HomePublicationsInsightsCAPACITY PLANNING IN A CONTAINER TERMINAL VIA SIMULATION – A CASE STUDY

CAPACITY PLANNING IN A CONTAINER TERMINAL VIA SIMULATION – A CASE STUDY

Currently, the container is the most used way to move materials and products by sea. Yun & Choi (1999) point out that more than 90% of international cargo is moved via ports and terminals and that 80% of this volume is packaged in containers.

Naturally, the increased demand for this service has a direct impact on the infrastructure of port terminals. Hyland (2001) points out that the port sector presents some challenges: the increase in the volume of cargo with the consequent congestion in ports; balancing road and rail flows in the hinterland1; the advent of container megaships and the constant search for adapting terminal capacity to maritime traffic. In this context, there is a need to use a tool capable of measuring and predicting the effects generated by the increase in container handling at terminals, allowing better preparation for the challenges cited by Hyland.

In the early 90s, Wadhwa (1990) revealed that the solution to the challenges of the port sector involves the use of simulation models. The idea is to use the simulation as a possible tool to measure the effects of changes in operational, technological and investment variables, thus supporting authorities and port managers in the decision-making process. In fact, many studies are converging on the use of simulation in the dimensioning and planning of port operations, with very satisfactory results being obtained.

In this work, we focused on observing how the increase in the rate of ship arrivals at a terminal and, consequently, the increase in the number of containers handled influence the performance of port operations and impact the dimensioning of the capacity of the container yard. To achieve this goal, we developed a simulation model to represent typical port operations at a container terminal.

 

LITERATURE REVIEW

To build a simulation model, it is necessary to understand well the entire process that will be simulated. In container terminals, Casaca (2005) pointed out that there are three main stages in the handling process: the one that takes place at the anchorage (cradle), that is, at the interface with the sea; the one that takes place in the container yard and the one at the road and rail access gates. These three steps are intertwined, with the performance of each affecting the performance of the other.

In addition, productivity in each of these stages depends on a series of factors, which are structural, organizational and technological, among others. In this way, we realize that port operations are quite complex by nature and that they require sophisticated modeling techniques. Simulation plays an important role in improving processes and increasing efficiency, basically because it makes it possible to identify possible problems and bottlenecks, anticipating the course of action to be taken by the manager (Bowersox, 1978). And, when it comes to container terminals that have very expensive assets, such as berths, portainers and space, such a technique helps to avoid unnecessary investments, which do not bring any immediate advantage to the system as a whole.

In their article, Shabayek & Yeung (2002) point out some possible applications of simulation as a tool to support the planning of a container terminal:

  • in cost and service level analysis, as the simulation allows evaluating two important parameters: the average waiting time of ships in the queue and the berth utilization rate, necessary to determine when and by how much the terminal capacity should be expanded ;
  • in dimensioning the number of spaces needed for trucks, in order to avoid possible costs, such as fines and penalties;
  • in choosing the best sequencing/priority policy for ships in the queue, thus minimizing demurrage costs.

 

A study by Kia et al. (2002) also showed that the stages of the handling process in a container terminal are directly connected and that the simulation stands out as an important tool to support planning and decision-making (corroborating Casaca). Based on a container terminal at the Australian port of Melbourne, Kia et al. obtained, as relevant results for capacity planning, the following directions: (1) the need to create internal distribution centers to improve the terminal's operation; (2) the expansion of the container yard, to reduce unnecessary handling, reducing the ship's loading/unloading time and (3) the increase in the number of berths.

Using simulation software, but focusing on more operational issues, such as the allocation of berths and the prioritization of ships in the queue, Wanke & Cortes (2008/2009) developed a model with the aim of evaluating how different allocation and queue affect demurrage costs. It was identified that these decisions depend not only on the specifics of each ship that stops at the terminal, but also on the whole. Examples of specificities include the frequency of visits per year, vessel size, operating time, etc.

These are some examples of the applicability of simulation in the planning of container terminals. In our study, a model was developed to extract information on the behavior of the utilization rate of the berths (or anchorages), the waiting time and the number of ships in the queue to berth, the movement of containers in the terminal and the queue of trucks and trains waiting to unload or load, due to the increase in the arrival rate of ships at the terminal.

THE CASE STUDYED

As previously mentioned, the first step in building a simulation model is to understand the processes involved in the operation to be simulated. In our case, we simulate operations in a container terminal that has two berths (or berths).

Shortly after arrival, the ship moves into a single queue and is only released when one of the two berths is free. When docking, at the berth, the ship's unloading stage begins. Each berth is equipped with two portainers and both operate simultaneously on the berthed ship. When the ship's unloading stage is completed, the loading stage begins. After being fully loaded, the ship leaves the port, freeing the berth.

