A Throughput Simulation of Port of Beirut

A Throughput Simulation of Port of Beirut Jean-Paul Arnaout, Ph.D., [email protected] Samar Hallab Caline El Khoury Lebanese American University, Byblos, Lebanon

Keywords: Port Operations, Risk Assessment, Security Incidents Abstract Up to 600,000 tons of goods (imports & exports) flow yearly through Port of Beirut (POB). Hence, an incident at the port (Natural or manmade) that results in its congestion or blockage will have distressing consequences not only to the Lebanese community, but also to the regional trade and economy. In this paper, a simulation model of the port operations up to the container level is developed. The model allowed us to analyze and evaluate system changes such as resource capacities and security changes that affect the port’s operations, and observe their impact on the port’s performance. The simulation results indicated that the current port capacity levels are sufficient for daily normal operations. Furthermore, several security incidents were tested and their impacts highlighted. 1.

INTRODUCTION The interest in studying the performance of ports has increased across the globe, and simulation analysis proved to be an effective tool to understand vessels’ operations. Such an approach identifies bottlenecks and searches for opportunities to improve efficiently. Through simulation, one can analyze the impact of schedules’ optimization and get new insights into certain areas of the port. Port managers can study the capacity of containers and trace the causes of delays in order to forecast trade level more accurately. In addition, they can apply alternative operating rules on the computer model rather than on the real port system and study the best scenario available. Areas in the topics being simulated expand to include the analysis of contingencies outcome as in cases of breakdowns or natural events. Simulating ports have an additional value that includes animation. The latter is a tool that helps capturing the real port process and review the modeling logic. The operational planning of containers managed by automated material handling equipment is enhanced using the simulation package. Numerical data on port performance, delays, resource utilization, total time in the system and operating

costs is generated by the model. Simulation services include optimization tools that can be easily employed to optimize capital investment and operating costs. Literature includes various examples on how practitioners benefited from the simulation process of Ports in the attempt to improve the desired performance measures. Stuff In this study, we develop a discrete-event port simulation model to evaluate the impact of certain security and natural incident scenarios on terminal throughput, delays, queues, and/or resource utilization. Simulation models have been used to evaluate scenarios or changes to systems before they occur in order to better understand the associated risk. Discrete-event simulation was selected as the tool of choice due to its ability to capture the dynamics of complex systems and model stochastic processes. A simulation model is likely the easiest approach for observing system outcomes from injecting the system with scenarios of different consequence levels. The advantage of a simulation model is that it mimics the actual system under study. In other words, once the model is developed, verified, and validated, it will behave identically to the port. The model can be used to test any risk scenario and capture the impact and consequences of this risk without perturbing the actual system (i.e. the port). It has been demonstrated that the discrete-event simulation model is an effective tool for modeling operations such as those of a port. For example, Shabayek and Yeung (2002) developed a discrete-event simulation model to simulate the operations of Kwai Chung container terminals in Hong Kong. Parola and Sciomachen (2005) have used simulation to model a port system in Italy. Leathrum et al. (2004) have used discrete-event simulation for modeling military port operations. Bruzzone et al. (2000) proposed a simulation approach for risk analysis applied to harbor and maritime environment. Port simulation models have also been developed by Koh et al. (1994) and Legato and Mazza (2001). The rest of this paper is organized as follows. In Section 2, an overview of the terminal processes and ships and trucks’ flows is given. In Section 3, the port simulation model is described along with its verification and validation and the generated input distributions. Experiments and

results are highlighted in Section 4. Finally, we conclude with the significant outcomes this study in Section 5. 2.

