SIMULATION AND OPTIMIZATION OF SELECTED CLASSIFICATION NUMBERS AT A CONTAINER TERMINAL: TECON - RIO GRANDE, BRAZIL

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1 SIMULATION AND OPTIMIZATION OF SELECTED CLASSIFICATION NUMBERS AT A CONTAINER TERMINAL: TECON - RIO GRANDE, BRAZIL Leif Hendrik Meier Andreas Lackner Helge Fischer Jörg Biethahn University of Göttingen, Platz der Göttinger Sieben 5, Göttingen, Germany { lmeier; alackne; hfische1; Antonio Carlos Gastaud Maçada Universidade Federal do Rio grande do Sul Washington Luis, 855 acgmacada@ea.ufrgs.br Abstract The following paper describes the results of a container terminal simulation at Tecon, Rio Grande. Most studies in this sector focus on optimizing one single decision field, e.g. berth allocation, vehicle dispatching, etc. In reality, these problems are dependant on each other and should not be separated in different simulation models. Up to now, there exist only few studies on integrated problems for container terminal optimization. Compared to the worldwide biggest container terminals like Singapure, Hong Kong or Hamburg Tecon turns out to be a good starting point to study the complex relationships of integrated problems. This paper is organized as follows: We focus on the vessel process and give a literature review on decision problems, which occur within this process. We then introduce our simulation model and investigate different strategies for each decision problem in order to minimize the vessel s time in port. The simulation model has been developed with Arena 5.0 from Rockwell Software. The research project is part of a Capes/DAAD programme. Keywords: Container terminal, simulation, integrated optimization, Arena 1 Introduction Literature concerning container terminal problems increased rapidly the last few years. Up to now, only a few studies on integrated optimization for container terminal problems exist. This field of investigation is extremely complex. Most studies concentrate on separated decision problems, for example berth allocation, stowage planning, vehicle dispatching. Steenken et al. [St2004] provide an extensive overview. Belonging to Henesey et al. [He2004] there exist five important approaches to start a container terminal optimization: 1. Increase the length or number of berths 2. Increase the productivity by acquiring new technology or machinery 3. Increase the time that berths can be operated 4. Improve the efficiency in allocation of resources 5. Improve policies and management decisions Only point 4 and 5 are considered in this model, because they may influence the situation even in a short-term. Tecon is already operating 24/7, so there is no more potential in point 3. Point 1 and 2 are long-term, strategic decisions and not yet considered in this model. They may be taken into account in a further step. In the simulation model the process from vessel s arrival to departure should be analyzed. This process contains berth allocation decision problems, crane split, vehicle dispatching and also management

2 decisions concerning the number of reach stackers, trucks, etc. Main decisions in this process should be taken into account. The goal is to understand the behaviour of interacting decision problems. The simulation model maps the following process: Figure 1: Vessel process The figure 1 shows events and functions. Every function contains several decision problems to be solved and is completed by an event. Decisions on berth and equipment allocation as well as vessel s discharge and loading processes will have great influence on the whole process especially on one important classification number: Time in Port (TiP) of a vessel. This model focuses on minimizing the Time in Port, which is defined as follows: TiP = t wait + tcheck + in, out t operation Models in literature provide a multiplicity of approaches for separated optimization. This simulation model is based on these literature ideas and integrates selected methods into the corresponding problem area. In this way it is possible to analyze the behaviour of complex decisions and from a more abstract point of view their efficiency of co-operation, because of considering these decisions as one part of the vessel process. 2 Literature review An extensive general overview of existing literature concerning container terminal problems was published by Steenken et al. in There are further overviews published by Vis and de Koster [VK2003] in 2003 and Meersmans and Dekker [MD2001] in Meersmans and Wagelmans [MW2001] give a first approach for integrated scheduling of handling equipment at automated container terminals. Separated problem areas have been handled by many different authors. The number of researches in the field of container terminal optimization increased in the last ten years. This chapter summarizes selected articles which have influenced this simulation model. Henesey et al. [He2004] describe and evaluate two different berth allocation policies: The berth closest to stack policy (BCSP) places a vessel to a target stack. The target stack is the stack where most of the containers will be stored during the operations. BCSP waits until a berth closest to the stack is available. The shortest turn around time policy (STTP) is considering the sum of service and waiting time. The vessel will be placed wherever the shortest turn around time is achieved. [He2004] 2273

