# Simulation of Container Queues for Port Investment Decisions

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1 The Sixth International Symposium on Operations Research and Its Applications (ISORA 06) Xinjiang, China, August 8 12, 2006 Copyright 2006 ORSC & APORC pp Simulation of Container Queues for Port Investment Decisions Mohammad Ali Alattar 1, Bilavari Karkare 2, Neela Rajhans 3, 1 Dept. of Civil Engineering, School of Engineering and Petroleum, Kuwait University, Kuwait 2 Dept. of Civil Engineering, Army Institute of Technology, Pune , India 3 Dept. of Industrial Engineering, Vishwakarma Institute of Technology, Pune, India Abstract Due to congestion of port or due to low tide situation or otherwise, sometimes the ships cannot come to the port directly. In such a situation, the ships are anchored offshore and the containers are transported to the ships through small crafts. This paper simulates this condition to find out the queue of containers at the port and also analyses the effect of increase in the facilities at the port to reduce this queue. However, a cost benefit analysis needs to be done for increasing the facilities at the port i.e. increasing the number of cranes and number of berths. The objective here is to analyse the effectiveness of the suggested solution before investing in increase in facilities. Thus, instead of actually implementing the solution, the advantages and disadvantages of the suggested solution are studied by simulation technique. Keywords simulation; queuing; ships; containers; port investment 1 Introduction Operations at port are very complicated. The complete situation at the port is very difficult to formulate as a single problem and can be divided into various sub-problems. Each sub-problem in turn can be solved to achieve optimum results. The sub-problems thus solved, may not achieve optimum results for the complete problem. Technically we can say that the local optima thus obtained may not lead to global optima. The situation therefore, needs to be analysed towards achieving global optima. In addition to this, condition at every port is different and demands various solutions. An attempt therefore is made here to analyse the situation and obtain a generalized solution, which can be applied at various ports. The research may also try to look into similar condition at airports, railways, etc. The general condition at any sea port can be explained as follows:

3 Simulation of Container Queues for Port Investment Decisions 157 decision support system in analysing the assignment of berths to arriving container ships at a container terminal. The main objective was to improve the performance of the container terminal by efficiently utilising the resources available. The system is defined as Berth Allocation Management System (BAMS) as a part of decision support system. The main objective here was to assist in creating berth schedules for arriving ships under various conditions. Efficient planning of berth allocation for container terminals in Asia was studied and discussed by Imai et al. [6]. The objective here was to utilize the terminal efficiently for container ports. The paper focuses on berth allocation that minimizes dissatisfaction of the ships in terms of berthing order and minimizing the sum of the time the ships spend waiting for berths. An algorithm is presented in the paper, which identifies non-inferior solutions to the berth allocation problem. The berth allocation problems generally discussed are static in nature, in the sense that the allocations are not changed with respect to time. But in actual practice, the change in the itinerary, tidal conditions, non-availability of resources, etc. affect the allocation of ships to various berths. Imai et al. [7] developed a heuristic procedure based on Lagrangian relaxation. They conducted a large amount of computational experiments to show that the proposed algorithm could be easily adapted in the real life conditions. Berth allocation planning for the ships in the public berth system was done using genetic algorithm by Nishimura et al. [8]. A heuristic procedure was developed based on genetic algorithm. The algorithm was tested using various real life problems and it was found that the algorithm was adaptable to real life situations. Major differences between standard vehicle routing scheduling problems and ship routing problem as discussed by Ronen [9] are: 1. Each ship has unique operating characteristics such as capacity, speed, cost structure, etc. Due to market fluctuations, even two identical ships may have different cost structure. 2. The scheduling environment depends to a great extent on the mode of operation of the ship. 3. Ships do not return necessarily return to their origin. 4. Higher uncertainty is involved in scheduling ships due to longer voyages. 5. Ships are operated round the clock while vehicles are usually not operated during the night (except few vehicles such as truck). Thus ships do not have planned idle periods, which absorb delays in operations. 6. Destination of ships may be changed at sea. The review emphasises the fact that the objective of ship routing and scheduling is not always clear, especially in cases where not all cargoes available are known in advance. Linear operations try to maximize the profit per time unit in the long run but may divert from this objective in the short run in order to gain market share. A conceptual model for high speed vessels was developed by Lagoudis et al. [10]. The paper reviews the role of high speed vessels in the context of the total

6 160 The Sixth International Symposium on Operations Research and Its Applications Figure 3: Option B : 1 Berth and 3 cranes Figure 4: Option C : 2 Berths and 3 cranes Simulation is a powerful tool to evaluate performance of proposed system and choosing appropriate design before actually implementing it. It helps in visualising the solution before implementation. The advantages of simulation can be as follows: 1. The complete problem can be divided into small problems. 2. Each sub-problem can be treated as a separate problem and yet a global optima can be achieved. 3. Facilities can be added or removed at any time. 4. Arrival as well as service distributions as well as their parameters can be changed at any moment and results can be obtained within minutes. 5. Separate distribution can be assigned to individual facility. 6. Simulation models can be realistic. Since they are not equation based, linearity, differentiability, time dependencies etc. are not the issues. The number of complex systems subject to realistic simulation-based experimentation is much greater than the number subject to realistic mathematical modelling. 7. The system whose behaviour is to be investigated need not actually exist. It has to exist in the mind of the designer. 8. Time can be compressed in simulation models. The equivalent of days, weeks, and months of real time operation often can be simulated only in seconds, minutes or hours on computer. Thus a large number of simulated alternatives can