Parallel to the arrival of ships, trucks loaded with containers, trains and empty trucks arrive to pick them up at the port. It is worth noting that the train only unloads containers at the terminal, leaving empty. For modeling purposes only, a terminal with two container yards was considered: one for those that are unloaded from the ship and another for those that arrive by truck and by train and that will be loaded on the ship. Both to enter the port and to leave, the trucks and trains pass through the gates (gates) where the weighing and inspection of the cargo is carried out. The described model is represented in detail in the attached flowchart (Annex 1).

In addition, for greater fidelity of the model to reality, the following aspects were observed:

  • Priority adopted for berthing ships in the queue was PEPS (first in, first out);
  • The technology used for unloading/loading containers from the ship was the carousel. In it, in the unloading stage, for example, when removing the container, the portainer unloads it directly onto a truck that goes to the yard where the container will be unloaded by a forklift and placed in its respective place. The truck, now empty, returns to the portainer to transport more containers to the yard. The same occurs in the ship loading stage, but with the order of processes reversed.
  • Bearing in mind that a portainer is a very expensive asset (in the order of millions of reais), the number of trucks participating in the carousel must be such that the equipment does not stand still waiting for the trucks to return from the container yard.

As already stated, the study's basic purpose was to evaluate how the variation in times between ship arrivals interferes with the performance of operations at the terminal. For this, we conducted experiments in which the times between ship arrivals were decreasing and we fixed all other input data, collecting information about: (1) the queue of ships waiting to berth; (2) the waiting time of ships in the queue; (3) the fee for using the berths; (4) the number of containers handled per year; (5) the queue of trucks at the port and (6) the queue of trains to unload. The variation in times between ship arrivals can be seen in Table 1.

 

2009_07_image 01

To choose which type of distribution would be used to represent the times between arrivals of ships, trains and trucks in the port, we based ourselves on a study carried out by Dragovic et al. (2005), who assumed the exponential distribution. According to the authors, the exponential distribution is one of the most adopted in the literature to represent times between arrivals. In turn, to represent the number of containers loaded and unloaded from the ship, a triangular distribution of parameters of 150, 200 and 250 was used, representing minimum, average and maximum, respectively. Finally, for each of the experiments, 25 replications of 365 days duration were performed. Such a procedure is necessary to guarantee the statistical validity of the results, allowing the performance of multivariate analyzes with the desired levels of significance.

RESULTS ANALYSIS

The average results obtained from the 25 replications of each experiment are presented here. Graph 1 shows the behavior of the berth utilization rate as a function of the time interval between vessel arrivals. Due to the fact that there is no prioritization in the allocation of ships in the berths, the values ​​presented correspond to an approximate average of the utilization rate of the two berths.

 

2009_07_image 02

As expected, the utilization rate increases as the time interval between ship arrivals decreases, that is, as more ships dock at the port per time interval. The next step analyzed the impact of this increase on the size of the queue of ships to berth and their waiting time.

2009_07_image 03

 

2009_07_image 04

In a more detailed view of the graphs on the behavior of the size of the queue of ships and their waiting time in the queue, we can see that up to a berth utilization rate of 0,40 the terminal performs well, with a small queue of ships and a very short waiting time. However, increasing even more the cradle utilization rate, the results indicate that the terminal begins to show signs of saturation. This has a negative impact on operational performance, generating a queue size that is much higher than acceptable and also waiting times that may result in high deferred costs.

It is clear that, if the demand for this terminal increases to the point where the utilization rate is, for example, greater than 0,40, actions must be taken in order to maintain a satisfactory level of service. These actions may include changes in the existing berths, such as the use of more portainers, or even the construction of a new berth, increasing the terminal's service capacity. It should be noted that ship operators are becoming more demanding and, when they do not perceive certain guarantees of availability of berths in the terminals, they seek alternatives to maintain a high level of service (Luo and Grigalunas, 2003).

As previously stated, only the input data time between ship arrivals was varied. Therefore, it is expected that the queues of trucks and trains at the port remain stable in all experiments, which is indeed verified in Graphs 4 and 5.

 2009_07_image 05

 

2009_07_image 06

The results presented were obtained through the use of simulation. Undoubtedly, this generates some work when running different scenarios, with different input data, as a large part of the simulation steps must be done again. In order to generate an equation that represents the behavior of a variable as a function of the other variables and avoid the exhaustive process of generating and collecting data, we took the results of the simulated scenarios to SPSS 15.0 (statistical package), to determine a set of regressions multiple linear lines that allow describing the output data as a function of the input data. As an example, the equation that represents the behavior of the queue of ships to berth is given by:

y = -11,810 – 3,425 x + 783,465 z (R-Squared = 0.91),
Where:
y = average queue size of ships to berth;
x = time between ship arrivals;
z = time between truck arrivals.

Results of this type allow the decision maker to quickly assess how increases in the arrival rate affect the average queue size, without having to resort to running new experiments to verify the impacts of a particular scenario.

