Modeling and Design of Container Terminal Operations

Design of container terminal operations is complex because multiple factors affect the operational perfor- mance. These factors include: topological constraints, a large number of design parameters and settings, and stochastic interactions that interplay among the quayside, vehicle transport, and stackside processes. In this research, we propose new integrated queuing network models for rapid design evaluation of container terminals with Automated Lift Vehicles (ALVs) and Automated Guided Vehicles (AGVs). These models offer the flexibility to analyze alternate design variations and develop insights. For instance, the effect of alternate vehicle dwell point policy is analyzed using state-dependent queues, whereas the efficient terminal layout is determined using variation in the service time expressions at the stations. Further, using embedded Markov chain analysis, we develop an approximate procedure for analyzing bulk container arrivals. These models form the building block for design and analysis of large-scale terminal operations. We test the model efficacy using detailed in-house simulation experiments and real-terminal validation by partnering with an external party

Development of a multimodal port freight transportation model for estimating container throughput

Computer based simulation models have often been used to study the multimodal freight transportation system. But these studies have not been able to dynamically couple the various modes into one model; therefore, they are limited in their ability to inform on dynamic system level interactions. This research thesis is motivated by the need to dynamically couple the multimodal freight transportation system to operate at multiple spatial and temporal scales. It is part of a larger research program to develop a systems modeling framework applicable to freight transportation. This larger research program attempts to dynamically couple railroad, seaport, and highway freight transportation models. The focus of this thesis is the development of the coupled railroad and seaport models. A separate volume (Wall 2010) on the development of the highway model has been completed. The model railroad and seaport was developed using Arena® simulation software and it comprises of the Ports of Savannah, GA, Charleston, NC, Jacksonville, FL, their adjacent CSX rail terminal, and connecting CSX railroads in the southeastern U.S. However, only the simulation outputs for the Port of Savannah are discussed in this paper. It should be mentioned that the modeled port layout is only conceptual; therefore, any inferences drawn from the model's outputs do not represent actual port performance. The model was run for 26 continuous simulation days, generating 141 containership calls, 147 highway truck deliveries of containers, 900 trains, and a throughput of 28,738 containers at the Port of Savannah, GA. An analysis of each train's trajectory from origin to destination shows that trains spend between 24 - 67 percent of their travel time idle on the tracks waiting for permission to move. Train parking demand analysis on the adjacent shunting area at the multimodal terminal seems to indicate that there aren't enough containers coming from the port because the demand is due to only trains waiting to load. The simulation also shows that on average it takes containerships calling at the Port of Savannah about 3.2 days to find an available dock to berth and unload containers. The observed mean turnaround time for containerships was 4.5 days. This experiment also shows that container residence time within the port and adjacent multimodal rail terminal varies widely. Residence times within the port range from about 0.2 hours to 9 hours with a mean of 1 hour. The average residence time inside the rail terminal is about 20 minutes but observations varied from as little as 2 minutes to a high of 2.5 hours. In addition, about 85 percent of container residence time in the port is spent idle. This research thesis demonstrates that it is possible to dynamically couple the different sub-models of the multimodal freight transportation system. However, there are challenges that need to be addressed by future research. The principal challenge is the development of a more efficient train movement algorithm that can incorporate the actual Direct Traffic Control (DTC) and / or Automatic Block Signal (ABS) track segmentation. Such an algorithm would likely improve the capacity estimates of the railroad network. In addition, future research should seek to reduce the high computational cost imposed by a discrete process modeling methodology and the adoption of single container resolution level for terminal operations. A methodology combining both discrete and continuous process modeling as proposed in this study could lessen computational costs and lower computer system requirements at a cost of some of the feedback capabilities of the model This tradeoff must be carefully examined.M.S.Committee Chair: Rodgers, Michael; Committee Member: Guensler, Randall; Committee Member: Hunter, Michae

Stochastic Modeling of Unloading and Loading Operations at a Container Terminal using Automated Lifting Vehicles

With growing worldwide trade, container terminals have grown in number and size. Many new terminals are now automated to increase operational efficiency. The key focus is on improving seaside processes, where a distinction can be made between single quay crane operations (all quay cranes are either loading or unloading containers) and overlapping quay crane operations (some quay cranes are loading while others are unloading containers). From existing studies, it is not clear if the design insights obtained from analyzing single operations, such as optimal stack layout, are consistent with the insights obtained from analyzing overlapping operations. In this paper, we develop new integrated stochastic models for analyzing the performance of overlapping loading and unloading operations that capture the complex stochastic interactions among quayside, vehicle, and stackside processes. Using these integrated models, we are able to show that that there are stack layout configurations that are robust for both single (either loading or unloading) and for overlapping (both loading and unloading) operations

Heuristic container placement algorithms

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2003Includes bibliographical references (leaves: 56-58)Text in English; Abstract: Turkish and Englishviii, 72 leavesWith the growth of transportation over sea; defining transportation processes in a better way and finding ways to make transportation processes more effective have become one of the most important research areas of today. Especially in the last quartet of the previous decade, the computers had become much powerful tools with their impressive amount of data processing cababilites. It was imminent that computers had begun taking serious roles in the system development studies. As a result; constructing models for the processes in container terminals and processing the data with the computers create opportunities for the automation of various processes in container terminals. The final step of these studies is the full automation of terminal activities with software packages that combine various functions focused on various processes in a single system.This study is about a project that had been made for a container terminal owned by a special company. During this study; there had been discussions with experts about the subject, and container handling processes in the terminal had been analyzed in order to define the main structure of the yard management software to be created.This study focuses on the container handling activities over the yard space so as to create a basis for a computer system that will take part in the decisions during the container operations. Object oriented analysis and design methods are used for the definition of the system that will help the decisions in the yard operations. The optimization methodology that will be the core of the container placement decisions is based on using different placement patterns and placement algorithms for different conditions. These placement patterns and algorithms are constructed due to the container handling machinery that was being used in the terminal that this study has been made for