Associate Professor Branislav DRAGOVIC1

1
MODELING OF SHIP-BERTH-YARD LINK
PERFORMANCE AND THROUGHPUT OPTIMISATION
Associate Professor Branislav DRAGOVIC1
1)Maritime Faculty, University of Montenegro,
Maritime Transport Traffic Division Dobrota 36,
85330 Kotor, Montenegro, branod_at_cg.ac.yu,
bdragovic_at_cg.yu www.users.cg.yu/bdragovic

Professor Nam Kyu PARK2 2)Department of
Distribution Management, Tongmyong University of
Information Technology, Korea Professor Zoran
RADMILOVIC3 3)Faculty of Traffic and Transport
Engineering, University of Belgrade, Serbia
2
This paper presents a ship-berth-yard link
modeling methodology based on statistical
analysis of container ship traffic data obtained
from the Pusan East Container Terminal (PECT).
The efficiency of operations and processes on the
ship-berth link has been analyzed through the
basic operating parameters such as berth
utilization, average number of ships in waiting
line, average time that ship spend in waiting
line, average service time of ship, average total
time that ship spend in port, average quay crane
(QC) productivity and average number of QCs per
ship.
3

• The rest of this lecture is organized as follow
• In the next section we will provide an overview
  of the literature related to the
• port simulation and analytical models and
  especially ship-berth-yard link models.
• Following section presents brief description of
  ship-berth-yard link simulation and analytical
  modeling procedure, consisting of model
  structure, data collection and applied simulation
  algorithm flowchart.
• This is followed by the next section which gives
  model validation and
• simulation analysis of ship-berth link at
  PECT.
• Following section presents two types of models
  that are developed on the basis of the queuing
  theory mathematical model and simulation model.
  Results from both models are compared with each
  other. The mathematical model has fewer inputs
  and requires less computational times, whilst the
  simulation model can handle more practical
  situations with more manipulated variables and
  less constraints.
• In conclusions we draw and incorporate
  suggestions to continue research on
• ship-berth-yard link performance

4
The basic approach used consists of two models.
The first is a simulation model adapted to the
problem of analyzing ship movements in port. The
second model applies the results of the queueing
model to an analytically formulated average
container ship cost function in port. The aim of
this function is to minimize average container
ship costs in port, including the allocation
planning of berths/terminal and quay
cranes/berth. Numerical results and computational
experiments are reported to evaluate a study on
the improvement of the calculation system of
optimal throughput per berth for PECT. As a
ship-berth-yard link at a container terminal is
the large and complex system, a performance model
has to be developed. This can be an analytical
model, which uses mathematical concepts and
mathematical notations to describe the processes
at the ship-berth-yard link. In contrast with it,
a simulation model is basically a computer
program, which mimics the important aspects of
the studied link.
5
Table 1 Literature review of a container port
and ship-berth link planning
There are few studies dealing with ship-berth
link planning. Researches related to a container
port and particularly ship-berth link planning,
which use simulation, are summarized in Table 1.
6
Table 1. Continue
7
PROBLEM STATEMENT
The crucial terminal management problem is
optimizing the balance between the shipowners who
request quick service of their ships and
economical use of allocated resources. Since both
container ships and container port facilities are
very expensive, it is desirable to utilize them
as intensively as possible. Main problem in
analytical modeling of container terminal relates
to the fact that they lose in detail and
flexibility, so they simplify the real situation.
On the other hand, simulation modeling is better
than analytical one in representing random and
complex environment of container terminal.
A simulation model of a container terminal is
basically a computer program written in a general
purpose language or in a special
simulation-oriented language. The different types
of simulation languages that have been used for
modeling of the processes at the ship-berth link
include MODSIM III, AweSim, Arena, Extend,
Witness, GPSS/H. The simulation models are used
to analyze queuing and bottleneck problems,
container handling techniques, truck and vessel
scheduling (departure and arrival rates),
equipment utilization, and port throughput and
operational efficiency (yard, gate and berth).