Another area of ​​interest for the study was the container yard. The results obtained involve two aspects relevant to the analysis: the total volume of containers handled per year at the terminal and the number of positions needed to accommodate these containers on a daily basis. Regarding the volume handled per year, what is expected – and which can actually be seen in Graph 6 – is the increase in the volume handled at the terminal due to the increase in the rate of arrival of ships and the consequent increase in the rate of use of the terminals. cradles. Such information makes it possible to estimate the effective handling capacity of the terminal at close to 250 containers/year.

 

2009_07_image 07

However, in order to assess the number of storage positions needed to accommodate this annual volume, a survey of the fluctuation in the number of containers in the yard over time, day by day, should be carried out (according to Graph 7). As there is no effective managerial possibility of a ship or a truck having to wait due to lack of space in the yard, the number of positions must be at least equal to the registered daily peak, which is easily identified in the graph.

 2009_07_image 08


In this case, the container yard must have a capacity of at least 4.809 positions.

CONCLUSION

The simulation appears as an important tool for planning port operations. Furthermore, the results generated are of a managerial nature, making it possible to draw up strategies and direct investments. In our study, the first step was to identify the current configuration of port operations and then generate future scenarios, increasing the ship arrival rate, in order to finally be able to assess the impact generated on the terminal's performance.

The results showed that this increase in the ship arrival rate has a significant impact on the berth utilization rate and, consequently, on the size of the ship queue and the waiting time. It was also possible to observe that, from a berth utilization rate of 0,40, the performance levels for terminal users begin to prove to be unsatisfactory, signaling the imminent saturation of operations.

To avoid this scenario, it becomes necessary to increase the loading/unloading capacity in the existing berths or invest in the construction of another berth. However, it is important to highlight that no investment in increasing the capacity of the berths will have an effect if the terminal does not have enough positions to store all the containers, indicating, for the decision maker, the need for a careful analysis to identify the real system bottlenecks port.


BIBLIOGRAPHY

Bowersox, DJ. Logistic management. A systems integration of physical distribution and materials management. MacMillan Publishing Co.: New York. pp. 12–17, 1978.

Casaca, ACP. Simulation and the lean port environment. Maritime Economics & Logistics 7: 262-280, 2005.

Dragovic, B; Park, NK; Radmilovic, Z.; and Maras, V.. Simulation modeling of ship-berth link with priority service. Maritime Economics & Logistics 7: 316-355, 2005.

Hyland, T.. Four east coast ports share growth strategies. Transportation & Distribution, June 2001.

Kia, M.; Shayan, E; & Ghotb, F.. Investigating port capacity under a new approach by computer simulation. Computers and Industrial Engineering, 2002.

Luo, M.; Grigalunas, T.. A spatial-economic multimodal transportation simulation model for US coastal container ports. Maritime Economics & Logistics 5: 158-178, 2003.

Shabayek, AA and Yeung, WW. A simulation model for the Kwai Chung container terminal in Hong Kong. European Journal of Operational Research 140: 1–11, 2002.

Yun WY & Choi, YS. A simulation model for container-terminal operation analysis using an object-oriented approach. International Journal of Production Economics, pp. 221-230, 1999.

Wadhwa, LC. Capacity and performance of bulk handling ports. Proceedings of the Australian Transport Research Forum, vol 15, Part 1, 1990.

Wanke, PF & Cortes, J. D .. Port PCP: simulating the ship-anchor connection to reduce total demurrage costs (demurrage). Tecnologística Magazine – December, 2008, and January, 2009.

 

1 Region of the country served by means of land, river or lake transport routes, to which goods unloaded at the port are sent directly or from which goods originate for shipment at the same port.

Authors: Peter Wanke and Frederico Barros

https://ilos.com.br

Doctor of Science in Production Engineering from COPPE/UFRJ and visiting scholar at the Department of Marketing and Logistics at Ohio State University. He holds a Master's degree in Production Engineering from COPPE / UFRJ and a Production Engineer from the School of Engineering at the same university. Adjunct Professor at the COPPEAD Institute of Administration at UFRJ, coordinator of the Center for Studies in Logistics. He works in teaching, research, and consulting activities in the areas of facility location, simulation of logistics and transport systems, demand forecasting and planning, inventory management in supply chains, business unit efficiency analysis, and logistics strategy. He has more than 60 articles published in congresses, magazines and national and international journals, such as the International Journal of Physical Distribution & Logistics Management, International Journal of Operations & Production Management, International Journal of Production Economics, Transportation Research Part E, International Journal of Simulation & Process Modeling, Innovative Marketing and Brazilian Administration Review. He is one of the organizers of the books “Business Logistics – The Brazilian Perspective”, “Sales Forecast - Organizational Processes & Quantitative Methods”, “Logistics and Supply Chain Management: Product and Resource Flow Planning”, “Introduction to Planning of Logistics Networks: Applications in AIMMS” and “Introduction to Infrastructure Planning and Port Operations: Applications of Operational Research”. He is also the author of the book “Inventory Management in the Supply Chain – Decisions and Quantitative Models”.

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