Maritime Economics & Logistics, 2005, 7, (316–335) r 2005 Palgrave Macmillan Ltd All rights reserved. 1479-2931/05 $30.00
www.palgrave-journals.com/mel
Simulation Modelling of Ship-Berth Link With Priority Service B R A N I S L AV D R A G O V I C´ 1 , N A M K Y U PA R K 2 , Z O R A N R A D M I L O V I C´ 3 & V L A D I S L AV M A R A Sˇ 1 1 M a r i t i m e Fa c u l t y, U n i v e r s i t y o f M o n t e n e g r o , D o b r o t a 3 6 , K o t o r 85330, Serbia & Montenegro. E-mail:
[email protected]; 2Department o f D i s t r i b u t i o n M a n a g e m e n t , To n g m y o n g U n i v e r s i t y o f I n f o r m a t i o n Te c h n o l o g y, 5 3 5 , Yo n g d a n g - d o n g , N a m - g u , B u s a n 6 0 8 - 711 , K o r e a . E - m a i l : n k p a r k @ t i t . a c . k r ; 3 Fa c u l t y o f Tr a n s p o r t a n d Tr a f f i c E n g i n e e r i n g , U n i v e r s i t y o f B e l g r a d e , 110 0 0 B e l g r a d e , Vo j v o d e S t e p e 305, Serbia & Montenegro. E-mail:
[email protected]
Simulation of the logistics activities related to the arrival, berthing, service and departure processes of ships in container ports can be carried out for different goals such as design of ship-berth link, increase productivity and efficiency of quay cranes, analysis and planning of operations at the ship-berth link, etc. These logistics activities are particularly complex and very costly since they require the combined use of expensive infrastructure capacities especially berths and quay cranes. Ship-berth link as a main port link is required to serve ships as quickly as possible. Thus, in order to successfully design and develop shipberth link in a container port and utilise it as efficiently as possible, it is necessary to develop a simulation model that will support decision-making processes of terminal managers. The results, analysis and conclusions given in this paper are intended to provide guidance on achieving time efficiency and accuracy in the modelling of ship-berth link and calibration of ship-berth link simulation models for Pusan East Container Terminal (PECT). Maritime Economics & Logistics (2005) 7, 316–335. doi:10.1057/palgrave.mel.9100141
Keywords: Container terminal; ship-berth link; performance evaluation; priority; modelling; simulation.
INTRODUCTION Simulation modelling techniques are being applied to a wide range of port and terminal planning processes and operational analysis of container handling
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
317
systems. These models have become extremely valuable as decision-support tools during the planning and modelling of ship-berth link in a port. A container port, which provides the interface between container ships, railroads and road trucks, represents a critical link in the intermodal transport chain. Container port efficiency is an important requirement which is necessary to be fulfilled in the best possible way by management of container terminals. It is especially important in today’s competitive world of port and shipping business. Because the container port facilities are very expensive to run and purchase, it is very important to determine whether the existing container terminal capacities are large and efficient enough to handle the changeable container flows during the considered periods of time. Determining the effect of changes in throughput, as well as the influence of various operational, technological and economic aspects on efficiency of container port operations, has been analysed widely by using port simulation models. The crucial terminal management problem is optimising the balance between shipowners who request quick service of their ships and the economic use of allocated resources. Since both container ships and container port facilities are very expensive, it is desirable to utilise them as intensively as possible. Main problem in analytical modelling of container terminal relates to the fact that models lose in detail and flexibility, so they simplify the real situation. On the other hand, simulation modelling is better than analytical one in representing the random and complex environment of a container terminal. Simulation modelling is particularly better suited for the presentation of processes at the container terminal, especially when several parameters and scenarios need to be included into the investigation. This paper gives a ship-berth link modelling methodology based on statistical analysis of container ship traffic data obtained from the Pusan East Container Terminal (PECT). Implementation of the presented procedure leads to the creation of a simulation algorithm that captures ship-berth link performance well. All the main performances of the ship-berth link are given. The efficiency of operations and processes on the ship-berth link has been analysed through the basic operating parameters such as berth utilisation, average number of ships in waiting line, average time that a ship spends in waiting line, average service time of ship, average total time that a ship spends in port, average quay crane (QC) productivity and average number of QCs per ship. The rest of this paper is organised as follows. In the next section, we provide an overview of the literature related to port simulation and especially ship-berth link models. The following section presents a brief description of ship-berth link simulation modelling 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 Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
318
ship-berth link at PECT. In conclusion, we draw and incorporate suggestions to further the research on ship-berth link performance.
