAINERS DWELL TIME = 1-FRACTIONAL CHANGE IN
CONTAINERS STORAGE PRICE*0.025
The actual containers dwell time is the result of the action of the auxiliary variable
effect of storage price on containers dwell time:
CONTAINERS DWELL TIME = REFERENCE CONTAINERS DWELL TIME*EFFECT OF
STORAGE PRICE ON CONTAINERS DWELL TIME [Units: days ]
Reference Dwell Time is taken as 5.2 days in the case of container terminals,
because this is the weighted average for containers dwell time at the port of Lisbon.
REFERENCE CONTAINERS DWELL TIME = 5.2 [Units: days ]
The stock and flow diagram represented in figure 2.17 illustrates the influence of
dwell time in storage capacity. The other components of capacity (i.e., ship-to-apron
REFERENCE
CONTAINERS
DWELL TIME
CONTAINERS
DWELL TIME
REFERENCE
CONTAINERS
STORAGE PRICE
EFFECT OF
STORAGE PRICE
ON CONTAINERS
DWELL TIME
CONTAINERS
STORAGE PRICE
CONTAINERS STORAGE
PRICE FRACTIONAL
DIFFERENCE

31
transfer capacity, apron-to-storage transfer capacity, storage-to-inland transport transfer
capacity, and inland transport processing capacity) are not directly affected by dwell
time, but, as mentioned before, the model assumes that all the other components will
increase its own capacity – through investments in order to match that of the component
with the highest capacity (as previously explained, this is done through the auxiliary
variable maximum container terminals installed capacity).
Figure 2.17 – Stock and flow diagram for the influence of dwell time in storage
capacity.
There is an inverse relation between dwell time and terminal storage capacity. So,
a relative decrease (or increase) in cargo dwell time will increase (or decrease) terminal
storage capacity by the same relative amount:
CONTAINER TERMINALS CAPACITY CHANGE THROUGH DWELL TIME = REFERENCE
CONTAINERS DWELL TIME/CONTAINERS DWELL TIME [Units: Dmnl]
container terminals storage capacity growth rate = IF THEN ELSE(Container Terminals Storage
Capacity>LIMITS TO CONTAINER TERMINALS CAPACITY IMPOSED BY PHYSICAL
CONSTRAINTS,0,IF THEN ELSE(DELAY3(INVESTMENT IN CONTAINER TERMINALS STORAGE
CAPACITY,1.5)*CONTAINER TERMINALS CAPACITY CHANGE THROUGH DWELL
TIME*200<0,0,DELAY3(INVESTMENT IN CONTAINER TERMINALS STORAGE
CAPACITY,1.5)*CONTAINER TERMINALS CAPACITY CHANGE THROUGH DWELL TIME*200)) [Units:
TEUs/(Year*Year)]
The influence of storage price is not restricted to cargo dwell time, since it is a
part of total port price, and, as such, it also has a direct effect on port demand. Together
with Port authority port price and other prices charged by terminal operators (for
example, loading and unloading charges), cargo storage price makes up the Port price.
Figure 2.18 illustrates the stock and flow diagram for the impact of port price on
port demand (only shows auxiliary variables).
CONTAINER TERMINALS
CAPACITY CHANGE
THROUGH DWELL TIME
<CONTAINERS
DWELL TIME>
<REFERENCE
CONTAINERS
DWELL TIME>
Container Terminals
Storage Capacity
container terminals
storage capacity growth
rate

