The Rapid Growth In International Trade Commerce Essay

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The volume in container terminals around the world has increased substantially each year for the past decade due to the rapid growth in international trade. As a result of this yearly increase in container volume, terminals stack containers to save space. These containers transported by container vessels or ships are stored in terminal grounds for a short duration before being dispatched to other ships, trains or over-the-road trucks. The stacking of containers requires special equipment to locate and lift these containers in the container transfer process. Some containers requested by trucks may be located far from the equipment position or underneath several other containers, thereby requiring a longer equipment travel time and additional lifts to deliver the container. Focusing on the process of transferring containers from the import yard to over-the-road trucks, this thesis examines the effect of using different schemes to schedule deliveries. The purpose of this thesis is to develop a simulation system framework, with which a number of container delivery scheduling schemes can be implemented and their performance statistics can be collected and compared. The performance analysis can help identify a scheduling scheme that will cut down equipment travel time and container lift time, thereby reducing the average truck wait time as well as queue size. This will result in better service, reduced truck turnaround time, and less port traffic congestion and air pollution around container terminals.

History of Container Development

During World War II, the United States Army needed a faster and better way of transporting goods because the time required to load and unload ships was long and cargo was delayed in ports. The Army was also losing money because of pilferage and in-transit damage. Toward the end of World War II, the United States Army experimented with using specialized containers to ship food and military supplies to the troops in the field. These containers were called "transporters." The main purpose of using these specialized containers was to speed up the loading and unloading of transport ships. During the Korean War, theft and damage to wooden crates at the Port of Pusan proved that containers needed to be made of steel. The effectiveness of steel containers marked a turning point that paved the way for the popularity of containers. Their use allowed shipment time to be cut in half. The United States Department of Defense standardized an 8 feet by 8 feet cross section in multiples of 10 feet lengths for military use. The British quickly adapted this standard, leading to the use of containers for general shipping purposes. The introduction of containers vastly improved port handling efficiency. This lowered the costs and reduced freight rates, which in turn boosted international trade [1].

Containers provide security. Each container has only one door and can be bolted and padlocked. They also provide the protection of goods from weather. With proper blocking and bracing, the steel walls of each container can prevent damage to its cargo. To further increase port handling efficiency, a form of standardization is needed for these containers. Containerization was then developed. "Containerization is a system of intermodal freight and cargo transport using standard ISO containers" [1]. With containerization comes intermodalism. Intermodalism is the use of several modes of transportation to accomplish a single movement of cargo. Cargo being moved from the source to the destination can be transported by truck, ship, train or plane. With different modes of transportation being used to transport cargo, it is naturally convenient if containers were to have standard sizes and characteristics to facilitate security and handling. Containerization is one of the most important innovations of the 20th century.

The most widely used containers are the 20-foot and 40-foot containers. Figure 1 shows what containers looks like. These containers are made of steel and have a single door in front that is 8 feet wide. Containerization has revolutionized cargo shipping and handling. Today, almost all consumer goods have spent some time in a container. It has facilitated the evolution of cargo movement by land, air and sea.

Motivation

The introduction of containers and containerization led to the boosting of trade flows. This in turn led to the building of ships that specialize in container transport.

FIGURE 1. Diagram of containers.

Container ports around the world have also been modernized to allow the smooth flow of goods from one country to another. Each year, the total number of containers that are transported in and out of container ports increases in number. This annual increase in volume has a number of implications. Table 1 derived from Container Management [2] shows the world's top 20 container ports in 2007 and its throughput from 2003 to 2007.