So, a simulation implements the most important
aspects of the processes at the container
terminal, often in a simplified manner. However,
the advantage of simulation modeling over
analytical modeling of container terminal is that
it allows for a greater level of detail and to
avoid too many simplifications.
8
Analytical modeling of container terminal
consists of setting up mathematical models and
equations which describe certain stages in the
functioning of the system. Specifically, the
probabilistic models are, often, used to describe
the evolution of these systems in the process of
its modeling. This choice accounts for the fact
that the events like ship arrivals, service time,
waiting time, etc., at the container terminal are
often unpredictable, and hence assumed to be
random. The big advantage of analytical modeling
is that it requires a thorough understanding of
the system. The biggest disadvantage in
analytical modeling of ship-berth link is that
many related processes are too complex to be in
reach of analytical methods. Therefore, a lot of
simplifications and approximations have to be
made during the modeling process, which lessens
the accuracy of the results. However, often
analytical models can give a rough feeling for
the influence of certain factors on the
performance measures at considered ship-berth
link. A second disadvantage is that the analyst
needs to know the necessary mathematics very
well, including their respective abilities to
model processes at ship-berth link.
In Table 2 we give a brief qualitative comparison
of the simulation and analytical techniques for
performance evaluation of ship-berth link.
9
Table 2. Qualitative comparison of the simulation
and analytical techniques for
performance evaluation in port
10
SIMULATION MODELING OF SHIP-BERTH LINK
PERFORMANCE
Generally speaking, we can realize the simulation
modeling by using GPSL or GPPL.
11
Ship-berth link is complex due to different
interarrival times of ships, different dimensions
of ships, multiple quays and berths, different
capabilities of QC and so on. The modeling of
these systems must be divided into several
segments, each of which has its own specific
input parameters. These segments are closely
connected with the stages in ship service
presented in Figure 1.
Figure 1. Port operation with ship movement in
port and process flow diagram of the terminal
transport operations
12
(No Transcript)
13
Figure 2. Flowchart for a ship
arrival/departure
14
LOGIC OF ALGORITHM FOR SIMULATION MODEL
Second come
Berths are not available! Wait in queue!
First class prioritiy
Compare priorities
Higher
Berth 4 available!!!
Berths are not available! Wait in queue!
First come
Second class prioritiy
Cranes are available!!!
Service completed
Service completed
There is no crane available! Wait for crane!
Service completed
Berth 1
Berth 2
Berth 3
Berth 4
15
Most container terminal systems are sufficiently
complex to warrant simulation analysis to
determine systems performance. The GPSS/H
simulation language, specifically designed for
the simulation of manufacturing and queueing
systems, has been used in this paper (Schriber,
1991). In order to present the ship-berth link
processes as accurate as possible the following
phases need to be included into simulation model
(Dragovic et al. 2005a,b 2006a,b) Model
structure Ship-berth-yard link is complex due to
different interarrival times of ships, different
dimensions of ships, multiple quays and berths,
different capabilities of QCs and so on. The
modeling of these systems must be divided into
several segments, each of which has its own
specific input parameters. These segments are
closely connected with the stages of ship service
(Figure 1). Data collection All input values of
parameters within each segment are based on data
collected in the context of this research. The
main input data consists of ship interarrival
times, lifts per ship, number of allocated QCs
per ship call, and QC productivity. Existing
input data are subsequently aggregated and
analyzed so that an accurate simulation algorithm
is created in order to evaluate ship-berth-yard
link parameters. Inter-arrival times of ships
The inter-arrival time distribution is a basic
input parameter that has to be assumed or
inferred from observed data. The most commonly
assumed distributions in literature are the
exponential distribution (Demirci 2003 Pachakis
and Kiremidjian 2003 Dragovic et al. 2006a,b)
the negative exponential distribution (Shabayek
and Yeung 2002) or the Weibull distribution
(Tahar and Hussain 2000 Dragovic et al. 2005a,b).