32
Figure 2.18 – Stock and flow diagram for the impact of port costs on port demand.
The equations for the represented variables are:
CONTAINERS STORAGE PRICE = 1.50 [Units: euros /TEU]
CONTAINER TERMINAL OPERATORS PRICE = 137.53 [Units: euros /TEU]
PORT AUTHORITY CONTAINERS PRICE = 81.29 [Units: euros /TEU]
CONTAINER TERMINALS PORT PRICE = CONTAINER TERMINALS OPERATORS
PRICE+CONTAINERS STORAGE PRICE+PORT AUTHORITY CONTAINERS PRICE [Units: euros /TEU]
REFERENCE CONTAINER TERMINAL OPERATORS PRICE = 137.53 [Units: euros /TEU]
REFERENCE CONTAINERS STORAGE PRICE = 1.50 [Units: euros /TEU]
REFERENCE PORT AUTHORITY CONTAINERS PRICE = 81.29 [Units: euros /TEU]
FRACTIONAL DIFFERENCE IN CONTAINER TERMINALS PORT PRICE = (CONTAINER
TERMINALS PORT PRICE-REFERENCE CONTAINER TERMINALS PORT PRICE)/REFERENCE
CONTAINER TERMINALS PORT PRICE [Units: Dmnl]
EFFECT OF PORT PRICE ON CONTAINER CARGO PORT DEMAND = -FRACTIONAL
DIFFERENCE IN CONTAINER TERMINALS PORT PRICE*Containerized Cargo Port Throughput*0.05
[Units: Dmnl]
2.6. Port revenues, port profits and investment in capacity
Port revenues are in direct dependence of cargo throughput. The more the cargo
throughput, the higher the revenues will be. This is illustrated in figure 2.19,
representing the evolution of TEUs throughput and revenues from sales and services
provided by the port of Lisbon.
As a consequence, higher investments can be made in order to increase port
capacity. In turn, an increase in port capacity will cause capacity utilization to decrease,
REFERENCE
CONTAINER TERMINAL
OPERATORS PRICE
REFERENCE PORT
AUTHORITY
CONTAINERS PRICE
EFFECT OF PORT
PRICE ON
CONTAINER
CARGO PORT
DEMAND
REFERENCE
CONTAINER
TERMINALS PORT
PRICE
<REFERENCE
CONTAINERS
STORAGE PRICE>
CONTAINERS
STORAGE PRICE
PORT AUTHORITY
CONTAINERS PRICE
CONTAINER
TERMINALS
OPERATORS PRICE
CONTAINER
TERMINALS PORT
PRICE
FRACTIONAL DIFFERENCE
PORT PRICE
Port Throughput>

33
which will cause resource availability to increase. Finally, an increase in resource
availability will be perceived as a positive factor for attracting more cargo to the port,
increasing port demand, which will lead to still higher revenues. This reasoning is
translated into a causal loop diagram in figure 2.20.
Figure 2.19 - Container throughput and revenues evolution (sales and services provided)
for the Port of Lisbon 2000-2010 [Source: APL (2001-2011)].
Figure 2.20 - Causal loop diagram for port revenues and investments.
-
10.00
20.00
30.00
40.00
50.00
60.00
0.00
100.00
200.00
300.00
400.00
500.00
600.00
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
SalesandServicesProvided
[millioneuros]
TEUs[thousandsTEUs]
TEUs [thousands TEUs] Sales and Services Provided [million euros]
Container terminals
Container terminals
+
Containerized cargo
port throughput
+
Revenue from
containerized cargo
+
Investment in container
terminals ship-to-apron
transfer capacity
+
Container terminals
capacity
Container terminals
-
-
+
R1

34
Figure 2.21- Stock-and-flow diagram for port revenue and investments.
Figure 2.21 presents the stock and flow diagram for the relation between port
revenue from containerized cargo and investments. For clarity, other variables
previously mentioned, and with no direct role in this relation, have been omitted here.
Port revenues were taken as a function of cargo throughput and port price for each
cargo type.
REVENUE FROM CONTAINERIZED CARGO = Containerized Cargo Port
Throughput*CONTAINER TERMINALS PORT PRICE*1000 [Units: euros ]
CONTAINER TERMINALS CAPACITY INVESTMENTS = INVESTMENT IN CONTAINER
TERMINALS APRON-TO-STORAGE TRANSFER CAPACITY + INVESTMENT IN CONTAINER
TERMINALS SHIP-TO-APRON TRANSFER CAPACITY + INVESTMENT IN CONTAINER TERMINALS
STORAGE CAPACITY + INVESTMENT IN CONTAINER TERMINALS STORAGE-TO-INLAND
TRANSPORT TRANSFER CAPACITY + INVESTMENT IN CONTAINERIZED CARGO INLAND
TRANSPORT PROCESSING CAPACITY [Units: euros ]
PROFITS FROM CONTAINERIZED CARGO = REVENUE FROM CONTAINERIZED CARGO-
CONTAINER TERMINALS CAPACITY INVESTMENTS [Units: euros ]
2.7. Impact of deepening the access channels and berths on containerized
cargo port demand
The deepening of port access channels and berths allows bigger ships to call the
port. The deepening of the port access channel and the dredging of the berths does not
affect port capacity directly. Port capacity, expressed as the maximum number of cargo
units (either TEUs or tons) that can be handled at port depends only on terminal
Container Terminals
Ship-to-Apron
Transfer Capacity
CONTAINER TERMINALS
SHIP-TO-APRON
TRANSFER CAPACITY
UTILIZATION
container terminals
PERCEIVED NEED TO INVEST
CAPACITY
STANDARD CONTAINER
TERMINALS
SHIP-TO-APRON
TRANSFER CAPACITY
UTILIZATION
INVESTMENT IN CONTAINER
TRANSFER CAPACITY
CONTAINER TERMINALS
<REVENUE FROM
CONTAINERIZED
CARGO>
STANDARD CONTAINER
TERMINALS CAPACITY
UTILIZATION
Port Throughput>