L I T E R AT U R E R E V I E W O F P O R T S I M U L AT I O N M O D E L S There are few studies dealing with ship-berth link planning. Ship-berth link is concerned with the interface between the land and water sides. The ship-berth link planning problem consists of assigning incoming ships to berthing positions, as well as QCs scheduling which play an important part in port operation management. In addition, the service time of a ship depends on its berthing point and is a function of the number of QCs assigned to it. This dependency strongly affects the performance of the ship-berth link. Earlier research related to a container port, particularly to the ship-berth link planning, using simulation, is summarised in Table 1. Simulation models have been used extensively in the planning and analysis of the ship-berth link. Many different simulation models regarding port operations, especially ship-berth link planning, have been developed in papers by Gambardella et al (1998), Legato and Mazza (2001), Tahar and Hussain (2000), Merkuryeva et al (2000), Nam et al (2002), Shabayek and Yeung (2002), Kia et al (2002), Pachakis and Kiremidjian (2003), Sgouridis et al (2003) and Demirci (2003). These models are coded in different simulation languages, as seen in Table 1.
Table 1: Literature review of a container port and ship-berth link planning by using simulation Considered problems
Approaches
References
Simulation of container terminals (CT) and ports
Modsim III Object-oriented programming, C++ ARENA ARENA, SLX Visual SLAM AweSim Witness software Taylor II GPSS/H Extend-version 3.2.2 Scenario generator
Gambardella et al, 1998; Yun and Choi, 1999;
Overview concept
Maritime Economics & Logistics
Quantitative models for various decision problems in CT Logistics processes and operations in CT – optimization methods
Tahar and Hussain, 2000; Merkuryeva et al, 2000; Legato and Mazza, 2001; Nam et al, 2002; Demirci, 2003; Shabayek and Yeung, 2002; Kia et al, 2002; Pachakis and Kiremidjian, 2003; Sgouridis et al, 2003; Hartmann, 2004; Vis and de Koster, 2003; Steenken et al, 2004.
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
319
It should also be pointed out, as shown in Table 1, that there are two overview concepts of container port operation literature given by Vis and de Koster (2003) and Steenken et al (2004). Lee et al (2002) have implemented four models, such as input model, strategy model, operational policy model and performance model, to design port supply chains that improve supply chain decisions. They have found that, through the modelling of terminal operations in a port supply chain perspective, and by evaluating a potential effective strategy, a multi-agent system can demonstrate its capabilities of identifying and justifying useful strategies. Their model has been applied in PECT. In a paper by van Asperen et al (2003), the impact of the arrival process of ships on the efficiency of the loading and unloading process has been assessed, making a case for careful modelling of arrival processes in port simulations. Veenstra et al (2003) present an economic evaluation of container terminals by operational simulations of generated cash flows. One can conclude that the ship-berth link, as a main port link, has been adequately analysed and modelled by using different simulation approaches. Various operations research models and methods in the field of optimising shipberth link planning are applied more and more in world terminals.