16
Loading and unloading stage Accurate
representation of number of lifts per ship call
is one of the basic tasks of ship-berth link
modeling procedure. It means that, in accordance
with the division of ships in different classes,
the distribution corresponding to those classes
has to be determined. Number of QCs per ship
The data available on the use of QCs in
ship-berth link operations have to be considered
too, as this is another significant issue in the
service of ships. This is especially important as
total ship service time depends not only on the
number of lifts but also on the number of QCs
allocated per ship. Different rules and
relationships can be used in order to determinate
adequate number of QCs per ship. On the other
hand, in simulation models, it is enough to
determine the probability distribution of various
numbers of QCs assigned per ship. Flowchart Upon
arrival, a ship needs to be assigned a berth
along the quay. The objective of berth allocation
is to assign the ship to an optimum position,
while minimizing costs, such as berth resources
(Frankel 1987). After the input parameter is
read, simulation starts by generating ship
arrivals according to the stipulated
distribution. Next, the ship size is determined
from an empirical distribution. Then, the
priority of the ship is assigned depending on its
size. The ship size is important for making the
ship service priority strategies. For the assumed
number of lifts per ship to be processed, the
number of QCs to be requested is chosen from
empirical distribution. If there is no ship in
the queue, the available berths are allocated to
each arriving ship. In other cases ships are put
in queue. The first come first served principle
is employed for the ships without priority and
ships from the same class with priority. After
berthing, a ship is assigned the requested number
of QCs. In case all QCs are busy, the ship is put
in queue for QCs. Finally, after completion of
the loading and unloading process, the ship
leaves the port. This procedure is presented in
the algorithm shown in Figure 2.
17
In order to calculate the ship-berth-yard
performance, it is essential to have a through
understanding of the most important elements in a
port system including ship berthing/unberthing,
crane allocation per ship, yard tractor
allocation to a container and crane allocation in
stacking area. As described in Figure 1 - process
flow diagram of the terminal transport
operations, the scope of simulation, strategy and
initial value and performance measure will have
to be defined. In addition, the operational
aspect such as machine failures having a direct
impact on ship, crane and vehicle will have to be
considered. To move containers from apron to
stacking area, four tractors are provided for
each container crane. It takes 3.15 minutes from
apron to stacking area including
unloading/loading time by transfer crane. The
distance between apron and stacking area is
assumed to be 700 meters.
18
ANALYTICAL MODELING OF SHIP-BERTH LINK
PERFORMANCE
Queueing theory (QT) models for analyzing
movements of ships in port is proposed and shown
in Fig.1.
In the analysis of various aspects of average
time that ships spend in port, tws, including ns,
nb, ?, ?, nc and ?, (e.g., ?Plumlee (1966),
Nicolaou (1967, 1969), Wanhill (1974), Noritake
(1985), Noritake and Kimura (1983,1990), Shabauek
and Yeung (2001), Taniguchi et al. (1999))
defined tws as the sum of the average waiting
time and average service time.
The average service time,
where
includes ships loading/unloading time in hours
per containership, tc, expressed as
(1)
(2)
It follows that
(3)
19
Further, it can be shown that
(4)
where
(5)
for the (M/M/nb) model.
For minimizing tws, the Eq. (5) can be
transformed in the form
(6)
Also, the Eq. (3) becomes
(7)
20
On the other side, the difference equations in
the steady-state condition which were obtained by
Morse (1958) refer to the (M/Ek/nb) model. But,
there is no theoretical formula which concerns
the average time that ships spend in port. Only
some approximation formulae exist, which relate
the average waiting time of ships in the
(M/Ek/nb) model to that in the (M/M/nb) model. In
this study, formulae due to Lee and Longton
(1959) and Cosmetatos (1975, 1976) have been
adopted relation to average port waiting time of
ships (Noritake (1985), Noritake and Kimura
(1983,1990), Radmilovic (1992) and Taniguchi et
al. (1999)). Accordingly with it, when the ships
service time has an Erlang distribution with k
phases, the following equations are obtained
(8)
the coefficient of variation of ships service
time distribution and k the number of phases
of an Erlang distribution
(9)
The Eqs. (8) related to ((M/Ek/nb)I) and (9)
related to ((M/Ek/nb)II) for the (M/Ek/nb) model
present average time that ships spent in port as
a function of ?.