35
capacity (the lesser of quay, yard and gate capacity) and inland transport processing
capacity. However, since the deepening of the access channel allows bigger ships to use
the port, through the so-called economies of scale, average cost per cargo unit is
reduced. As Pearson (1988) points out: “irrespective of ship type, as the ship size
increases the ship costs at sea per ton or TEU decrease.” This reduction in shipping
costs that comes from choosing that port, will, in turn, increase port demand. The
magnitude of this increase will depend on the sensitivity of demand to that port’s costs.
Port entry charges (which include port dues, pilotage, towage, wharfage, and so
on) are dependent on ship size and the amount of cargo handled. When they are
assessed, however, on a “per TEU slot” basis there is little variation with ship size. In
their analyses of ship size economies, Goss and Jones (1982), Thorburn (1960), and
Pearson (1988) similarly chose to ignore these costs. While Jannson and Shneerson
(1987) gave some small recognition to these costs, they too admit that they are
relatively insignificant and could well be ignored.
But as mentioned when describing the growth and underinvestment archetype, an
increase in port demand will cause terminal operators to perceive a need for investment
in capacity in order to accommodate that extra demand. So, although the direct impact
of the channel deepening is limited to port demand through the reduction of costs, it
also has an indirect effect in port capacity.
Figure 2.22 shows the Causal Loop Diagram for the deepening of the port access
channels and berths. Moving through the reinforcing loop R1 and beginning at the
variable access channels and berths depth, an increase in the access channels depth will
lead to an increase in the draft of the ships calling the port (ship’s draft). This will be a
natural tendency since the bigger the ship’s draft, the more cargo units the ship is able to
carry (TEUs per container ship), and the more the cargo units that can be carried, the
less the shipping costs per TEU. Since shipping costs per TEU add to total container
port costs, the less the shipping costs per TEU, the less the total container port costs.
And, finally, the less the total container port costs, the more the containerized cargo
port throughput. An increase in Port demand arising from the deepening of the access
channels will lead to the perception of the linkage between the two, and, as a
consequence, pressure to increase access channels and berths depth will increase,
which, in turn, will promote a further deepening of the access channels (and so closing
the reinforcing loop R1).

36
Figure 2.22 - Causal Loop Diagram for the Deepening of the Access Channels and
Berths.
However, the depth of the access channels cannot increase indefinitely. There will
always be a limit, either imposed by the budget available for the dredging operations, or,
evidently, a limit imposed by the existing ships draft and capacity (the larger container
carriers sailing in 2010 had a draft of approximately 17 meters and it is not foreseeable
that this will change dramatically in the near future), or other policy reasoning. So, the
maximum value for maximum access channels and berth depths will be imposed by the
maximum draft of the existing ships, but it seems more reasonable to consider a lower
value, depending on the possibility of the Port of Lisbon to attract long-haul and
transshipment cargo (which makes use of bigger ships).
Figure 2.23 shows the stock and flow diagram for the deepening of the access
channels and berths. Variables more directly related to dry bulk cargo are shown
because the impact on this cargo type of deepening the access channel was also assessed
(see section 2.10).
For safety reasons, it is common practice to use an under keel clearance of 10% of
the ship’s draft (meaning that the access channels and berths depths should be the ship’s
draft plus 10% of that same draft). It was assumed that the unit cost of deepening the
access channel is 10 million euros per meter.
Containerized cargo
port throughput
Pressure to increase
access channels and
berths depth
Access channels and
berths depth
Ship's draft
TEUs per
container ship
Shipping costs
per TEU-
Total container
port costs
+
-
+
+
+
+
R1