S H I P - B E R T H L I N K S I M U L AT I O N M O D E L L I N G Most container terminal systems are sufficiently complex to warrant simulation analysis to determine systems performance. Simulation is recommended for analysing ship-berth link performance. Ship-berth link simulation models can be written by using general-purpose algorithmic languages (eg Pascal, C, C++, etc) and simulation languages (eg AweSim, EXTEND, SIMAN, SLAM, ARENA, Witness software, GPSS/H, Taylor II). The GPSS/H simulation language, specifically designed for the simulation of manufacturing and queueing systems, has been used in this paper (Schriber, 1991). Model structure Ship-berth link is complex owing to the different interarrival times of ships, different dimensions of ships, multiple quays and berths, different capabilities of QCs and so on. The modelling 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). Ship service begins with ship arrival to port anchorage area. Depending on the state of congestion, or priority of the arriving ship, the latter may have to wait in the Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
320
Figure 1: Operational procedures in the ship-berth link
anchorage area. After berthing, containers are unloaded/loaded from/on the ship. Finally, when service is completed, the ship leaves the port. 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 analysed so that an accurate simulation algorithm is created in order to evaluate ship-berth link parameters. Also, it is assumed that other terminal resources such as storage capacity; number and capacity of container yard equipment; number of lanes at the gates; and intermodal capacities, are constraints that do not influence the performance parameters at ship-berth link. Interarrival times of ships The interarrival 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); the negative exponential distribution (Shabayek and Yeung, 2002) or the Weibull distribution (Tahar and Hussain, 2000). Loading and unloading stage Accurate representation of number of lifts per ship call is one of the basic tasks of ship-berth link modelling procedure. It means that, in accordance with the Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
321
division of ships in different classes, the distribution corresponding to those classes has to be determined. Empirical distributions of the number of lifts per ship (DNLS) are very often found to fit the normal distribution. Even when real data are not available to justify the use of a particular distribution, the number of lifts per ship can be reasonably approximated by the normal random variable (Pachakis and Kiremidjian, 2003). 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 determine 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 minimising 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 firstserved 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.
M O D E L VA L I D AT I O N A N D S I M U L AT I O N A N A LY S I S This section describes a ship-berth link modelling methodology based on statistical analysis of container ship traffic data obtained from the PECT. Implementation of the presented procedure leads to the creation of a simulation algorithm that captures the ship-berth link performance well. All the main Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
322
Figure 2: Flowchart
performances of the ship-berth link are given. The efficiency of operations and processes on the ship-berth link has been analysed through the basic operating parameters such as berth utilisation, average number of ships in waiting line, average time ships spend in waiting line, average service time, average total time in port, average QC productivity and average number of QCs per ship. PECT is one of the biggest container terminals in the world with a capacity of 1,790,000 20-foot equivalent units (TEU) per year (in 2003). There are four berths with total quay length of 1,200 m and draft around 14–15 m. Ships of every size can be serviced at each berth. The main part of PECT is presented in Figure 3 (PECT website). Input data An important part of model implementation is the correct choice of the values of the simulation parameters. The input data for the simulation are based on actual ship arrivals at PECT for the 3-month period from September 6 to December 1, 2004. This involved approximately 336 ship calls. The ships were categorised into the following four classes according to their capacity: under 2,000 TEU; 2,001–3,500 TEU; 3,501–5,000 TEU; and over 5,001 TEU. Ship Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
323
Figure 3: PECT layout
arrival probabilities were as follows: 26.3% for first class, 24.3% for second, 20.4% for third and 29% for fourth class of ships. Total throughput during the simulating period was 486,244 TEU. Also, the berthing/unberthing time of ships was assumed to be 1 h. The interarrival time distribution is plotted in Figure 4a. Interestingly, even though ship arrivals are scheduled and not random, the distribution of interarrival times fitted very well the Weibull distribution. The number of lifts per ship call was derived from real data during the simulation period. The empirical DNLS was applied to each of the four classes of ships. The most frequent type of distribution for these input data was the normal distribution (Figure 4b – first class of ships, Figure 4c – second class of ships and Figure 4d – third class of ships). For the fourth class of ships, no distribution could be fitted successfully, so that two separate groups were formed based on service duration: one for ships serviced in less than 24 h, and one for ships whose service time was longer than 24 h. The lognormal distribution was found to be the best fit for the first group (Figure 4e), and the normal distribution, as with the previous three classes, fitted successfully this input parameter for the second group (Figure 4f). The assignment of QCs was assumed random with probabilities equal to the percentages of the number of QCs that were allocated for ship servicing. The results of the analysis of frequencies of QC assignment are given in Table 2. For the first class of ships (under 2,000 TEU), 11.96% of total ships were given one QC; 70.63% were given two QCs; 16.30% were given three QCs and 1.11% were Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
324
a 60
b
25
50 20 Number
Number
40 30
15 10
20
.8
.1
63 168 273 379 484 589 695 800 905
Classes
33
.3
31
.5
28
.8
25
.0
22
.3
20
0
.5
17
14
11
9.