21
BERTH UTILIZATION FACTOR
(12)
SHIP TRAFFIC INTENSITY
Further, ? as a port operation parameter, i.e.
berth occupancy index, can be defined in the
following manner (Nicolaou (1967 and 1969)
Noritake (1983)).
(13)
Furthermore, there holds
(14)
Then, the average number of ships present in port
with nb berths in the period T is expressed as
(15)
Also, average number of ships waiting for berths
with nb berths in the period T is obtained as
(16)
It follows from (15) and (16) that average number
of ships served at nb berths in the period T can
be written in the form
(17)
22
In view of that
the Eq. (15) becomes
(18)
or
(19)
From Eqs. (13), (14) and (19) we have
(20)
AVERAGE TIME THAT SHIPS SPEND IN PORT
The substitution of Eq. (12) into Eq. (8) yields
(22)
for the (M/M/nb)(FCFS/?/?) model, and hence by
(4) we have
(23)
23
(24)
(25)
In order to write tws from (4) as a function of ?
in the form
(27)
we substitute (20) into (8) to obtain
(30)
for the (M/M/nb)(FCFS/?/?) model.
When the service time of ships obeys the Erlang
distribution with k phases, the following
equations are obtained by substitution Eq. (30)
into Eqs. (10) and (11), respectively
(31)
(32)
24
SHIP LOADING/UNLOADING OPERATIONS MODELING
In general, this model integrates main actual
operations of the container terminal by
simplifying complex activities, and these
operations are defined according to ship class.
In this section, various objects were observed in
the real terminal and model elements. Model
elements of the container terminal can be
separated into follow group
- berth cost in per hour,
- QCs cost in per hour,
- storage yards cost in per hour,
- transportation cost by yard transport
equipment between quayside and storage yard in
per hour
- labor cost for QC gangs in per hour,
- ships cost in port in per hour,

• containers cost and its contents
• in per hour

The total cost function, would be concerned with
the combined terminals and containerships cost
as
25
It is necessary to know that only the total port
cost function computes the number of
berths/terminal and QCs/berth that would satisfy
the basic premise that the service port cost plus
the cost of ships in port should be at a minimum.
This function was introduced by Schonfeld and
Sharafeldien (1985). We point out that their
solutions may not be as good as ours because we
have simulation approach to determine key
parameters tw, t s, ?, ?, ? and especially kc.
Therefore, to find the optimal solution, their
function can be obtained in the following form
(33)
where TC - total port system costs in /hour.
By substituting the Eq. (9) into Eq. (33) yields
(34)
where tws (?) is defined by the Eq. (4) or the
Eq. (31) or the Eq. (32) or it is a result of
simulation modeling.
26
From the total port cost function per average
arrival rate, we can obtain
(35)
Since ? ??, we get
(36)
or because of by the Eq. 12), ? ?nb?, the Eq.
(36) also has the form
(37)
Eqs. (35), (36) and (37) show the average
container ship cost in /ship, AC. In this study,
the trade-off will be simulative and analytically
resolved by minimizing the sum of the relevant
cost components associated with the number of
berths/terminal and QCs/berth, and average
arrival rate. These three parameters are key to
the analysis of facility utilization and
achieving major improvements in container port
efficiency, increasing terminal throughput,
minimizing terminal traffic congestion and
reducing re-handling time. A reduction in
operating cost can be achieved by jointly
optimizing these parameters. In solving the
berths/terminal and QCs/berth, analysts and
planners are concerned primarily with the average
time that ships spend in port and the average
cost per ship serviced.
27
NUMERICAL EXAMPLES WITH EXPERIMENTAL
STRATEGY This section gives a ship-berth link
modeling methodology based on statistical
analysis of container ship traffic data obtained
from the PECT. PECT is big container terminals
with a capacity of 1,963,304 twenty foot
equivalent units (TEU) in 2004. There are four
berths with total quay length of 1,200 m and
draft around 14-15 m. Ships of each class can be
serviced at each berth.