37
Figure 2.23 – Stock and flow diagram for the impact of deepening the access channels
and berths at the Port of Lisbon.
The equations for the represented variables are:
UNDER KEEL CLEARANCE=0.1 [Units: Dmnl
INTENDED MAXIMUM DRAFT = 13.632 [Units: meters]
INTENDED ACCESS CHANNELS AND BERTH DEPTHS = INTENDED MAXIMUM
DRAFT+INTENDED MAXIMUM DRAFT*UNDER KEEL CLEARANCE [Units: meters]
ACCESS CHANNELS AND BERTH DEPTHS GAP FRACTION = (INTENDED ACCESS
CHANNELS AND BERTH DEPTHS-Access Channels and Berths Depth)/INTENDED ACCESS
CHANNELS AND BERTH DEPTHS [Units: Dmnl]
PRESSURE TO INCREASE ACCESS CHANNELS AND BERTHS DEPTH = Containerized Cargo
Port Throughput [Units: Dmnl]
change in access channels and berths depth = DELAY3(PRESSURE TO INCREASE ACCESS
CHANNELS AND BERTHS DEPTH,5)*ACCESS CHANNELS AND BERTH DEPTHS GAP FRACTION
[Units: meters/year]
Access Channels and Berths Depth = INTEG(change in access channels and berths depth,11)
[Units: meters]
SHIP'S DRAFT=Access Channels and Berths Depth-UNDER KEEL CLEARANCE*Access
Channels and Berths Depth [Units: meters]
INVESTMENT IN DREDGING ACCESS CHANNELS AND BERTHS = EUROS PER METER OF
DREDGING*change in access channels and berths depth [Units: euros]
EUROS PER METER OF DREDGING = 10e+006 [Units: euros/meter]
The flow change in access channels and berths depth is modeled with a DELAY3
function with a value of 5, assuming it takes 5 years to complete the dredging of the
Access Channels
and Berths Depth
change in access
channels and berths
depthPRESSURE TO INCREASE
ACCESS CHANNELS AND
BERTHS DEPTH
Port Throughput>
SHIP'S DRAFT
TEUS PER
CONTAINER SHIPSHIPPING COSTS
PER TEU
INTENDED ACCESS
CHANNELS AND BERTH
DEPTHS ACCESS CHANNELS AND
BERTH DEPTHS GAP
FRACTION
UNDER KEEL
CLEARANCE
<UNDER KEEL
CLEARANCE>
INTENDED
MAXIMUM DRAFT
DEADWEIGHT PER
BULK CARRIER
SHIPPING COSTS PER
TONNE OF DRY BULK
CARGO TRANSPORTED
<Dry Bulk Cargo
Port Throughput>
EUROS PER METER
OF DREDGING
INVESTMENT IN
DREDGING ACCESS
CHANNELS AND BERTHS
<MANAGEMENT POLICY
FOR INTENDED
MAXIMUM DRAFT>
INITIAL
CHANNEL DEPTH
REFERENCE
SHIPPING COSTS PER
TEU
<SHIPPING COSTS
PER TEU>
EFFECT OF SHIPPING
COSTS PER TEU ON
PORT DEMAND
SHIPPING COSTS PER TEU
FRACTIONAL DIFFERENCE
Port Throughput>