TABLE 1. 2007 World Top 20 Container Ports Throughput from 2003 to 2007

2007 Rank

Port Name

Country

2007

TEU*

2006

TEU

2005

TEU

2004

TEU

2003

TEU

1

Singapore

Singapore

27,935,500

24,792,400

23,199,252

21,329,000

18,410,500

2

Shanghai

China

26,150,000

21,710,000

18,080,000

14,557,200

11,280,000

3

Hong Kong

China

23,990,000

23,539,000

22,480,000

21,984,000

20,499,000

4

Shenzhen

China

21,099,000

18,468,890

16,197,000

13,615,200

10,614,900

5

Busan

South Korea

13,270,000

12,030,000

11,840,000

11,430,000

10,407,709

6

Rotterdam

Netherlands

10,800,000

9,603,000

9,286,757

8,280,786

7,106,778

7

Dubai

UAE

10,700,000

8,923,465

7,619,000

6,428,883

5,151,958

8

Kaohsiung

Taiwan

10,256,829

9,774,670

9,470,000

9,714,115

8,843,365

9

Hamburg

Germany

9,890,000

8,861,545

8,087,545

7,003,479

6,138,000

10

Qingdao

China

9,462,000

7,702,000

6,307,000

5,139,700

4,239,000

11

Ningbo-Zhousan

China

9,360,000

7,068,000

5,208,000

4,005,500

2,772,000

12

Guangzhou

China

9,259,000

6,651,580

4,684,000

3,308,200

2,761,700

13

Los Angeles

USA

8,355,039

8,469,853

7,484,624

7,321,440

7,178,940

14

Antwerp

Belgium

8,176,164

7,018,799

6,488,029

6,063,746

5,445,436

15

Long Beach

USA

7,312,465

7,290,365

6,709,818

5,779,852

4,658,124

16

Port Klang

Malaysia

7,120,000

6,300,000

5,540,000

5,243,593

4,840,000

17

Tianjin

China

7,103,000

5,950,000

4,801,000

3,814,000

3,015,000

18

Tanjung Pelepas

Malaysia

5,500,000

4,770,000

4,177,000

4,020,421

3,487,320

19

New York

USA

5,299,105

5,092,806

4,785,318

4,478,480

4,067,812

20

Bremerhaven

Germany

4,900,000

4,400,000

3,735,574

3,469,253

3,189,853

*Twenty feet equivalent units

Table 2 from Dr. Rodrigue's compilation of Container Traffic of the Top 100 Ports [3] shows the world's top 20 container ports in 2007 and its throughput from 1998 to 2002.

TABLE 2. 2007 World Top 20 Container Ports Throughput from 1998 to 2002

2007 Rank

Port Name

Country

2002

TEU

2001

TEU

2000

TEU

1999

TEU

1998

TEU

1

Singapore

Singapore

16,940,900

15,520,000

17,040,000

15,944,793

15,135,557

2

Shanghai

China

8,620,000

6,340,000

5,613,000

4,216,000

3,068,421

3

Hong Kong

China

19,144,000

17,900,000

18,100,000

16,210,792

14,582,000

4

Shenzhen

China

7,613,754

5,080,000

3,993,714

2,986,014

2,100,000

5

Busan

South Korea

9,436,307

8,070,000

7,540,387

6,439,589

5,945,614

6

Rotterdam

Netherlands

6,515,449

6,100,000

6,275,000

6,343,242

6,010,502

7

Dubai

UAE

4,194,264

3,500,000

3,058,886

2,844,634

2,804,104

8

Kaohsiung

Taiwan

8,493,000

7,540,000

7,425,832

6,985,361

6,271,053

9

Hamburg

Germany

5,373,999

4,690,000

4,248,247

3,738,307

3,546,940

10

Qingdao

China

3,410,000

2,640,000

2,120,000

1,540,000

1,214,000

11

Ningbo-Zhousan

China

1,860,000

1,213,000

902,000

600,000

310,000

12

Guangzhou

China

2,180,000

1,800,000

1,429,900

1,180,000

847,000

13

Los Angeles

USA

6,105,863

5,183,520

4,879,429

3,828,851

3,378,219

14

Antwerp

Belgium

4,777,387

4,220,000

4,082,334

3,614,246

3,265,750

15

Long Beach

USA

4,526,365

4,462,967

4,600,787

4,408,480

4,097,689

16

Port Klang

Malaysia

4,533,212

3,760,000

3,206,753

2,550,419

1,820,018

17

Tianjin

China

2,410,000

2,100,000

1,708,423

1,301,905

1,018,000

18

Tanjung Pelepas

Malaysia

2,660,000

2,050,000

418,218

20,696

0

19

New York

USA

3,749,000

3,320,000

3,006,493

2,863,342

2,465,993

20

Bremerhaven

Germany

3,031,587

2,900,000

2,712,420

2,180,955

1,812,441

As the volume of containers increases, terminals need to stack containers to save space. If the scheduling of deliveries from the import yard to over-the-road trucks is inefficient, truck turnaround times are higher, thus resulting in port traffic congestion and increased air pollution around the container terminal. The stacking of containers means that there is a need to provide better service to reduce truck turnaround times and increase port handling efficiency, thereby ensuring the smooth delivery of containers to its destination. Table 3 shows the increase in container volume of the world's top 20 containers for a period of 10 years from 1998 to 2007. Six out of the 20 top container ports show an increase in container volume of more than 500 percent. The cumulative increase in the 10-year period for all 20 ports is 196 percent. This clearly shows that