OVERVIEW OF TERMINAL PROCESSES The port addressed in this paper is a Lebanese container port that handles cargo stored in containers, generally 20 or 40 feet in length without wheels; i.e. as one TEU (twentyfoot equivalent unit) or as one FEU (forty-foot equivalent unit). This port is considered an intermodal node in the transportation network, where cargo changes modes of transportation (e.g. from a ship to an inland transport mode and vice versa). Note that as no rail infrastructure exists in Lebanon, the transportation modes are restricted to ships and trucks. The modeling of this port was designed at the level of containers, trucks, cranes, reach stackers, Rubber Tyred Gantry (RTG) Cranes and ships. Other resources and components that are at a lower level were considered either embedded in the ones listed above or always available (such as personnel). The flow of operations at most container ports including the one described here can be categorized as follows. 2.1. Trucks Flow As highlighted in Figure 1, trucks arrive at three different inspection gates. After the Security check, they undergo one of the three sequences: unload the container in the designated storage area, just load a container, or just unload. Containers are loaded or unloaded using a RTG or a reach stacker. Afterwards, the truck departs the port.

2.2. Ships Flow Ships arrive with different lengths and undergo a delay before they berth. One of the three quays considered, quay xxx, is characterized by having 6 sea-to-shore (STS) cranes. The other two, quay x and xx, use different container handling tools, mainly, mobile cranes. In quay xxx, ships with both loading and unloading operations begin with unloading containers using the STS cranes onto terminal trucks. RTGs or reach stalkers are then used to deposit containers in their designated storage areas. Upon the finishing of unloading process, that of loading is initiated. Terminal trucks go to the selected storage spot and each is loaded with a container by an RTG or reach stalker. Then trucks head towards the quay and STS are used to deposit the containers on ships. Once the process is over, ships are delayed before they departure. In quays x and xx, instead of using STS cranes, top mobile cranes are used from ship to floor or from truck to ship. The rest remains the same as in quay xxx. When the case is a sole operation, either loading or unloading, the ship performs the designated process, experiences a delay upon finishing, and departs. The ships’ flow is highlighted in Figure 2. 3.

SIMULATION MODEL The model consists of several components that capture the dynamic movements described in the previous section. These components are Entities, Resources, Processes, and Transporters as shown in Table 1. The Simulation model logic is described in Figure 3.

 

Figure 1. Trucks Flow

Figure 2. Ships Flow 3.1. Input Analysis As any real system is subject to randomness, the simulation model must include realistic levels of input uncertainty. Following this, the port historical data was used to generate statistical distributions. The data was fitted into distributions and tested using Chi-Square and Kolmogorov-Smirnov (KS) goodness-of-fit tests to ensure they are a good representation of the real processes (Devore, 2007). The tool used was Input Analyzer from Rockwell Systems, and a sample of the latter’s output for ships interarrivals is shown in Figure 4. The following inputs were based on historical data:  Inter-arrivals of Ships (exponential distribution)  Delay until Berth of Ships (beta and exponential distribution)  Delay until actual start of operations of ships (lognormal distribution)  Delay until departure after loading and/or unloading processes are over (beta and erlang distribution)  Total number of containers to both load and unload (weibull distribution)  Number of containers to unload (exponential distribution)  Number of containers to load (weibull distribution)  Number of containers for a single loading operation (triangular and uniform distribution)

      

Number of containers for a single unloading operation(beta and weibull distribution) Time to load container on ship ( uniform distribution) Time to load container on terminal truck (uniform distribution) Time to unload container from ship ( uniform distribution) Time to unload container from a terminal truck (uniform distribution) Time for a top loader or a reach stalker to load containers (uniform distribution) Inter-arrivals of Trucks (empirical distribution) Table 1. Model Components

Figure 3. Simulation Model Logic

Figure 4. Sample of Input Analysis (Ships Interarrivals) 3.2. Model Verification and Validation For Verification is the process of ensuring that the simulation model behaves in the way it was intended according to the modeling assumptions made (Kelton et al., 2004; Law and Kelton, 2000). Different methods were applied in verifying the behavior of our model: 1.

2.

3.