3 Daganzo [Da1988] considers the crane scheduling problem. In this approach the goal is to turn around all the vessels in a way that minimizes all the vessels waiting costs. Researches in recent time tend to solve this problem in combination with the berth allocation problem, for example in Park and Kim [PK2003]. Böse et al. [Bö2000] develop a model for vehicle dispatching at a seaport container terminal. Its goal is to maximize the Quay crane (QC) productivity by minimizing the waiting times of QC and Straddle carrier (SC). Dynamic (a fixed number of SC can perform transports for all QC), semi-dynamic (a fixed number of SC is assigned to the QC of one vessel) and static (one SC is assigned to one QC) assignment strategies are presented. It is easy to convert this approach to the Tecon situation. Tecon is working with Trucks and Reach Stackers instead of SC in the terminal. The approaches described in this chapter have been integrated into our simulation model. 3 Methodology 3.1 Decisions In order to minimize the Time in Port and to optimize the allocation of resources and management policies, there are several decisions to be made at a container terminal. As in Böse et al. [Bö2000] proposed we minimize the waiting times between handshake operations. The discharging process is characterized as follows: A quay crane has to wait if there is no truck available to take over the container. The next handshake takes place between truck and reach stacker. Trucks have to wait at the respective yard position until the assigned reach stacker is free to take over the container again. The loading process is organized the other way round. Our approach is to deal with several strategies in order to minimize the handshake waiting times, which (should) result in lower Time in Port for vessels. In addition, simulation allows us getting more information about the system s behaviour. This knowledge can build a basis for further research with a more complex integrated model. The model layout is taken from Tecon. There are two berth places, five quay cranes, thirty trucks and (assumed) seven reach stacker available. A yard layout plan with distances is given in Annex 3. This model deals with the following decisions: 1. Berth allocation 2. Crane split 3. Vehicle dispatching 4. Reach stacker distribution at the yard Approaches in berth allocation First in, first served (FIFS) Transport minimization: STTP Transport minimization: BCSP Approaches in crane allocation The following six different crane situations are considered in this model: 2274

4 Figure 2: Crane situations The crane situations (1) to (4) describe a (3/2) crane split, the situations (5) and (6) consider a (4/1) crane split. The goal is to assign these QC so that the Time in Port for vessels will be minimized. If there are four QCs working for one vessel (assuming that this is possible in practise) this vessel has the possibility to be operated very fast. This possibility assumes that there have to be enough resources (Trucks and RS) working in the background. There is only one QC left to operate on the second vessel. We will analyze if and when it is useful to change the setup to these situations. There are fixed and dynamic approaches implemented in this model. Fixed approaches rest in their given crane situation the whole time period. Dynamic approaches will change their situation to different positions, dependant on given criteria. Approaches in truck allocation static (six trucks for one QC) semi- dynamic dynamic The dynamic approach is implemented in this model as follows. Trucks set to be dynamic will do services for all quay cranes and will go back to a common meeting point after this job. Static trucks will work only for one QC assigned. The dynamic approach is simulated in a first step with 15 trucks working dynamically and 3 trucks statically assigned to each quay crane. This setup can be changed via parameters in the model to do further simulations. The semi-dynamic approach is not implemented in the simulation model. It is mentioned for further research. Approaches in reach stacker distribution basic situation increasing number of reach stackers in critical areas This model considers the interaction of these four problem areas and not only from isolated point of view. It is simulated a mix of these approaches, in order to analyze the minimum vessel s time in port. There are also three basic parameters evaluated from Tecon data: 1. Number of import container per vessel 2. Number of export container per vessel 3. Arrival rate for each vessel 2275

