2 nd International Conference on Urban Disaster Reduction November 27~29, 2007 Scenario-based simulation of revenue loss at seismically damaged seaports U. J. Na *, S. R. Chaudhuri, M. Shinozuka 1 Department of civil and environmental engineering, University of California, Irvine, CA, USA Abstract These days more than 90% of international cargo moves through seaports. So seaports play a crucial role in economic activity of the nation. Because of this, damage to seaports by natural disasters such as an earthquake results in severe economic loss in terms of repair cost and revenue loss, which was resulted from the reduced throughput associated with downtime. For estimation of revenue loss, several complex logistic algorithms such as ship arrival, crane allocation, and movement of the container boxes must be considered. The purpose of this study is to estimate revenue loss due to downtime of a container port terminal. This study comprises of actual port operation data and port operation simulation model, which was developed using a professional simulation program, Arena. The simulation model can be used to decide on retrofit strategies for existing ports and design new ports by means of benefit/cost analysis. Keyword: seaport, earthquake, revenue loss, simulation, port operation, HAZUS * Corresponding author address: U. J. Na, Department of civil and environmental engineering, University of California, Irvine, CA, USA, una@uci.edu
1. Introduction Sea ports are very important nodes of national and international transportation networks and play a crucial role in economic activity of the nation. They provide shipping, distribution, and other functions for the transport of cargoes via water. In many countries, trade through ports is most dominant mode compared to other modes such as land and air. Ports are often regional economic centers and important components of regional and local transportation lifeline systems. In the past, damage to ports due to natural disasters such as an earthquake has proven to cause severe economic loss. The loss arises from repair cost and associated downtime which results in reduced throughput. The loss of revenue due to reduced throughput may sometimes exceed the repair cost. For example, the Kobe earthquake in 1995 resulted in a direct loss $ 5.5 billion to the Kobe port while the indirect loss was $ 6 billion within the first 9 months. HAZUS program by Federal Emergency Management Association (FEMA) includes a loss estimation methodology for port transportation system [1]. This methodology focuses on the direct repair cost due to different hazards. However, this conventional approach of loss estimation may not be appropriate for estimating the economic loss of a port where the revenue loss associated with the downtime of a damaged terminal is significant. For a more advanced approach of loss estimation, which can be applied for ports, following two aspects needed to be considered: (a) a refined inventory of the port transportation system, such as waterfront structures (i.e., wharves, piers, and seawalls), cranes, and warehouse; (b) loss estimation methodology based on system risk and functionality. For the estimation of revenue loss based on functionality of damaged system, several logistic algorithms such as ship arrival, crane allocation, and movement of the container boxes need to be considered. This study focuses only on the estimation of revenue loss due to downtime of a port terminal as result of seismic damage induced to port structures. For this purpose, fragility curves relating the damage states of port structures with ground motion intensity are used to incorporate uncertainties associated with a scenario earthquake. These fragility curves are obtained from a preliminary study conducted by the authors. To evaluate the revenue loss due to reduced throughput, scenario-based port simulation model is developed. 2 Framework of the proposed methodology
In the terminal system of ports, especially in container terminals, there are many individual components which play an important role in terminal operation. For example, there are structural quay wall, container gantry cranes, container yard cranes, warehouses, lifelines etc. Among them, structural quay wall and container gantry cranes are most important for terminal operation and most expensive components. In addition, these components are very critical from the point of earthquake recovery schedule of a port. Therefore, in the proposed methodology, these components are considered as the main components to represent the terminal operation system after an earthquake. Reduced TEU Throughput Revenues After EQ Quay wall damage states Scenario Earthquakes (PGA) Crane damage states 1. Scenario EQ 2. Port data Ship Traffic Algorithm Port Operation Simulation Model Revenue Loss Simulation of components damage and functionality (berths and cranes) Vulnerability Model (Fragility Analysis) Damage State (Monte Carlo) Revenues without-event Replacement Cost Total Loss Simulation of terminal operation System Risk (e.g. Container throughput, unit : TEU) (a) Overview of loss estimation (b) System risk evaluation Fig. 1. Los estimation methodology using fragility curves and port simulation model Fig. 1(a) shows the overview of loss estimation involving fragility analysis for quay walls and container cranes and simulation model for the terminal operation. In this procedure, container throughput expressed in terms of TEU (Twenty-foot Equivalent Unit) corresponding to damage states can be obtained using simulation. After then, the difference in revenue between with-event and without-event cases can be used to calculate the revenue loss due to earthquakes. Fig. 1(b) demonstrates system risk evaluation methodology. For a scenario earthquake, based on various damage states quay wall and cranes, fragility curves are developed. Note that because of the wide ranges of uncertainty involved in this problem (e.g., in estimation of seismic hazard, structural characteristics, and site conditions), seismic vulnerability of component structures can be associated with various damage states [2]. Once the damage states of all involved components are simulated using Monte Carlo simulation, port operation can be simulated using terminal operation simulation model under different damage states of port. In this research, port operation simulation model considers damage states of berths. 3 Simulation model for port operation 3.1 Overview