TABLE 3. Increase in Throughput of World's Top 20 Container Ports from 1998 to 2007

2007 Rank

Port Name

Country

2007

TEU

1998

TEU

Change

Percentage of Change

1

Singapore

Singapore

27,935,500

15,135,557

12,799,943

85%

2

Shanghai

China

26,150,000

3,068,421

23,081,579

752%

3

Hong Kong

China

23,990,000

14,582,000

9,408,000

65%

4

Shenzhen

China

21,099,000

2,100,000

21,099,000

905%

5

Busan

South Korea

13,270,000

5,945,614

7,324,386

123%

6

Rotterdam

Netherlands

10,800,000

6,010,502

4,789,498

80%

7

Dubai

UAE

10,700,000

2,804,104

7,895,896

282%

8

Kaohsiung

Taiwan

10,256,829

6,271,053

3,985,776

64%

9

Hamburg

Germany

9,890,000

3,546,940

6,343,060

179%

10

Qingdao

China

9,462,000

1,214,000

8,248,000

679%

11

Ningbo-Zhousan

China

9,360,000

310,000

9,360,000

2919%

12

Guangzhou

China

9,259,000

847,000

8,412,000

993%

13

Los Angeles

USA

8,355,039

3,378,219

4,976,820

147%

14

Antwerp

Belgium

8,176,164

3,265,750

4,910,414

150%

15

Long Beach

USA

7,312,465

4,097,689

3,214,776

78%

16

Port Klang

Malaysia

7,120,000

1,820,018

5,299,982

291%

17

Tianjin

China

7,103,000

1,018,000

6,085,000

598%

18

Tanjung Pelepas

Malaysia

5,500,000

20,696

5,479,304

26475%

19

New York

USA

5,299,105

2,465,993

2,833,112

115%

20

Bremerhaven

Germany

4,900,000

1,812,441

3,087,559

170%

Total

235,938,102

79,713,997

156,224,105

196 %

there is a need for container ports around the world to accommodate these increases and expect that it will continue to increase in the years to come.

This thesis attempts to address the problem of present and future congestion in container ports by finding a priority scheduling scheme that will provide better service and allow the efficient transfer of containers from the import yard to over-the-road trucks. The transfer of containers from import yards to over-the-road trucks is a very important stage in the process of delivering import containers to its destination. This stage in the delivery process is a very crucial one since the efficiency of this operation will affect the swiftness of the delivery of goods to the consumers. It will also affect the amount of free space that will be available for the storage of other containers that have arrived or will arrive at a future date. The ultimate goal of every cargo shipment is to have the cargo delivered to its destination in the fastest and most efficient manner possible. Therefore, the efficiency in transferring the containers from import yards to over-the-road trucks could help to achieve this end.

Most container terminals have already devised methodologies and strategies to help make the container delivery and other operations more efficient. Methods using technologies like Differential Global Positioning System (dGPS) and Radio Frequency Identification (RFID) to provide information on container locations and truck arrivals as well as appointment systems have been employed to help organize and facilitate the delivery process. This thesis, however, attempts to go beyond these methods by looking into priority scheduling schemes that will affect the order in which trucks' requests for container pickup are processed and serviced. These scheduling schemes would reorder container deliveries to over-the-road trucks based on priority. If proved to be effective, these alternatives may warrant implementation that could result in improved service, shorter truck queues, reduced road congestion and less air pollution around the container port areas.

Container Terminal Operations

The operations in a container terminal involve a wide variety of tasks. Among these tasks is the loading and unloading of containers from ships, the transport of containers to and from yard stacks and storage and retrieval of containers. Managing terminal operations is not a simple task due to the large number of operations involved. Decisions made in one stage of operations could influence the operational efficiency of other stages. While managers in a container terminal make certain operational policies, some of the decision-making tasks could be left with equipment operators and would therefore be based on experience and intuition. Such a scenario could affect the overall efficiency of container terminal operations.

A typical container terminal layout can be divided into two areas, the quayside and the landside or the hinterland. The quayside refers to the ship operation area or the berth area where the ships or container vessels are moored and where the loading and unloading of containers take place. On the other hand, the landside or the hinterland refers to the landside operation area where trucks and trains transfer containers in and out of the container terminal. In between these two areas is a large area where both empty and non-empty containers are stored. This area houses the import and export yard, empty yard and the sheds where import, export, empty and special containers are stored. Figure 2 shows a typical layout of a container terminal.