We used first deterministic data instead of distributions for the input data; this allowed us to predict the system’s behavior. We let only a single entity enters the system, and then followed this entity through all the decisions nodes to ensure that the model’s logic is correct. We monitored the model’s animation, which made it easier to detect any errors in our logic. Figure 5. Ships and Trucks' Interarrival Times

The simulation validation method followed in this paper is to compare the model’s outputs to the real port historical data. First, Figures 5, 6, and 7 show samples of the closeness of the simulation data to the historical ones with respect to input. Next, Figures 8 and 9 reflect the validity of the simulation model with respect to output. In particular, Figure 8 and 9 highlight the Ships and Trucks statistical accumulators respectively.

4.

SIMULATION MODEL IMPLEMENTATION The above port simulation model was implemented on Arena 14 from Rockwell Systems. We ran the simulation model for 10 replications; the half width obtained (for a 95% CI) was fairly large. We decided on the tolerable half width that we want and substituted the appropriate values in the following equation: ≅

where n0 and h0 refer respectively to the initial replication number (10) and its associated half width, h refers to the desired half width (tolerable), and n is the number of needed replications (n = 50).

Figure 9. Trucks’ Statistical Accumulators

Figure 6. Ships Input Delays

Figure 7. Number of all types of Containers

Figure 8. Ships’ Statistical Accumulators

4.1. Assessment of Current Port Operations After running the model and observing the performance measures of interest, it was obvious that the current level of resources, transporters, and cranes is sufficient to ensure a normal operation of the port with no bottlenecks nor congestion. This is assuming that the flows and operations in the port remained at their prior historical data. . 4.2. Possible Security Incident Scenario While the port’s capacity is sufficient for the normal operation of the latter, it is of interest to test unforeseen scenarios that can arise to see their impact on the port. Having said this, several scenarios were tested and the results/assessments are as follows. 4.2.1. Scenario 1 This scenario was adopted from Rabadi et al. (2007) and modified for the case of Lebanon. Lebanese intelligence agencies intercept information on a plan for a major terrorist attack on the Lebanese transportation infrastructure that may take place around the middle of the month. Specifically, their plan is to use empty containers that in reality are loaded with explosives. Consequently, severe requirements were enforced on all containers entering the truck gates to be opened and inspected, which is expected to increase the delay at the police gates of an average of 5 minutes per truck. The question the port authority is interested in answering is what impact the added security procedure will have on the port in terms of delays, queue times, and throughput? After running the simulation model, the following observations were drawn for the day that included the security incident:  The number of full containers on terminal has increased by 22%.  The average queue for trucks at the police gates has increased by more than 200%.  An increase in the truck turnaround time by 8%.

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An excessive trucks’ queue that will immobilize the highway adjacent to the port.

4.2.2. Scenario 2 This scenario tackles an excessive security check on the ship when it is berthed. After information came concerning a possible unforeseen security incident, the port directory needs to contact the responsible agency according to the tip they have received. Between the response time of the governmental agencies, and the inspection needed relevant to the severity of the situation, added time on the delay to start the actual operations is estimated between one and ten hours. What impact this added security procedure holds is also answered in terms of the ship total time in the system, waiting time to load/unload containers from and into storage areas, and the cranes utilization. After running the model, the performance factors resulted in the following measurements:  11.27% increase in ship total time in system  5% decrease in Ship to Shore Cranes Utilizations  9.8% decrease in waiting time in queues for loading or unloading containers from or into storage areas 4.2.3. Scenario 3 With the current situation in the Middle East and the incidents happening in Syria, rerouting of Ships heading originally to Syria is an inevitable step; thus, since the Beirut Port is the neighboring and the closest facility to Syria, it is expected that ships change destinations to both port of Beirut and that of Tripoli. Since the former is larger in capacity than the latter, the ships inter-arrival time is assumed to decrease by 75 %, where the remaining ships go to Tripoli. The port directory must cope with this situation, but first it needs to learn about the effect of ship congestion at the port. For this reason, the ship total time in the system is an important factor to look into. Add to this, the waiting time of terminal trucks in storage area. This may call to increase their number by buying more transporters if feasible, or renting some. Since resources are expected to have an increase in their utilization, the Ship to Shore cranes and mobile cranes are studied as well. After running the simulation model, the port managers can benefit from the following recorded results to direct their planning strategies into a more realistic direction:  9.35% increase in ship total time in system  11% increase in Ship to Shore Cranes Utilizations  8.11% increase in mobile cranes utilization  49% increase in waiting time in queues for loading or unloading containers from or into storage areas 5.