38
access channels and berths to the new depth, including the delay for the investment
decision to be made.
There is a direct relation between a ship’s draft and its cargo capacity. So, once a
container carrier’s draft is known, the average number of nominal TEUs (NTEUs, based
on the standard assumption of 14 DWT per TEU) it can carry is also known. According
to Cullinane and Khanna (1998: p.189):
Although yard-denominated TEU slot capacity is the most frequently used basis for
comparing container ship sizes, Meckel (1985) strongly criticizes this practice because
a ship’s carrying capacity, as determined by its TEU slots, may well be constrained by
the highly variable influence of available DWT per TEU slot. Both he and Lloyds
Shipping Economist (1996a) advocate using the nominal TEU (NTEU) measure of a
container ship’s carrying capacity (and ultimately its slot costs), based on the standard
assumption of 14 DWT per TEU. This standard is supported by the fact that the
average payload per TEU is about 10 tonnes (Drewry Shipping Consultants, 1995), the
tare weight of each container is about 2.3 tonnes, and some part of the vessel’s total
deadweight is allocated to bunkers, fresh water, spares, and supplies. A comparison of
the NTEU of containerships is fundamentally, therefore, a comparison of DWT, except
that a concept is used (that is, the number of containers that can be carried), which is
much easier to relate to.
A polynomial regression line can be fit to quantify this relation. Such a regression
is represented in figure 2.24, where the cargo capacity (in NTEUs) is regressed against
the ship’s draft (in meters). The data for the regression was obtained from a web based
ship database (Goes, 2012).
Figure 2.24 - Relation between the ship's draft and cargo capacity [adapted from Goes
(2012)].
y = 0.1011x6 - 5.387x5 + 115.13x4 - 1251.5x3 + 7277.8x2 - 21239x + 24095
R² = 0.9779
0
2 000
4 000
6 000
8 000
10 000
12 000
14 000
16 000
18 000
20 000
0 2 4 6 8 10 12 14 16 18
Cargo Capacity [NTEUs]
Ship's Draft [m]

39
The regression is easily introduced in the model:
TEUS PER CONTAINER SHIP = 0.1011*(SHIP'S DRAFT^6)- 5.387*(SHIP'S
DRAFT^5)+115.13*(SHIP'S DRAFT^4)-1251.5*(SHIP'S DRAFT^3)+7277.8*(SHIP'S DRAFT^2)-
21239*SHIP'S DRAFT+24095 [Units: TEUs/ship]
Cullinane and Khanna (1998) studied the impact of the distance travelled and
quantity of TEUs carried on total shipping costs. Three main groups of voyages were
covered in the referred study: trans-atlantic voyages (4 000 miles), trans-pacific voyages
(8 000 miles), and Europe-Far-East voyages (11 500 miles). Figure 2.25 presents the
results of that study for total shipping costs per TEU as a function of the number of
TEUs carried and voyage distance.
Figure 2.25 - Voyage costs (in US$) as a function of ship capacity and distance travelled
[adapted from Cullinane and Khanna (1998)].
One of the objectives of assessing the impact of deepening the access channels
and berths is to evaluate the possibility of capturing cargo destined to other ports but
with a final destination located at the hinterland served by the Port of Lisbon.
According to Neves and Mendes (2010), some 300 million euros are spent each
year in road freights from Europe ports to Portugal, for cargo that often sails pass the
Portuguese coast (with origin in the Far-East and heading to a Northern Europe port
before being loaded on to a truck for its final destination in Portugal).
In this context, emphasis was put on the Europe-Far East route. Moreover, as
figure 2.25 indicates, voyage costs exhibit a greater change with NTEU capacity when
the travel distance is greater, so it is likely that it would be the Europe-Far East route
that would respond more intensively to a change in ships’ capacity. For this reason, the
costs used in the model were those attributed to this route.
The modeling of shipping costs per cargo unit as a function of cargo units per ship
is achieved through the use of Vensim’s lookup function.
00
100
200
300
400
500
600
700
800
900
0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000
VOYAGECOSTSPERTEUINUS$
CAPACITY IN NTEU
4 000 miles (Trans-Atlantic)
8 000 miles (Trans-Pacific)
11 500 Miles (Europe-Far East)

Analysis of investment policies for the Port of Lisbon with a System Dynamics model

Analysis of investment policies for the Port of Lisbon with a System Dynamics model

Recommended

Recommended

More Related Content

Similar to Analysis of investment policies for the Port of Lisbon with a System Dynamics model

Similar to Analysis of investment policies for the Port of Lisbon with a System Dynamics model (20)

More from Cláudio Carneiro

More from Cláudio Carneiro (20)

Recently uploaded

Recently uploaded (20)

Analysis of investment policies for the Port of Lisbon with a System Dynamics model