5 3.2 Model assumptions Because of the complexity it is not possible to model the container terminal reality in a simple model. Therefore assumptions had to be made and will be pointed out in this part. 1. As already shown in figure 1 this model considers only the vessel process, from arrival to departure. Hinterland processes are not considered. Export containers arrive at the terminal when needed without delays. There are no capacity problems at the yard. Every container is staying at the right place. 2. The yard address is fixed by its row number (A to O) and its part number (I or II). This is to develop a simple but problem satisfying yard model. 3. During vessel operations, delays occur only because of missing QC, trucks and RS. Other problems like additional container movements, shift changeover, weather, failures, strike, etc. do not appear. 4. Every QC is able to move on every position and is able to lift every container. Velocities of technical equipment are fixed and constant. Equipment deadlocks and blockage do not occur. 5. Stowage planning and yard planning are assumed to be given by the information system. 6. Algorithms can be implemented in a more realistic way considering more restrictions which occur in practise. 3.3 Running the simulation Every simulation is running with 50 replications. The simulated time period is one month, 24 hours a day, 7 days a week. To verify the model, we have to compare Tecon s original figures and output data of the model. The basic situation representing the Tecon situation in the model is assumed to be the following: First in, first served - berth allocation (3/ 2) crane allocation, exchange of one crane is possible static truck allocation, six trucks per crane RS working in an assigned area 4 Analyzing simulation results 4.1 Basic results and model failure Based on the assumptions mentioned above, the model failure can be calculated as followed: Δ TiP = TiP Tecon TiPmod el, where TiP Tecon = tb, Tecon + t w, Tecon and TiPmod el = tb,mod el + t w, mod el and berthed time t b, waiting time t w. The following figure illustrates the model results and shows the model failure Δ TiP. 2276

6 Figure 3: Simulation results - overview ΔTiP is calculated as follows: Δ TiP = TiP Tecon TiP = h h = h. mod el Considering the assumptions mentioned above this, the difference between model results and reality data is less than 5%. From about 300 scenarios simulated, one can see in Figure 3 that most of them show much worse results with up to 60 hours Time in Port. There are also results showing better TiP and we ll have a look at the reasons. The Time in Port consists of waiting time t w and berthed time t b, so we can do a more detailed analysis: Δ TiP = ΔW + ΔB, with Δ W = t w t and Δ B = t b t, Tecon w, mod el, Tecon b, mod el Figure 4: Simulation results scatter plot Figure 4 shows a scatter plot of all simulation results. One can see that there are a lot of scenarios with shorter berthed time, but also higher waiting time than in the original situation. The point of intersections is showing the original situation. There are also a few scenarios, where both criteria, tb and t w have better results. The success factors of these scenarios should be analysed in detail. The best scenarios in figure 4 are situated in the left bottom corner. 2277

7 4.2 Success factors of scenarios in the model Properties of best scenarios in the model are: - First in, first served berth allocation; - (3/2) - balanced crane allocation, exchange of one crane is possible and also static crane allocation; - Dynamic truck allocation. To judge the quality of these results, we will have to observe each result in detail Impact of berth allocation decisions During this simulation several berth allocation methods have been compared. Especially the results of transport minimizing decisions (Shortest turn around time and also berth closed to stack policy) and time minimizing decisions (first in, first served) have been analyzed. Figure 5: Comparison STTP, BCSP, FIFS Figure 5 shows the filtered results with reference to the berth allocation decision. It shows that most scenarios with transport minimizing methods are situated in the left part, so they have a better (lower) berthed time t b, but also a (much) higher waiting time. This is easy to understand, because vessels have to wait longer for a better (transport minimizing) berth place. The advantage of lower t b does not compensate the higher value fort w. The first in, first served strategy seems to be optimal for Tecon in this situation. The small terminal (compared to larger CTs like Hamburg, Rotterdam) and only two berth places do not yet justify a transport minimizing berth allocation strategy like STTP and BCSP- as the results in Figure 5 show. There are even FIFS- scenarios with lower t b andt w. This will be analyzed in the next part. 2278

8 4.2.2 Impact of crane allocation decisions Figure 6: Comparison crane allocation Figure 6 shows box plots for several crane allocation decisions. In order to minimize the time in port of a vessel the following allocation strategies show best results: Dynamic (1) Fixed situation (1) (2) (3) - (4) These strategies have in common, that they do not use (4-1) crane split, so that there are two QC for each vessel at least. Tecon has five QC available. Dynamic strategies will switch between crane situations, as soon as possible. Dynamic 1 switches between crane situations (1) and (2), so at least there are two QC available [Compare Figure 2]. Dynamic 2 switches between crane situations (5) and (6), so one berth place has one QC and the other berth place has four QC available. This strategy has a strong impact on the time in port. Dynamic 3 switches between crane situations (1), (2), (5) and (6). Fixed allocations have only theoretical meaning, but they show how important it is for Tecon to use a balanced (3/2) distribution for the crane split Impact of truck allocation decisions In this simulation dynamic and static truck allocation were compared. The following table shows the results. The table shows the Time in Port in hours, for static and dynamic truck allocation. One can see that dynamic allocation leads to a lower Time in Port. One has to be careful with this result- in fact dynamic truck allocation can lead to a better truck occupancy and efficiency. In this model it is assumed that there is no blockage and traffic jam. Therefore one has to check if this assumption may be followed in reality. Also this simulation investigated only a full dynamic strategy, where a dynamic truck may work for every QC at every berth place. A semi dynamic strategy is also possible: trucks serve every QC at a fixed berth place, which is not simulated in this model. 2279