CONCLUSIONS In this study, a discrete-event port simulation model was developed to assess the current operations of the port

understudy as well as evaluate the impact of several security incident scenarios on terminal throughput, delays, queues, and/or resource utilization. The current port capacity was sufficient to ensure the proper running of operations at the current level of trucks and ships movements. On the other hand, we showed that if certain security incidents are to happen, then the delays and queues will not only impact the port, but also the surrounding transportation nodes. Such incidents could be alleviated by proper management decisions such as opening extra emergency truck gates, installing additional STS cranes, and adding Mobile cranes. ACKNOWLEDGMENTS This work was funded in part by a grant from the Lebanese National Council for Scientific Research (NCSR Grant #480). The authors would like to thank Mr. Serge Jabbour, terminal manager, for his help and guidance in understanding the port operations. References Bruzzone, A.G., Mosca, R., Revetria, R., and Rapallo, S. Risk analysis in harbor environments using simulation, Safety Science, 35, p. 75-86 (2000). Devor, Jay L., Probability and Statistics for Engineering and the Sciences, Duxbury Press (2007). Kelton, W. David, Sadowski, and Sturrock, David T., Simulation with Arena, McGraw-Hill (2004). Koh, P-H., Goh, J., Ng, H-S., and Ng, H-C. Using Simulation to Preview Plans of Container Port Operations”, Proceedings of the 1994 Winter Simulation Conference ed. J. D. Tew, S. Manivannan, D. A. Sadowski, and A. F. Seila (1994). Leathrum J.F., Mielke, R.R., Mazumdar, S., Mathew, R., Manepalli, Y., Pillai, Y., Malladi, R.N. and Joines, J. A simulation architecture to support intratheater sealift operations, Mathematical and Computer Modelling, 39 (6–8), pp. 817–838 (2004). Legato, P., and Mazza, R.M. Berth planning and resources optimisation at a container terminal via discrete event simulation, European Journal of Operational Research, 133 (3), P. 537-547 (2001). Parola, F. and Sciomachen, A. Intermodal container flows in a port system network: Analysis of possible growths via simulation models, International Journal of Production Economics, 97, pp. 75–88 (2005). Rabadi, G., Pinto, C.A, Talley, W., and Arnaout, J-P. Port Recovery from Security Incidents: A Simulation Approach, Chapter 5 (p.83-94) in Bichou, Bell & Evans: Risk Management in Port Operations, Logistics and Supply Chain Security, INFORMA/Lloyd’s List Publishing, London, UK (2007).

Shabayek, A.A. and Yeung W.W. A simulation model for the Kwai Chung container terminals in Hong Kong’, European Journal of Operational Research, 140 (1), P. 1–11 (2002). Law, Averill M., and Kelton, W. David, Simulation Modeling and Analysis, McGraw-Hill (2000).

Biography Jean-Paul M. Arnaout is an Associate Professor at the Industrial and Mechanical Engineering Department at the Lebanese American University. He received his PhD and M.S. from the Department of Engineering Management and Systems engineering at Old Dominion University, Norfolk, Virginia in 2006 and 2003 respectively. He received his bachelor’s degree in Mechanical Engineering from the University of Balamand, Lebanon. Dr. Arnaout developed several simulation and optimization models including port operation simulations. His Research interests include Optimization Techniques, Modeling and Simulation, and Scheduling and Rescheduling. He can be reached at [email protected]. Samar Hallab is an undergraduate student at the Industrial and Mechanical Engineering Department at the Lebanese American University. Her research interests include Optimization Techniques and Simulation and Modeling. She can be reached at [email protected]. Caline El Khoury is an undergraduate student at the Industrial and Mechanical Engineering Department at the Lebanese American University. Her research interests include Optimization Techniques and Simulation and Modeling. She can be reached at [email protected].