Information Integration for Container Terminal Management and Operations-Informatie Integratie voor Container Terminal Management en Operaties

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Delft University of Technology

FACULTY MECHANICAL, MARITIME AND MATERIALS ENGINEERING

Department Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl

This report consists of 32 pages and no appendices. It may only be reproduced literally and as a whole. For commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into consideration under the condition that the applicant denies all legal rights on liabilities concerning the contents of the advice.

Specialization: Transport Engineering and Logistics Report number: 2014.TEL.7874

Title: Information Integration for

Container Terminal Management and Operations

Author: I.C. Gelens

Title (in Dutch) Informatie Integratie voor Container Terminal Management en Operaties

Assignment: literature

Confidential: no

Supervisor: prof.dr.ir. Y. Pang

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Summary

With more and more freight being moved by container and the size of container ships increasing the pressure is on container terminals to increase their efficiency. A lot of research has been done on optimizing the different parts of a container terminal, but integration of these optimizations could yield considerable benefits. To identify the current state of these integration efforts, a literature study is conducted.

Container Handling Process and Operations

Containers are relatively uniform boxes whose contents do not have to be unpacked at each point of transfer and are measured in Twenty-foot Equivalent Units. On a terminal the containers are (un)loaded from or to trains, trucks and ships. Equipment used on terminals are Quay Cranes, Gantry Cranes, Overhead Bridge Cranes, Automated Guided Vehicles, Reach Stackers, Straddle Carriers and Multi Trailers.

Terminal Management

Terminal management consists of the decisions that have to be made in order to realise smooth operation. When leaving maintenance out of scope, three objectives are identified for terminal management, namely securing a high throughput, offering a reliable service and cost effectiveness. On the marine side interface three types of problems exist namely the Berth Allocation Problem, the Quay Crane Assignment Problem and the Quay Crane scheduling problem. The Berth Allocation Problem focusses on assigning a berth to a container vessel while minimizing the ship handling time. The Quay Crane Assignment Problem covers how many cranes need to be assigned to a ship. The Quay Crane Scheduling Problem looks at what part of a ship each crane needs to handle.

Concerning horizontal transport, the focus of this review lies on the Automated Guided Vehicle system. Most research focusses on optimizing guide paths, but research on free ranging approaches exists as well and it shows more promising results. Other optimization problems are scheduling, positioning, battery management and deadlock resolution.

Optimization problems at the container storage system consist of optimizing the stacking policy. Research in the delivery and receipt system at hinterland is very limited.

Information Flow

All these optimization problems have good individual solutions, but friction between the subsystems implies a sub-optimal solution for the whole terminal. To go from several separate optimization problems to the integrated problem the information flow needs to be studied. Two types of

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information flow can be identified. Between different stakeholders information exchange exists via phone, email and the Electronic Data Interchange. On the terminal itself information is collected into the Terminal Operation System by scanning containers on different points of the terminal. This is done by barcode scanners, microwave technology, radio frequency identification or voice recognition. All this information collected in a single system clears the path for integrative optimization.

Information Integration

Within the research done on integrated optimization of container terminals two approaches can be identified. The centralized and decentralized approach. The centralized approach uses all the data collected on the terminal to solve the full system. Approaches found in literature consist of Genetic Algorithms or Model Predictive Control. The problem with these methods is that they are computationally very heavy.

The decentralized approach is based on optimization of the different subsystems while exchanging data with each other in a multi-agent system. Because the computational load is distributed, this shows more promising results towards optimization. These multi-agent systems can also be implemented one level higher to produce an information exchange system between all terminals and stakeholders. These systems are known as Port Community Systems and are already commercially available. The next step is to not only exchange information but also provide active management of capacity by an unbiased agent. This is currently piloted in the Port of Rotterdam by the name Nextlogic.

Conclusion

In literature a clear trend of information integration can be found and the decentralized approach is the most promising at the moment. One level higher are the information sharing platforms. This technology is widely available and already works in theory. What lags behind is legislation and willingness to exchange information with competitors. However, future developments will likely consist of further integration between the different actors because the possible benefits for all parties involved are substantial.