Figure 3. PECT layout
28
INPUT DATA An important part of the model
implementation is the correct choice of the
values of the simulation parameters. The input
data for the both simulation and analytical
models are based on the actual ship arrivals at
the PECT for the six months period from September
6, 2004 to February 27, 2005, which involves 711
ship calls, see Table 3 (PECT Management
reports). The ships were categorized into the
following three classes according to the number
of lifts made per each ship first class consists
of ships with less than 500 lifts made, second
class of 501 1,000 lifts and third class are
those with more than 1,000 containers
loaded/unloaded per ship. Ship arrival
probabilities are as follows 28.1 for first
class, 42.3 for second class and 29.6 for third
class. The ship arrival rate is 0.175 ships/hour.
The total throughput during the considered period
was 979,655 TEU. Also, the berthing/unberthing
time of ships is considered to be 1 hour.
29
Table Input data - Ship characteristics
Note T1 Scheduled time T2 Time of arrival
Cs Capacity of ships in TEU L Ship length
nc Number of QCs assigned per ship PQC
Productivity of QCs
30
The assignment of QCs per ship was assumed random
with probabilities equal to the percentages of
number of QCs that was allocated for ship
servicing. Therefore, the results of analysis of
frequencies of QCs assignment per ship expressed
in are given in Table 4. For first class of
ships (under 500 lifts), 15 of total ships is
given 1 QC, 66.5 are given 2 QCs, 17 are given
3 QCs and 1.5 are given 4 QCs. For second class
of ships, 2 QC are assigned for servicing in 23
cases, 3 QCs in 67.3, 4 QCs in 9.4 and 5 QCs in
0.3 cases. The data for third class of ships are
as follows 2 QCs in 1.9 cases, 3 QCs in 47.7
cases, 4 QCs in 43.8 cases and 5, 6 and 7 QC are
assigned in 4.3, 1.9 and 0.4 cases,
respectively. Furthermore, average QCs
productivity given in lifts per hour is shown in
Table 5.
Table No. of QCs assigned per ship in
31
The inter-arrival time distribution of ships at
the PECT was plotted in the Figure 3. It is found
that even though the arrivals of the ships at the
PECT, taking the whole period of six months and
each class of ships, are scheduled and not
random, the distribution of inter-arrival times
fitted very well exponential distribution.
In order to obtain punctual data, we have done
fitting of empirical distribution of service
times of ships with appropriate theoretical
distribution for each class of ships. It is
observed that service time of first class of
ships follows 7-stages Erlang distribution
(Figure 4), while 12-stages Erlang distribution
fits very well the service time of second class
of ships (Figure 5). Finally, service time of
third class of ships follows the 3-stages Erlang
distribution (Figure 6). The distribution types
of service time for each class of ships are given
in Figures 4 - 6. Goodness of fit was evaluated,
for all tested data, by both chi-square and
Kolmogorov-Smirnov tests at a 5 significance
level.
32
Figure 3. Distribution of ships inter-arrival
times (IAT) at PECT
Figure 4. Service distribution of first class of
ships (the 7-stage Erlang distribution)
Figure 5. Service distribution of second class
of ships (the 12-stage Erlang distribution)
Figure 6. Service distribution of third class
of ships (the 3-stage Erlang distribution)
33
We have carried out extensive numerical work for
high/low values of the PECT model
characteristics. Our numerical experiments are
based on different parameters of various PECT
characteristics such as number of containers
loading/unloading from containership, the QC move
time, hourly berth cost, average yard container
dwell time, transportation cost by yard transport
equipment between quayside and storage yard,
number of m2 of storage yard per container,
storage yard cost, paid labor time, labor cost,
ship cost in port and average payload of
containers, presented in Table 6 (PECT Management
reports, Korea Maritime Institute (1996)). The
described and tested numerical experiments
contain four segments in relation to the input
variables.