7 Simulation of Container Queues for Port Investment Decisions 161 be investigated, and results can be made available soon enough to influence the choice of design for a system. 9. All variables can be held constant, except those whose influence is being studied. As a result, the possible effect of uncontrolled variable on system behaviour need not be taken into account, as is done when experiments are performed on a real system. Some of the disadvantages of simulation can be: 1. Past data must be available to predict the situation. 2. It can analyse various conditions but can not give the best solution. 3. A person should be educated in a variety of areas before becoming a simulation practitioner. 4. Simulation studies also involve cost, such as cost of hardware, software, training, etc. Steps in simulation studies: 1. To formulate the problem and plan the study. 2. To collect data and define a model. 3. To validate the model. 4. To construct computer model and verify it. 5. To take pilot runs. 6. To validate the model again. 7. To Design Experiments. 8. To take production runs. 9. To analyse the output data. 10. To document and implement the results. 3.1 GPSS/H modelling language A system under study is a collection of interrelated elements that work together to achieve a stated objective. A GPSS/H model takes the form of a series of statements. A GPSS/H model can be expressed as a Block Diagram, or as the statement equivalent of a Block Diagram. Units of traffic move along the one-way paths in a block diagram. The name transaction is given to a unit traffic in GPSS/H. The movement of transactions from block to block as a simulation proceeds is a vital part of GPSS/H. A model is built by selecting appropriate blocks from the available types. Selected blocks are then sequenced in a Block diagram to form patterns, corresponding to patterns in the system being modelled. The Blocks are used to represent such things as system resource, information gathering and decision making capabilities. The physical and logical aspects of the system being modelled and the type of information that the model is to provide, determine which Blocks are used in constructing the model. When a model is executed by computer, it is the movement of units of traffic from Block to Block that is analogues to (simulates) the movement of traffic through the system being modelled. As the simulation proceeds, transactions in

8 162 The Sixth International Symposium on Operations Research and Its Applications Table 1: Summary of relevant simulated results Option O Option A Option B Option C Maximum queue at port Maximum queue (for each berth) at berth Crane utilization 97% 96% 82% 78% Average waiting 8.81 hrs hrs hrs hrs. time per craft GPSS/H move along path from Block to Block in a model. Each block represents an action to be performed whenever the Block is executed. The GENERATE block creates a transaction while TERMINATE block destroys the transaction. When traffic moves in a system it reaches a point at which it pauses and spends time before returning its movement. These points frequently correspond to locations in a system at which traffic receives service. This condition is depicted by ADVANCE block. The service times required can have various distributions and varied parameters. QUEUE and DEPART blocks collect the statistical data about the system. These blocks are mainly used to find WIP at various stages of the system. SEIZE and RELEASE blocks are used to engage and disengage a facility or service. ENTER and LEAVE replace SEIZE and RELEASE when there are multiple facilities or service stations available. The system is simulated using GPSS/H package and the results obtained are summarised in table 1. The program and output files are attached at the end of the report. 4 Results and Conclusions The system is simulated for all the four situations and the results obtained are as follows: 1. Queue at the port decreases by increasing the number of cranes but an increase in the queue is observed at the berth. 2. Although the simulation results show that the maximum queue contents at the port are reduced, this simulation does not consider the breakdown of the cranes, which is a common feature. In addition to this, parallel movement of the cranes is not possible in all cases. 3. Crane utilization decreases with increase in number of cranes. 4. Average waiting time of the craft increases drastically. The crafts have to wait before coming to berth since they are getting loaded faster due to increase in number of cranes. 5. The increase in queue at berth demands a cost benefit analysis to be done before adding cranes and berth.

9 Simulation of Container Queues for Port Investment Decisions Average waiting time of the craft in option c shows a decrease but this time is at two berths. Thus total time is 7 *2 = 14 hrs. approx. Thus it can be concluded that increase in the port facility does not increase the output. This increase leads to container queues at berth. Since this queue is not desirable, the solutions given by options A, B and C do not work out to be feasible solutions. References [1] E. D. Edmond and R. P. Maggs. How useful are queue models in port investment decisions for container berths? Journal of Operations research Society, 29(8), [2] Jose Holguin-Veras and Sergio Jara-Diaz. Practical implications of optimal space allocation and pricing. Ports, 98, [3] G. E. Horne and T. Z. Irony. Queuing processes and trade-offs during ship-toshore transfer of cargo. Naval Research Logistics, 41, , [4] P. Schonfeld and O. Sharafeldien. Optimal Berth and Crane Combinations in Container Ports. Journal of Waterway, Port, Coastal and Ocean Engineering, 111(6), , [5] L. Henesey, P. Davidsson and J. A. Persson. Using simulation in evaluating berth allocation at a container terminal. In Proceedings for the 3 rd International Conference on Computer Applications and Information Technology in the Maritime Industries (COMPIT 04), 61 72, Siguenza, Spain. [6] A. Imai, K. Nagaiwa and C. W. Tai. Efficient planning of berth allocations for container terminals in Asia. Journal of Advanced transportation, 31(1), [7] A. Imai, E. Nishimura and S. Papadimitriou. The dynamic berth allocation problem for a container terminal port. Transportation Research Part B, 35, , [8] E. Nishimura, A. Imai and S. Papadimitriou. Berth allocation planning in the public berth system by genetic algorithms. European Journal of Operational Research, 131, , [9] David Ronen. Cargo ships routing and scheduling: survey of models and problems. European Journal of Operations Research, 12, , [10] I. N. Lagoudis, C. S. Lalwani, M. M. Naim and J.King. Defining a conceptual model for high-speed vessels. International Journal of Transport Management, 1, 69 78, 2002.

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