9 4.3 Capacity analysis Tecon is planning to build a third berth place to offer enough capacity for the growing demand in the future. Within this simulation, capacity has been analysed to demonstrate that this step will be necessary. Figure 7: Correlation - Berth occupancy & Waiting time Figure 7 shows the capacity situation with two berth places. The left figure is done with Tecon data, the right one is calculated from the model by rising the vessel arrival rate. It is obvious that there are important steps. With less than 65% berth occupancy, there is a waiting time of less than ten hours. Between 70 and 80 % occupancy the waiting time increases strongly. With more than 85 % percent berth occupancy, Tecon would have waiting times of more than 20 hours, which is according to Tecon too high for practical usage. This calculation shows that a third berth place will be necessary in the future. 5 Conclusion The simulation was done for the following Tecon situation: Two berth places Five quay cranes Thirty trucks available Seven reach stacker working in separated yard areas The layout plan and equipment velocities are displayed in Annex A2 and A3. This simulation showed that a first in first served strategy in berth allocation, a balanced crane allocation [(3-2) not (4-1)] and a dynamic truck allocation are the best strategies to minimize the time in port of a vessel for Tecon. These results should be seen with regard to the model assumptions mentioned above. This study identified improvement opportunities like dynamic truck allocation, but also it could show that the decision system Also it showed that a third berth place is necessary in the future. A next step should be to analyse the future layout within this simulation. Because of a larger terminal size and a longer quay length, 2280

10 transport-oriented berth allocation decisions like STTP and BCSP will become more important and may have influences to other decisions as well. 6 Acknowledgements The simulation is based on data from Tecon, Rio Grande, Brazil. Appreciation for assisting the development process: Marina Vidal dos Santos and Thierry Rios, Tecon S.A., Rio Grande and for financial support to DAAD (German Academic Exchange Service). 7 References Böse, Jürgen et al. [Vehicle Dispatching, 2000]: Vehicle Dispatching at a Seaport Container Terminal Using Evolutionary Algorithms. Proceedings of the 33 rd Hawaii International Conference on System Sciences, Daganzo, Carlos F. [Crane scheduling, 1989]: The crane scheduling problem. In: Transportation Research B 23 (1989), Issue 3, Pages Henesey, Lawrence et al. [Simulation, 2004]: Using simulation in evaluating Berth allocation at a Container Terminal, 3 rd International Conference on Computer applications and Information Technologies in the Maritime Industries, Meersmans, Patrick; Dekker, Rommert [Container handling, 2001]: Operations Research supports container handling. In: Economic Institute report EI , Meersmans, Patrick; Wagelmans, Albert [Integrated scheduling, 2001]: Effective algorithms for integrated scheduling of handling equipment at automated container terminals. Econometric Institute report EI , Park, Young-Man; Kim, Kap H. [Berth and Quay cranes, 2003]: A scheduling method for berth and quay cranes. In: OR Spectrum, 2003, Issue 25, Pages Steenken, D. et al [Container Terminal Operations, 2004]: Container Terminal operation and Operations Research a classification and literature review. In: OR Spectrum, 2004, Issue 26, Pages Vis, Iris F.A.; de Koster, R. [Transshipment, 2003]: Transshipment of containers at a container terminal: an overview. In: European Journal of operational research, 2003, Issue 147, Pages

11 Annex Annex A1: Animation of container terminal processes in Arena A2: Velocities of terminal equipment in the model (average) Transporter Arena-identifier Veloctity in m/min Velocity in km/h Quay crane Crane Transporter 60 3,6 Yard tractor Truck Transporter ,2 Reach Stacker Reach Stacker Transporter 120 7,2 A3: Yard-distancees 2282