6.
3.
0.
.8
0 3
0 5
5
7
10
IAT (h)
c 18
Observed frequency
d
Theoretical frequency
25
16 20
14
15
Number
Number
12 10 8
10
6 4
5
2 0
e
0 405 656 908 1159 1410 1662 1913 Classes Observed frequency Theoretical frequency
244 433 621 810 998 1186 1375 Classes
f
25
12 10
20
Number
Number
8 15 10
6 4
5
2
0
0 396 731 1065 1400 1735 2069 2404 Classes Observed frequency Theoretical frequency
1162 1450 1737 2025 2313 2600 2888 Classes Observed frequency Theoretical frequency
Figure 4: (a) Distribution of interarrival times (IAT) of container ships at PECT; (b) DNLS for first class of ships; (c) DNLS for second class of ships; (d) DNLS for third class of ships; (e) DNLS for fourth class of ships (first group) and (f) DNLS for fourth class of ships (second group)
given four QCs. For ships in the 2,000-3,500 TEU class, one QC was assigned in 1.13% of cases; two QCs in 23.86%; three QCs in 70.45%; and four QCs in 4.56% of all cases. The data in the other two classes can be interpreted in the Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
325 Table 2: Analysis of frequencies of QCs assignment per ship in % (current strategy) No. of QCs assigned per ship in % Classes of ships o2,000 TEU 2,001–3,500 3,501–5,000 45,001
One QC
Two QCs
Three QCs
Four QCs
Five QCs
Six QCs
11.96 1.13 F F
70.63 23.86 6.06 1.11
16.30 70.45 63.63 50.00
1.11 4.56 30.31 46.67
F F F 1.11
F F F 1.11
Table 3: Average QCs productivity in lifts per hour Average QCs productivity in lifts per hour Classes of ships
One QC
Two QCs
Three QCs
Four QCs
Five QCs
Six QCs
o2,000 TEU 2,001–3,500 3,501–5,000 45,001
19.37 19.87 F F
35.40 38.70 41.88 59.45
47.89 56.62 60.38 59.78
52.49 68.42 70.93 75.66
F F F 75.11
F F F 38.83
Average
19.62
43.88
56.17
66.87
75.11
38.83
same way from Table 2. Furthermore, average QC productivity, in lifts per hour, is shown in Table 3. Parameters given in Tables 2 and 3 and Figure 4 were used for the development of Simulation model I. Service times were calculated by using the appropriate DNLS; the distribution of QCs; and QC productivity. In addition, we have developed another model (Simulation model II) where service times were calculated by using a particular distribution. To obtain accurate data, we have first fitted the empirical distribution of service times to the appropriate theoretical distribution for each ship class. It is observed that service time of the first ship class follows the normal distribution (Figure 5a), while lognormal distribution fits very well the service time of other classes (Figures 5b–d). Service time distributions are given in Figure 5. Goodness-of-fit was evaluated, for all tested data, by both w2 and Kolmogorov–Smirnov tests at a 5% significance level. Validation-verification Simulation model I For purposes of validation of the simulation model and verification of the simulation computer program, the results of the simulation model were Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
326
b
25
20
15
15
10
10
5
5
0
0 4.3
7.0
9.6 12.3 15.0 17.6 20.3 22.9 25.6 28.2 Classes
Observed frequency
c
25
20
Number
Number
a
8.0
Theoretical frequency
Observed frequency
d
20
12.0 16.0 20.0 24.0 28.1 32.1 Classes Theoretical frequency
25
18 16
20
12
Number
Number
14
10 8
15
10
6 5
4 2 0
0 10.0
13.6
17.2
20.8
24.4
28.0
31.5
Classes Observed frequency
Theoretical frequency
8.9
14.6 20.2 25.8 31.4 37.0 42.6 48.2 53.8 C lasses Observed frequency
Theoretical frequency
Figure 5: Service distribution for each class of ships. (a) Service distribution of first class of ships; (b) service distribution of second class of ships; (c) service distribution of third class of ships and (d) service distribution of fourth class of ships
compared with the actual measurements. Three statistics were used as a comparison between simulation output and real data: berth utilisation, average service time and average number of assigned QCs. The simulation model was run for 44 statistically independent replications. The average results were recorded and used in comparisons. After analysis of the port data, it was determined that berth utilisation is about 68.2%, while the simulation output shows the value of 72.1%. Average service time shows very little difference between the simulation results and actual data, that is, 16.97 h and 16.31 h, respectively. Comparison between simulation output and real data for average Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
327