Table 3. Input data Terminal characteristics
nc - average number of QCs assigned per ship
(Real data and Simulation resluts) cnb 62
million i .0663 ny - 40, cnbm 6.2 million
cnb 1215 cnc 38.8 /QC hour ttcon
188 hours aconcy 63.9 m2/container ccy
0.000292 /m2 hour cl 357 /gang hour cw 1.4
/container hour.
34
VALIDATION-VERIFICATION
For purposes of validation of simulation model
and verification of simulation computer program,
the results of simulation model were compared
with the actual measurement. Four statistics were
used as a comparison between simulation output
and real data traffic intensity, berth
utilization, average service time and average
number of serviced ships. The simulation model
was run for 40 statistically independent
replications. The average results were recorded
and used in comparisons. After analysis of the
port data, it was determined that traffic
intensity and berth utilization are about 2.573
and 64.34, while the simulation output shows the
value of 2.564 and 64.12, respectively, see
Table 7. Average service time shows very little
difference between the simulation results and
actual data, that is, 15.12 h and 15.20 h,
respectively (Table 4). The simulation results of
the number of serviced ships completely
correspond with the real data (i.e. the
simulation result of the total number of ships is
712.3 and the real data is 711 the first class
of ships 201.25 and 201 the second class
301.75 and 301 and the third class 209.3 and
210), see Table 5. All the above shows that
simulation results are in agreement with real
data.
35
VALIDATION-VERIFICATION
Table Number of ships serviced in simulation
period September 6, 2004 to February 27, 2005
Analitical,
Table Average service time of ships, traffic
intensity and berths utilisation
36
RESULTS
The impact of different models is determined by
comparing the key performance measures of
simulation and analytical approaches to those of
the real data of PECT. Table 4 displays the
results, the key measures are average traffic
intensity, berth utilisation and average service
time of ships (all classes, first class, second
class and third class), while Table 6 shows
average time that ships spend in queue (all
classes, first class, second class and third
class). In addition, Table 7 gives average time
that ships spend in port (all classes, I class,
II class and III class). According to this,
judging from the computational results for some
numerical examples of the models (M/Ek/nb)I
using the average waiting time given by Eq. (10)
(for brevity, the analytical Model I is denoted
as AM I) and (M/Ek/nb)II using average waiting
time given by Eq. (11) (for brevity, the
analytical Model II is denoted as AM II). It can
be confirmed that the Eq. (10) is inclined to
estimate the values of average time that
container ships spend in port, i.e. average
waiting time of ships.
37
RESULTS
Table 9. Average time that ships spend in queue
Table 10. Average time that ships spend in port
38
AVERAGE CONTAINER SHIP COST
39
Figure 7. Average container ship costs for
various traffic intensity (? 0.5-3.5) - Minimum
AC per ship first class are 50,202 for SM
50,754 for AM I and 50,908 for AM II
Figure 8. Average container ship costs for
various traffic intensity (? 0.5-3.5) - Minimum
AC per ship second class are 96,008 for SM
96,405 for AM I and 96,769 for AM II
40
Figure 9. Average container ship costs for
various traffic intensity (? 0.5-3.5) - Minimum
AC per ship third class are 149,621 for SM
149,329 for AM I and 149,536 for AM II
Figure 10. Average container ship costs for
various traffic intensity (? 0.5-3.5) - Minimum
AC per ship third class are 101,990 for SM
101,323 for AM I and 101,536 for AM II
41
Figs. 11 16 show the optimization function AC
of two variables nb (nb 3 , 4, 5) and nc (nc
1 , 2,, 7) for constant value of ?. In Fig. 12
obtained results correspond to those from Fig. 7,
in Fig. 14 to results from Fig. 8, and in Fig. 16
to those in Fig. 19. Still, even in Fig. 12, the
study offers similar results, i.e. the minimum
average cost per ship served are 50,202 in
relation to 50,202 from Fig. 7 curve AM I.
These results will emphasize the effects of
terminal and traffic intensity, average time that
ships spend in port, numbers of QCs/berth, QC
productivity and numbers of berths/terminal.
These five parameters are keys to the analysis of
the whole container port efficiency and
achievement of economies of scale. However, major
improvements in port productivity, quality of
service and costs reduction can be achieved by
joint optimizing these variables.