Typical container terminal operations are performed in three directions. A container can be an import, export, or transshipment container. Considering only import and export containers, it can be said that two directions involved in container terminal operations are either an import direction or an export direction. In a simplistic way of looking at these operations, the import direction basically involves the unloading of containers from the container vessels, the transferring of these containers to the import yard and the transferring of the containers from the import yard to trains or over-the-road trucks. The export direction would then involve the unloading of containers from trains

FIGURE 2. Typical layout of a container terminal.

or trucks, the transferring of these containers to the export yard and the loading of the containers onto the ships or container vessels. This simplistic viewpoint illustrates the need for different types of container terminal equipment for the transfer of containers from the quayside to the landside and vice versa.

Container Terminal Equipment

Container terminal equipment can basically be classified into two types, the cranes and the horizontal transport vehicles. The cranes can further be classified into several types. The quay crane is used for the loading and unloading of containers from a container vessel or ship. This type of crane is the equipment used in the quayside or ship operation area. Figure 3 shows a diagram of a quay crane. For the import yards, three or

FIGURE 3. A quay crane.

more types of cranes can be used to transfer containers, the rail-mounted gantry crane (RMG), the rubber-tired gantry crane (RTG) and the overhead bridge cranes (OBC) [4]. Rubber-tired gantry cranes are more flexible in operation while rail-mounted gantry cranes are more stable and overhead bridge cranes are mounted on concrete or steel frames. These are the equipment being used to transfer containers in and out of import and export yards. Figure 4 shows a diagram of a rubber tired gantry crane.

Different types of vehicles are used for horizontal transport. A horizontal transport vehicle can either have the capability to lift containers or not. Trucks with

FIGURE 4. Rubber tired gantry crane.

trailers, multi-trailers and automatic guided vehicles (AGV) are examples of horizontal transport vehicles not capable of lifting containers. The containers have to be lifted by some other equipment and placed onto these vehicles. An automatic guided vehicle is a robotic vehicle that is able to drive on a road network with electric wires or transponders in the ground to control its position and direction. Only a few container terminals like the ones at Rotterdam, Netherlands and Hamburg, Germany employ the use of AGVs. The main objective of using AGVs is to cut labor costs involved in operations. Figure 5 shows a diagram of an automatic guided vehicle.

Another class of horizontal transport vehicles has the capability of lifting containers. These are the straddle carriers, the forklifts and the reach stackers. Straddle carriers are very widely used and can sometimes be used in place of cranes. They are

FIGURE 5. Automatic Guided Vehicle (AGV).

very flexible and can move around the container yards in relatively fast speeds. That is why straddle carriers are also considered cranes that are not locally bound since they can access containers anywhere inside the container terminal regardless of where the straddle carriers are located [4]. Figure 6 shows a diagram of a straddle carrier while figures 7 and 8 show diagrams of a forklift and reach stacker respectively.

FIGURE 6. A straddle carrier.

FIGURE 7. A forklift.

FIGURE 8. A reach stacker.

Typical Container Terminal Process Flow

Figure 9 shows the process flow in a typical container terminal. The stages and order of operations involved in the import or export of a container can be thought of as reciprocal processes depicted in figure 9 as two arrows coming in and out of each box.

FIGURE 9. Process flow in a typical container terminal.

Import and export containers are brought into and out of port by ships or container vessels. Before a ship arrives, a berth, which is a place at a dock, is allotted to the ship by a process called berth allocation [4]. A lot of studies have been made on berth allocation using queuing theory since berth allocation is very similar to client-server interaction. It can be done with the objective of minimizing ship turnaround time and maximizing berth utilization. Berth allocation is determined way before the arrival of a container vessel. When a ship arrives and the process of berthing is done, the containers in the ship are discharged consecutively from the container vessel by quay cranes based on an unload plan, which is also determined in advance. The unload plan provides data on which containers are to be unloaded and where they are located. The type of quay crane used and the number of quay cranes assigned to a ship is important in determining how long a ship stays at a container terminal. These containers are then loaded onto transport vehicles like AGVs or transferred to the yard stacks using transport vehicles like straddle carriers, forklifts or reach stackers. Trucks can also be used for this purpose. If trucks are used to transfer containers from the quayside to the yard stacks, a yard crane is needed to perform an additional transfer from the truck to the yard stack. In this stage of operations, planning which vehicles to use, the number of vehicles and the travel route are important factors in determining the speed of the transport of containers to the yard stacks. Containers that reach yard stacks are either stacked on top of one another or placed on wheels and stored. In this stage, the stack height and the yard layout can be important factors to consider at it affects the lift time needed to transfer containers to and from trains and trucks. The containers are then ready to be transferred to another departing ship, train or over-the-road trucks. The speed of transfer can be determined by the number of yard equipment used and the scheduling or the order by which the containers are to be transferred. If a container is placed on wheels, the truck driver picking up a container just needs to attach the chassis to the truck. Placing containers on wheels takes up a lot of space since containers cannot be stacked on top of one another.