number of QCs assigned showed exactly the same value. It was found that on average 2.87 QCs were allocated. All the above shows that simulation results are in agreement with real data. This conclusion gives the validity to the model to be the base for comparison of the primary case, without priority of ships, with the alternative cases where priority is assigned to different classes of ships. Simulation model II The same output parameters of Simulation model II were compared with actual measurements in order to validate this model too. Simulation output from this model shows that berth utilisation is 66.8%, which is, also, in agreement with the value of the real parameter (68.2%). Average service time shows very little difference between the simulation results and actual data, that is, 16.20 h and 16.31 h, respectively. If we compare the values of considered parameters obtained from both models, we see that the parameters converge to the real measurements, but from different sides. We can thus conclude that the validity of model II is well supported. Simulation results for PECT The results of both simulation models include average number of ships in queue, average time a ship spends in queue, average service time, and average time a ship spends in port. The priority assignment, based on ship class, is introduced next in order to improve the performance parameters of the shipberth link. Priority is therefore assigned to each class and the output is considered in order to help port management establish the best service strategy. To achieve accuracy, we first evaluate the queue properties, and then we deal with service and port time of ships. Results of simulation model I The results shown in Figure 6 are obtained by using input data from Tables 2 and 3. It is notable that queue properties are minimised for the second strategy than in the case when priority is assigned to the first class of ships. The reason for this trend taking place is that smaller ships have shorter service time and, because of that, ships in queue have less waiting time. A considerable increase in average waiting time is recorded when priority is assigned to the biggest ships. This result occurs because the decrease in waiting times of the fourth class of ships is not enough to overcome the increase of waiting time of the other three classes. All the above relate to the current strategy of QC assignment at PECT. Ship-berth link performance measures for current strategy of QC assignment for Simulation model I are shown in Figure 6. Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
328
a
b
c
d
Figure 6: Ship-berth link performance measures for current strategy in QCs assignment (Simulation model I) Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
329 Table 4: Analysis of frequencies of QCs assignment per ship in % (improved strategy) No. of QCs assigned per ship in % Classes of ships o2,000 TEU 2,001–3,500 3,501–5,000 45,001
One QC
Two QCs
Three QCs
Four QCs
Five QCs
Six QCs
1.96 1.13 F F
80.63 13.86 6.06 1.11
16.30 80.45 53.63 40.00
1.11 4.56 40.31 56.67
F F F 1.11
F F F 1.11
To improve performance measures, sensitivity analyses were carried out by changing QC assignment strategy. We have noted the most common number of QCs serving all the classes of ships. The changes we have made to improve the strategy are shown in Table 4. The results of improved strategy in QC assignment are presented in Figure 7. It can be seen, from Figures 6c and 7c, that the value of average service time of all ships does not change considerably over different strategies in priority assignment for both of evaluated strategies. Even more, this time, it has exactly the same value in the current strategy of QC assignment, that is 16.97 h, while differences with the improved strategy is in the order of 0.03 h, that is from 16.58 h to 16.61 h. Also, it is obtained from the improved strategy that 2.96 cranes, on average, were allocated per ship. Comparative analysis of results from the current and improved strategies are given in Figure 8. Results of simulation model II The trends of the average value of the number of ships in queue and waiting time fluctuate a lot over the computation with different priority strategies. This conclusion can be drawn from Table 5 and Figure 9. Similar comparative parameters, calculated for all strategies in priority assignment, are presented in Table 5. This table shows the impacts of assigning priority to different classes on average waiting time, given in minutes, in relation to the values obtained in the case when all ships are of equal priority. Besides, there is no significant fluctuation in the average service time of all classes of ships over different strategies in priority assignment. This is normal since the service time of ships does not depend on the priority of ships being served, but it is a function of the number of QCs allocated for service, QC productivity, working time, etc. Furthermore, appropriate distributions of service time for each class, as used in this model, do not make significant influence upon the strategy in priority assignment. On the other hand, the time a ship spends in port, comprising waiting and service time, depends on the considered strategies, mainly because of the considerable differences in the Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
330
a
b
c
d