42
Figure 11. Average container ship costs for
various berths/terminal (nb 3,4,5) and
QCs/berth (nc 1,2,,7) Minimum AC per first
class of ships is 52,160 for AM I (? 2.85) nb
4 and nc 2.05
43
Figure 12. Average container ship costs for
various berths/terminal (nb 3,4,5) and
QCs/berth (nc 1,2,,7) Minimum AC per first
class of ships is 50,202 for AM I (? 2.95) nb
4 and nc 2.5
44
Figure 13. Average container ship costs for
various berths/terminal (nb 3,4,5) and
QCs/berth (nc 1,2,,7) Minimum AC per second
class of ships is 99,302 for AM II (? 2.84)
nb 4 and nc 2.85
45
Figure 14. Average container ship costs for
various berths/terminal (nb 3,4,5) and
QCs/berth (nc 1,2,,7) Minimum AC per second
class of ships is 96,008 for AM II (? 2.93)
nb 4 and nc 3
46
Berth throughput To obtain a deeper
understanding of terminal throughput, the PECT is
compared by four parameters operation efficiency
and number of ships serviced in simulation period
of PECT Fig. 17 (RD real data and SR
simulation result), ship operation efficiency in
PECT Fig. 18, operation efficiency and
throughput of PECT Fig. 19 and the TEU/hectare
and TEU/berth meter of PECT Fig. 20. Therefore,
500,000 TEU per berth is a high standard in PECT,
which is achieved by top 50 of terminals
operators. But 700,000 TEU per berth is standard
is Chinese major ports, which have been achieved
by top 50 of terminals operators. The ship
operation efficiency has a significant
relationship with throughput of berth. In 2010,
the standard is expected to be higher, because
the ship is bigger and advanced technology
implemented in yard operations. Based on the
performance achieved, and highly competitive
environment in the Far East region, it is
expected that PECT could achieved at least
700,000 TEU/berth as a new standard for major
terminal operators in Asia.
47
Figure 17. Operation efficiency and number of
ships serviced in simulation period of PECT
48
Figure 18. Ship operation efficiency in PECT
49
Figure 19. Operation efficiency and throughput
of PECT
50
Figure 20. The TEU/hectare and TEU/berth
meter of PECT
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CONCLUSIONS Models described and developed in
this paper, especially SM can be used to
estimate the improvements in performance of the
ship-berth link operations when their handling
capacities vary for average cost analysis, as
the simulation provides seven important
parameters, i.e., average service time of ships
in port, average arrival rates of ships, the
number of QCs/berth, QC productivity, the berth
throughput, the degree of utilization and traffic
intensity of container terminal, which are needed
to establish average cost effective system and
in the planning for future additional QCs/berth
and berths/terminal that may be needed, through
the use of forecasted average interarrival time
of ships (obviously, high average time that ships
spend in queue would indicate the need for
additional QCs/berth and berths/terminal). From
the features of average cost curves and global
optimum solutions various obtained delayed
systems, the following facts are confirmed If
the values of containers transferred per ship,
the average QC move time, the number of
berths/terminal, the number of Erlang phases of
service time distribution and other numerical
input values are given, then the optimum number
of QCs/berth, the traffic intensity, the berth
utilization, the average time that ships spend in
port, the total port cost and average cost per
ship or container served, can be easily obtained
by the use of average cost curves and global
optimum solution The results obtained here
suggested that an increase in the number of
QCs/berth could reduce the average cost per ship
or container served. In accordance with that, the
correspondence between simulation and analytical
results completely gives the validity to the
applied analytical model to be used for
optimization of processes of servicing ships at
PECT. Finally, these models also addresses issues
such as the performance criteria and the model
parameters to propose an operational method that
reduces average cost per ship served and
increases the terminal efficiency and berth
throughput.
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--- THANK YOU ---
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The purpose of this monograph is to present the
achievements of the authors in the field of ports
and container terminals modeling during the last
few years. The material in the monograph is
divided in two parts ports modeling and
container terminal modeling.
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