The stages of operations involved in the export of a container are just the reverse process of the import operations. A truck or train delivers a container to the container terminal. The container is transferred to the export yard by a yard crane and stored. After a period of time, the containers in the export yard are then transferred to the quayside by transport vehicles. The quay crane then loads the containers onto a container vessel or ship that is ready for departure based on a stowage plan. The stowage plan contains data that specifies which containers are to be loaded and the position of all the containers on the container vessel.

Current State of Container Delivery to Over-the-Road Trucks

This thesis uses two of the world's top 20 container ports as models for the development of the simulation system. These are the Port of Los Angeles (POLA) and the Port of Long Beach (POLB). Table 4 derived from POLA TEU Statistics [5] shows

TABLE 4. Port of Los Angeles Container Trade from 1998 to 2007

Year

Loaded Inbound

Loaded Outbound

Total Loaded

Empties

Total Containers

2007

4,410,170

1,607,643

6,017,812

2,337,226

8,355,039

2006

4,408,185

1,423,620

5,831,805

2,638,048

8,469,853

2005

3,881,326

1,171,231

5,052,557

2,432,068

7,484,624

2004

3,940,420

1,129,880

5,070,300

2,251,140

7,321,440

2003

3,814,473

1,163,345

4,977,818

2,201,122

7,178,940

2002

3,232,411

1,093,807

4,326,218

1,779,645

6,105,863

2001

2,683,657

1,037,795

3,721,452

1,462,068

5,183,520

2000

2,492,546

984,651

3,477,197

1,402,231

4,879,429

1999

1,965,853

817,581

2,783,433

1,045,417

3,828,851

1998

1,715,414

794,668

2,510,082

868,136

3,378,219

TABLE 5. Port of Long Beach Container Trade from 1998 to 2007

Year

Loaded Inbound

Loaded Outbound

Total Loaded

Empties

Total Containers

2007

3,704,593

1,574,241

5,278,834

2,033,631

7,312,465

2006

3,719,680

1,290,843

5,010,523

2,279,842

7,290,365

2005

3,346,054

1,221,419

4,567,473

2,142,345

6,709,818

2004

2,987,980

1,007,913

3,995,893

1,783,959

5,779,852

2003

2,409,577

904,539

3,314,116

1,344,008

4,658,124

2002

2,452,490

855,202

3,307,692

1,218,673

4,526,365

2001

2,420,687

952,845

3,373,532

1,089,435

4,462,967

2000

2,456,188

1,044,353

3,500,541

1,100,246

4,600,787

1999

2,317,050

989,221

3,306,271

1,102,209

4,408,480

1998

2,096,901

973,647

3,070,548

1,027,141

4,097,689

the Port of Los Angeles container trade figures for a 10-year period from 1998 to 2007 while table 5 derived from POLB Yearly TEUs [6] shows the Port of Long Beach container trade figures in the same period. The figures show that there is a phenomenal increase in the number of loaded import containers for the past 10 years.

When a ship or container vessel arrives, the containers are unloaded based on an unload plan with special priority containers indicated in the plan unloaded first. The containers are unloaded by a quay crane and are placed onto chassis. The destination of each container is determined and those containers that are bound for distant locations (e.g., other states) are transferred and loaded to trains using on-dock or near-dock rail facilities [7]. Those containers that are bound for nearby destinations will be transferred to a specific area inside the import yard by a terminal truck. Upon reaching the import yard, a container is either kept on wheels or placed in a stack and its location is recorded in a terminal computer. The containers that are to be stacked are handled by a yard crane or other equipment like straddle carriers or toploaders. This process is repeated until all the containers have been unloaded from the container vessel and transferred to its proper location in the storage yard. The customers are then notified of the container's arrival with the expectation that the containers will be picked up in a few days time. A storage fee will be charged to customers for containers not picked up within a specified period of time, which is typically four to seven days [7].