Figure 7: Ship-berth link performance measures for improved strategy in QCs assignment (Simulation model I) Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
331
a
c
d
Figure 8: Ship-berth link performance measures. Comparative analysis of considered strategies (Simulation model I)
Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
332 Table 5: Simulation results of average waiting time for Model II Average waiting time in minutes Priority assigned to Classes of ships o2,000 TEU 2,001–3,500 3,501–5,000 45,001
No priority
First class (changes)
Second class (changes)
Third class (changes)
Fourth class (changes)
218.02 209.13 272.37 218.3
123.73 +29.39 +29.24 +31.07
+40.90 132.29 +40.54 +56.64
+37.67 +35.84 173.56 +57.22
+55.96 +51.93 +80.01 121.01
Total
34.03
+5.79
42.83
+66.89
a
rd
b
rd
c
d
Figure 9: Ship-berth link performance measures (Simulation model II)
average values of waiting times. Figure 10 illustrates the average values of time a ship spends in port for all strategies. As it can be seen, the results of the first three strategies with priority show that this time is less than that of the basic Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
333
a
in hours
b
in hours
rd
rd
c
d
Figure 10: Ship-berth link performance measures (Simulation model II)
case, that is when all ships are of equal priority. On the other hand, ships will have longer port time when priority is assigned to the biggest class of ships. Similarly (Table 5), changes in average time a ship spends in port, incurred by assigning priority to each class of ships, in relation to the value of this parameter in the case of a no priority strategy are given in Table 6. The above implies that assigning priority to smaller ships, irrespective of the kind of the division of ships, would cause the improvement of performance measures at the ship-berth link.
CONCLUSIONS This paper gives the results of the simulation models for the ship-berth link at PECT. The ship berth-link performance for five alternative strategies has been evaluated, and system behaviour observed. The results have revealed that Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
334 Table 6: Simulation results of average time that ship spends in port for Model II Average time that ship spends in port in minutes Priority assigned to Classes of ships o2,000 TEU 2,001–3,500 3,501–5,000 45,001
No priority
First class (changes)
Second class (changes)
Third class (changes)
Fourth class (changes)
980.21 1168.1 1299.25 1596.41
127.45 +27.39 +24.20 +24.19
+48.45 136.81 +28.50 +40.24
+38.34 +32.16 184.24 +45.76
+61.69 +57.88 +75.76 120.74
Total
51.67
19.62
67.98
+74.59
simulation modelling is a very effective method to examine the impact of introducing priority, for certain class of ships, on the ship-berth link performance at PECT. Simulation model I, that is, current and improved strategy of QC assignment and Simulation model II show that assigning priority to smaller ships would lead to an improvement of the main operational parameters. It was shown that this conclusion does not depend on the division of ships into certain classes. In addition, comparing results form Simulation model I, that is, the current strategy in QC assignment, and Simulation model II with output of improved strategy, we can see that improvement of all evaluated parameters at ship-berth link has been achieved. For example, better results are achieved for the average number of ships in queue. Also, the average time a ship spends in queue is shortened by about half-an-hour. Average service time decreases from 16.97 h in the current strategy to about 16.6 h in the improved strategy. And finally, the average time a ship spends in port, comprising the average time a ship spends in queue and the average service time, is reduced by about 1 h. Comparing the average number of QCs allocated per ship, it can be concluded that all above improvements are achieved by increasing this number from 2.87 to 2.96, for current and improved strategies in QC assignment, respectively. The simulation models can be used by the port management to improve the various operations involved in the process of ship service at the ship-berth link. Finally, as cost is a key measure in the selection of alternative strategies, further research needs to incorporate a cost analysis of the ship-berth link. This research ought to focus especially on the cost incurred in cases where priority is assigned to the largest of ships, as their proper utilisation is of crucial importance to their owners. In addition, the satisfaction or dissatisfaction of container shipping lines with respect to priority assignment is another important consideration that needs to be taken into account in future research. Maritime Economics & Logistics
B Dragovic´ et al Simulation Modelling of Ship-Berth Link
335
Acknowledgements We are grateful to Professor Hercules Haralambides and the referees for their constructive comments and helpful suggestions.