Simulation model is widely used for analysis of port and terminal planning process and container handling system. It can be used as a decision making tool to optimize terminal operation. Keeping a balance between the shipping companies who demand a speedy service for their ships and the operation companies who pursue economic use of resources is a very critical task in the terminal operation. Note that both waiting time of ships and use of container terminal facilities are very costly and thus it is desirable to maximize the degree of utilization in a short period. Even though several simulation models have been developed [3,4], those have a tendency to use much simplified model to simulate actual ports. In this study, the authors developed a simulation model which makes it possible to perform diverse scenario analyses by computerizing several features such as ship s arrival distribution. To build a comprehensive model incorporating all the necessary elements of the port terminal operation, Arena simulation program was used which has been proven to be capable of simulating and investigating complex queuing model [5]. In addition, for data processing and user interface, SPSS and MS Visual Basic computer program were used, respectively. 3.2 Simulation model for the estimation of container throughput Ship arrival and quay performance models were developed based on the actual observed data collected from container terminals and thus making it possible to investigate the validity of this model. For this purpose, observed data from six container terminals were collected from port management information system (Port-MIS) of South Korea. These data set included ship s arrival time distribution, lift per call (LPC) distribution of ships, the number of assigned container cranes and its productivity. The simulation model of this study was based on the ongoing occurrence of events instead of using the resource allocation, i.e., it was assumed that after the loading/unloading activities for anchored ship are over, the other ship waiting to be served will be allocated to the available berth on an ongoing basis. Fig. 2 shows the flow algorithms to simulate the port operation and allocation of the berth. 3.3 Model verification Using the real operation data from six container terminals located at Busan, probability distributions of ship arrival time interval, LPC per ship, the number of assigned cranes, and handling time per container box were determined. It was found that exponential distribution for ship s arrival interval, weibull distribution for the LPC and lognormal distribution for
crane handling time were found to be appropriate. Note that LPC including the unloading and loading of both 40 ft and 20 ft container boxes has been divided into four sections: 1) < 500, 2) 500 to <1000, 3) 1,000 to < 1500, and 4) 1,500 and then the distribution of each section of LPC and assigned number of cranes were estimated separately. Generate Ship traffic Ship arrival Allocate LPC for each ship Is there available berth? No Waiting Yes Allocate ship to berth Calculate the number of container crane Allocate container crane to each berth Is there available container crane? Container handling Ship s departure Allocate container crane to each berth (a) Operation model (b) Ship and crane allocation Fig. 2. Flowchart of simulation model Based on the assumption that the workdays and work hours of all the container terminals are 365 days and 24 hours, respectively, and the preparation time for ship berthing and unberthing remains unchanged, simulations were carried out and results along with observed data are shown in table 1 for two different terminals. One can say from Table 1 that the developed simulation model has a good capability to reflect the real operation status of port terminals. Table 1. Simulation results Cases The number of ship arrival Berth occupancy rate (%) Real data Simulation results Real data Simulation results Terminal 1 1532 1441 51.5 50 Terminal 2 1477 1388 63.4 61 4 Economic loss estimation due to earthquake For demonstration of simulation model, it is assumed that the design earthquake has a peak ground acceleration (PGA) of 0.4 g. Then using the fragility curves and probabilistic restoration functions [6], the reduced container throughput for container terminal with 4 berths was calculated using Monte Carlo simulation. A total of 100 cases for damage states
and corresponding repair period were generated. The economic loss was presented in terms of the reduced container throughput and increased ship waiting time as the number of container throughput is directly associated with the profit of the port authority and port operation company and ship waiting time is generally considered as a very important indicator terminal service evaluation. Fig. 3 shows their distributions. Note that the mean and standard deviation for reduced container throughput are 611,784 and 201,742 TEU and for increased waiting time are 468 and 344 hours, respectively. x 10-6 x 10-3 3 1.8 1.6 2.5 1.4 2 1.2 y it D ens 1.5 y it D ens 1 0.8 1 0.6 0.4 0.5 0.2 0 2 3 4 5 6 7 8 9 10 11 0 0 200 400 600 800 1000 1200 1400 1600 1800 Reduced container throughput (TEU) x 10 5 Increased waiting time (hours) Fig. 3. Results of economic loss evaluation 5 Conclusions In addition to repair cost, revenue loss due to downtime of the port terminal is found to be very significant in case of port damage. In this study, through a port terminal operation simulation model, reduced container throughput and increased ship waiting time have been estimated which are directly associated with the revenue loss due to hazards. For this purpose, probabilistic framework combined with fragility curves and restoration models has been used. The simulation model can be used to decide on retrofit strategies for existing ports and design new ports by means of benefit/cost analysis. References [1] FEMA, Hazus 99-technical manual, (1999). [2] Na U.J., Ray Chaudhuri S. and Shinozuka M., Probabilistic assessment for seismic performance of port structures, Soil dynamics and earthquake engineering, doi:10.1016/j.soildyn.2007.05.003. [3] Kia M., Shayan E. and Ghotb F., Investigation of port capacity under a new approach by computer simulation, Computers & industrial engineering, Vol. 42, (2002), pp 533-40. [4] Pachakis D. and Kiremidjian A.S., Ship traffic modeling methodology for ports, Journal of waterway, port, coastal and ocean engineering, ASCE, Vol. 129, No. 5, (2003), pp 193-202. [5] Rockwell Automation, Arena User s Guide, Rockwell software, (2006). [6] Zhou Y., Probabilistic seismic risk assessment of highway transportation network, Ph.D Dissertation, University of California Irvine, (2006).