The proper companies are notified when containers arrive and are ready for pickup. Some terminals implement an hourly appointment system for container pickups/drop offs while others do not. In the hourly appointment system, companies that send their trucks to pick up a container has to set an appointment first and specify the hour of the day when the container will be picked up (e.g., between 1:00 pm to 2:00 pm). The Evergreen Terminal, West Basin Container Terminal and the TransPacific Terminal are examples of terminals that implement this appointment system [7]. The hourly appointment system can either be mandatory or optional. In the mandatory appointment system like the one implemented at West Basin Container Terminal, making an appointment is a must before any container can be picked up. The optional appointment system, on the other hand, allows walk-ins to pick up containers with priority given to those with appointments. Some terminals have no appointment system at all so all container pickups are considered walk-ins. For walk-in trucks, a ticket that contains the location of the container is given to the truck driver and the truck driver proceeds to the specified location to pick up the container. The container can either be on-wheels or stacked. If the container is on-wheels, no equipment is necessary to deliver the container. All the truck driver has to do is hook up his truck to the chassis and he is on his way. If the container is stacked, a work order is issued to an equipment operator to proceed to the same location and transfer the container to the truck. Yard cranes are used for this purpose. In general, trucks are served on a first-come first-served basis in most container terminals. In the appointment system, when more than one truck arrives in the same hour, the truck that arrives first will be serviced first.

The appointment system may be a practical and logical way of making the container delivery more efficient. It is meant to diffuse truck arrivals and avoid congestion by preventing a scenario where a lot of trucks arrive at the same time. The disadvantage of this implementation is that some container terminals make appointments optional and even if it were mandatory, missed appointments, traffic situations and late arrivals cannot be prevented. There is also the possibility of delays within the container terminal itself that keeps the equipment busy causing unnecessary delays in container delivery. For an appointment system to be successful, both sides need to strictly comply with the schedule and this is not always easy to accomplish. Another problem with the current situation is the determination of which containers are to be put on wheels and which are to be stacked. The choice is often random [7]. Therefore, there is a possibility that the containers put on wheels may not be picked up within the free storage window period. It is also possible that sometime in the future, containers may no longer be put on wheels to free up space within a terminal. With the scarcity of land and the increase in container volume each year, all containers might need to be stacked to save space. A more efficient way of delivering containers may be needed to prevent congestion from happening inside container terminals. The order by which containers are delivered relative to truck arrivals and container location might need to be revised to provide a more efficient delivery system. The first-come first-served system may be the most common implementation but the scenario in container terminals and future projections require a better method of scheduling deliveries. Since transactions in a container terminal especially in container deliveries can be determined in advance, proper scheduling of deliveries can easily be implemented. This thesis will look into scheduling scheme alternatives for implementing an efficient system of container delivery from import yards to over-the-road trucks.

Scope of Thesis

The objective of this thesis is to develop a simulation system that models the delivery of containers from the import yard to over-the-road trucks using eight different priority scheduling schemes. The simulation will be run for a user-specified duration and selected performance measures will be generated for comparison. The simulation system provides a graphic user interface to allow users to enter certain input parameters. The simulation system is modeled after an import yard configuration where different areas are provided for import container storage. These areas are subdivided into blocks and each block is further subdivided into bays (rows) and stacks (columns) with each stack having a certain height. The only equipment provided for by the simulation system is the rubber tired gantry (RTG). While the use of the straddle carrier or other more flexible equipment is imbedded into the simulation system, this feature is labeled as others in the equipment group of the graphic user interface since this type of equipment is not commonly used in the delivery of containers from import yards to over-the-road trucks.

This thesis consists of eight chapters. Chapter 2 discusses related literature involving studies that have been made on the different stages of container terminal operations. These studies include papers, thesis and dissertation on topics ranging from berth allocation to the delivery of containers. Chapter 3 discusses the eight different priority scheduling schemes and the relevance of its application in this thesis. These priority scheduling schemes are based on simple heuristics that have been applied in CPU and disk scheduling. Chapter 4 provides a general literature on the concepts of simulation and modeling. It defines simulation and discusses the reasons why computer simulation is a good tool in studying the effects of certain implementations on the operations of real world processes. Chapter 5 shows the different simulation parameters used in the experiments to generate the statistical data outputs used in the analysis. It also discusses the assumptions made in the experiments and the design considerations involved in the development and implementation of the simulation system. Chapter 6 details the implementation of the simulation with the development of an object-based discrete-event simulation system using the C# language. It also shows the different system classes and events as well as the program flow. The different input parameters in the graphic user interface is defined to give the user the knowledge on how the default input parameters can be changed to suit certain simulation requirements. This chapter also discusses the different input probability distributions used in the simulation system and the different types of statistical outputs and their definitions. Chapter 7 shows the actual statistical data outputs generated based on the experimental design. A discussion on the performance and starvation analysis of the priority scheduling schemes is provided and a recommendation is made on which particular priority scheduling scheme can be feasibly implemented for import yard operations. The conclusion of this thesis as well as discussions on future research can be found in Chapter 8.