REFERENCES Demirci, E. 2003: Simulation modelling and analysis of a port investment. Simulation 79: 94–105. Frankel, GE. 1987: Port Planning and Development. John Wiley and Sons: New York. Gambardella, LM, Rizzoli, AE and Zaffalon, M. 1998: Simulation and planning of intermodal container terminal. Simulation 71: 107–116. Hartmann, S. 2004: Generating scenarios for simulation and optimization of container terminal logistics. OR Spectrum 26: 171–192. Kia, M, Shayan, E and Ghotb, F. 2002: Investigation of port capacity under a new approach by computer simulation. Computers & Industrial Engineering 42: 533–540. Legato, P and Mazza, RM. 2001: Berth planning and resources optimization at a container terminal via discrete event simulation. European Journal of Operational Research 133: 537–547. Merkuryeva, G, Merkuryev, Y and Tolujev, J. 2000: Computer simulation and metamodelling of logistics processes at a container terminal. Studies in Informatics and Control 9: 53–59. Nam, K-C, Kwak, K-S and Yu, M-S. 2002: Simulation study of container terminal performance. Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE 128: 126–132. Pachakis, D and Kiremidjian, AS. 2003: Ship traffic modeling methodology for ports. Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE 129: 193–202. PECT. http://www.pect.co.kr. Schriber, TJ. 1991: An Introduction to Simulation Using GPSS/H. Wiley: New York. Sgouridis, SP, Makris, D and Angelides, DC. 2003: Simulation analysis for midterm yard planning in container terminal. Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE 129: 178–187. Shabayek, AA and Yeung, WW. 2002: A simulation model for the Kwai Chung container terminal in Hong Kong. European Journal of Operational Research 140: 1–11. Steenken, D, Vob, S and Stahlbock, R. 2004: Container terminal operation and operations research – a classification and literature review. OR Spectrum 26: 3–49. Tahar, MR and Hussain, K. 2000: Simulation and analysis for the Kelang Container Terminal operations. Logistics Information Management 13: 14–20. Lee, T-W, Park, N-K and Lee, D-W. 2002: Design of simulation system for port resources availability in logistics supply chain. IAME 2002 Panama Conference Proceedings, International Association of Maritime Economist Conference, Panama, 13–15 November 2002. Available at http://www.eclac.cl/ transporte/perfil/iame_papers/proceedings/AgendaProceedings.html. van Asperen, E, Dekker, R, Polman, M and de Swaan Arons, H. 2003: Modeling ship arrivals in ports. In: Chick, S, Sa´nchez, PJ, Ferrin, D and Morrice, DJ (eds.). Proceedings of the 2003 Winter Simulation Conference. Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA. pp. 1737–1744. Veenstra, AW, Lang, N and van de Rakt, B. 2003: Economic analysis of a container terminal simulation. The International Workshop on Harbour, Maritime & Multimodal Logistics Modeling and Simulation, Maritime Environment II, Riga TU, Latvia, 18–20 September 2003. Available at http://www.eur.nl/ fbk/dep/dep1/Introduction/Staff/People/Lang/ia1.pdf. Vis, IFA and de Koster, R. 2003: Transhipment of containers at a container terminal: an overview. European Journal of Operational Research 147: 1–16. Yun, WY and Choi, YS. 1999: A simulation model for container-terminal operation analysis using an object-oriented approach. International Journal of Production Economics 59: 221–230. Maritime Economics & Logistics