Since the simulation experiment generated many statistical data outputs, not all these data were used in the discussion and analysis. Appendices A and B contain additional statistical data output tables generated by the simulation system that were not discussed or analyzed. However, the concepts provided for in the analysis of the statistical data outputs can also be applied to these data.

CHAPTER 2

RELATED LITERATURE

Many studies have been conducted on improving the efficiency of container terminal operations. Most of these studies focus on a particular stage of container terminal operations while some would deal with the overall efficiency of container terminal operations.

Imai, Nagaiwa and Chan [8] present a solution for minimizing ship turnaround time in ports, which is a berth allocation problem in a lot of container terminals. It also presents a solution to minimize dissatisfaction of ships in terms of berthing order. They came up with an algorithm to identify the best solutions to the berth allocation problem. The effectiveness of the algorithm was tested with sample problems and results that indicate the importance of the berth allocation problem in proper and efficient terminal utilization. In Imai, Nishimura and Papadimitriou [9], a study was made to use service priorities in finding a solution to the berth allocation problem. The paper extended the existing dynamic berth allocation formulation to include service priority but the problem became non-linear and difficult to solve. They then tried to reduce the problem to a Lagrangian relaxation problem to find a subgradient optimization but the resulting subgradient method was NP-hard since the relaxed problem was a quadratic assignment problem. The final solution was a genetic algorithm-based heuristic algorithm which takes into account the container volume of ships and its weight as basis for prioritizing the berth allocation of ships. Goodchild and Daganzo [10] studied the problem of minimizing ship turnaround time by proposing the implementation of double cycling. This refers to the simultaneous loading and unloading of ships. They used a greedy strategy to analyze the advantages of double cycling.

When a ship or container vessel arrives, scheduling quay cranes to load or unload the containers from a ship is a critical part in determining the ship turnaround time. Zhu and Lim [11] examine the important crane-scheduling problem of container terminals with the objective of minimizing the completion time for all crane jobs. They were able to prove that the problem is NP-complete and the resulting algorithm or solution was designed using the branch and bound technique. Daganzo [12] came up with a proposal to use a linear integer programming formulation for loading a few ships and a principle-based heuristic approach if the number of ships is large. The assumption is that only one quay crane is used at the ship operations area. Peterkofsky and Daganzo [13] developed a branch and bound algorithm with the same assumptions as [12].

Containers loaded and unloaded to and from a ship have to be transferred between the quayside and the container storage area. Soriguera, Robuste, Juanola and Lopez-Pita [14] analyzed the internal transport system of a container terminal and the effect the type of handling equipment has on the amount of time it takes to transfer containers between the quayside and the container storage area. They also investigated the optimum number of internal transport units needed for maximum efficiency. They used simulation and applied queuing theory with the assumption that the stochastic processes involved in this operation such as the arrival of internal transport units and the quay crane's service process in the loading and unloading of ships follow the Poisson distribution of arrival and service times. They were able to come up with a comprehensive cost analysis and the savings that would be realized if a certain set of criteria is followed. The results concluded that internal transport units should not be assigned to individual quay cranes but should instead be assigned to the berth as a whole.

After containers are transferred from the quayside to container storage areas, the strategy of how containers are stacked and organized is also important in determining efficiency in container terminal operations. Le-Griffin and Murphy [15] studied the productivity of the Ports of Los Angeles and Long Beach and gave suggestions on how to increase productivity in these two ports. Special notice was given to the fact that these two ports rely more on wheeled operations than on dense stacking resulting in land underutilization and a recommendation of shifting to high-stacking operations was made.

Containers located in an import yard stack of a container terminal that are ready for pickup are transferred to over-the-road trucks by yard cranes, a subject for this thesis. Kim, Lee and Hwang [16] studied the sequencing problem of delivering and receiving containers in container terminals. They identified a static sequencing problem and a dynamic sequencing problem. In the static sequencing problem, all truck arrivals are known in advance while trucks arrive continuously in the dynamic scenario. A dynamic programming model was suggested for the static case while a learning-based method for deriving decision rules was suggested for the dynamic case. It is important to note that the study made by Kim, Lee and Hwang [16] made certain assumptions. It assumes that only a single yard crane is used, the transfer time of a container from the yard to the truck is fixed regardless of where the container is located and that each truck has its own due time for service to be provided by the yard crane. An assumption was also made that the truck interarrival time is exponentially distributed and that the yard block configuration is fixed. The number of trucks allowed was limited to only seven and the overflowed trucks were discarded in the simulation due to the large computation time needed in the dynamic programming model. Kim, Lee and Hwang [16] evaluated different sequencing rules: modified dynamic programming, first-come-first-served (FCFS), uni-directional travel (UT), nearest-truck-first-served (NT), shortest processing time rule (SPT) and the reinforcement learning rule (RL). Their first simulation experiment included all six sequencing rules and the truck interarrival time was assumed to be 6 minutes. In the second simulation experiment, the truck interarrival time was assumed to be 12 minutes and dynamic programming was excluded due to the fact that it did not prove to be optimal in the first simulation and excessive computational time was necessary to generate the results. The results of the second simulation experiment showed SPT to be the best sequencing rule while FCFS was the worst. RL only performed better than all other sequencing rules when the target container locations deviated from a uniform distribution.

Sgouridis and Angelides [17] provided a simulation-based analysis of handling import containers in a container terminal. The paper focuses on import yard configuration with yard configuration, equipment speed and truck interarrival rate as inputs to the simulation system. They assumed that truck interarrival times follow an Erlang distribution and that import yards only use straddle carriers to transfer import containers to trucks. The paper takes into consideration the distances between each truck pad (i.e., waiting area for a particular truck) and all stacking yard rows. The main objective of the Sgouridis and Angelides [17] study is to provide a simulation tool for container terminal management to evaluate the effects of import yard layout modifications, truck pad placements, equipment upgrades and working shift policies on the efficiency of import container handling in a container terminal.

Huynh's dissertation [18] investigates two measures that will aid container terminal management in making decisions that will affect truck turnaround time in import yard operations. The first measure is the number of yard cranes that will facilitate the transfer of containers to trucks and the second measure is the limiting of truck arrivals to container terminals. The second measure can be achieved with the implementation of an appointment system that will prevent trucks from congesting the container terminal. Huynh [18] developed statistical models (e.g., regression models) and a simulation model that will analyze the effect of increasing the number of yard cranes on truck turnaround time. The simulation model incorporates the precise movements of trucks and yard cranes considering distance and speed. The results show that increasing the number of yard cranes will reduce truck turnaround time. A methodology of implementing an appointment system was also developed in the dissertation. A mathematical model that will provide optimal appointment scheduling shows an improvement in truck turnaround time with the appointment system implemented.

Khoshnevis and Asef-Vaziri [19] proposes the automation of computer terminals by providing a 3-D virtual and physical simulation of an automated container terminal with the main objective of analyzing the effects of automation on throughput, space utilization and horizontal material-handling equipment utilization. A very notable feature of this paper is the suggestion of using AGVs to provide transport services in a container terminal. The paper concluded that automation is feasible and a very good alternative that will reduce labor costs and significantly improve the three key performance measures.

Valkengoed [20] proposes the use of two yards cranes per block of containers. These two yard cranes must not be mounted on the same rail and one will be bigger than the other to allow the small yard crane to pass under the bigger yard crane. This type of crane configuration will allow both sides of the block to be served. Valkengoed [20] refers to this configuration as the passing crane configuration. He used dynamic simulation to obtain results that shows the difference between using a single yard crane as against the implementation of the passing crane configuration. The results of the simulation show that the average maximum delay is higher with the single yard crane implementation. He also applied two heuristic sequencing rules in his study: Nearest due time (ND) (i.e., similar to first-come first-served) and the nearest neighbor (NN) (i.e., the truck requesting a container closest to the current yard crane position is serviced first). The results of Valkengoed's paper show minimal differences between the two heuristic sequencing rules with NN edging out ND by a small margin.

Simulation is a common methodology used in many studies on container terminal operations. The study done by Kim et al. [16] assumes that the truck interarrival time distribution is exponential. Their study tests only two truck interarrival rates and also assumes that the container transfer time, yard block configuration, and speed of yard cranes are fixed. This thesis, however, uses a simulation that considers equipment speed in distance calculations and equipment travel time. We also considered the exact number of lifts needed to transfer a container in calculating lift times. While it is common to assume that truck interarrival time distribution is exponential, there is evidence that shows it can be lognormal or other distributions [7]. Yard block configuration can also affect the performance of scheduling schemes in comparison to first-come first-served. This thesis generates statistical data outputs using both exponential and lognormal distributions on several yard block configurations. It also takes into account many details that will increase the accuracy of the statistical data outputs generated by the simulation system.

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