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List of Abbreviations

AGV Automated Guided Vehicle

ALV Automated Lifting Vehicle

BAP Berth Assignment Problem

DEFT Dynamic Evasive Free-ranging Trajectories

EDI Electronic Data Interchange

ETA Estimated Time of Arrival

ETD Estimated Time of Departure

GA Genetic Algorithms

GAPM Genetic Algorithm Plus Maximum Matching

HHLA Hamburger Hafen Logistik AG

IPCSA International Port Community System Association

MAS Multi-Agent System

MLGA Multi-Layer Genetic Algorithm

MPC Model Predictive Control

NN Neural Network

NNAMPC Neural Network Algorithm Model Predictive Controller

OBC Overhead Bridge Crane

PCS Port Community System

QCAP Quay Crane Assignment Problem

QCSP Quay Crane Scheduling Problem

RFID Radio Frequency Identification

RMG Rail Mounted Gantry Crane

RTG Rubber Tired Gantry Crane

SC Straddle Carrier

TEU Twenty-foot Equivalent Unit

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1 Introduction

Since de development of the container in the fifties it has rapidly changed the way freight moved through the world. The first regular sea container service began about 1961 with an international container service between the US East Coast and points in the Caribbean, Central and South America (Steenken, Voß, & Stahlbock, 2004). The breakthrough after a slow start was achieved with large investments in specially designed ships, adapted seaport terminals with suitable equipment, and availability (purchase or leasing) of containers. A large number of container transhipments then led to economic efficiency and a rapidly growing market share (Steenken, Voß, & Stahlbock, 2004).

With more and more freight being moved by container and the size of container ships increasing the pressure is on container terminals to increase their efficiency. Higher demands in efficiency require a higher optimization of the container terminal. Traditionally container terminals are optimized by optimizing different parts, like stacks and cranes. Within the Information Technology age, more and more data comes available. This literature review will focus on how data integration on container terminals is currently used to further optimize the container terminal.

Over the years a lot of research has been done on optimizing the different parts of a container terminal. These parts have been formulated as either analytical or simulation problems and a lot of progress has been made on increasing the efficiency of a container terminal as it is known today. However, when the different operations are viewed as different sub-problems, the different optimizations will not be aligned with each other, resulting in a sub-optimal solution for the terminal as a whole. In order to tackle this problem, the operations need to be solved as a combined problem. The objective of this review is to identify the current state of these integration efforts and to provide an outlook on future developments in this field.

For this review, the type of container terminal that will be used as a reference is the deep-sea terminal. This is a terminal connecting the deep-sea with either rail, road or inland barge transport. However, it will be possible to apply the findings of this survey to virtually every container terminal, since the abstract basis of data integration will be roughly the same. For horizontal transport inside the terminal only Automated Guided Vehicles will be covered. Because man driven vehicles have less possibilities for optimization, integration has the biggest advantages for automated vehicles. For cranes this is not an issue since cranes have less degrees of freedom than a horizontal transport vehicle. Therefore, for cranes the general operation shall be covered.

In the next section, the process and operations on a container terminal will be outlined. An overview will be made of common container handling equipment and how this equipment interacts with each

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other. Roughly two classes of equipment will be covered, namely vertical transport equipment like cranes and horizontal equipment like Automated Guided Vehicles.

In Section 3, terminal management will be covered. Every part of the container terminal like the stack or the key cranes come with a variety of decision problems, for example how to allocate ships to berths, how many cranes to deploy per ship and how to stack containers while minimizing the amount of reshuffling moves.

Section 4 will cover the information flow of a terminal. Starting at the history of transmitting the bay plan via the Electronic Data Interchange (EDI) system the development of scanners and RFID systems will be outlined. Nowadays this information is accumulated in the Terminal Operation System (TOS). This development will be covered.

Section 5 will contain the different steps that have been made in the field of data integration. Different approaches of data integration have been researched of which the Multi-Agent System (MAS) approach is the most popular one. With computing power still increasing, more elaborate optimizations of terminal operations become available. This chapter will focus on outlining these optimizations. Also, an outlook on the future and some areas of possible future research will be presented. This will mainly consist of the inter-terminal relations that are currently under investigation. Research focusses on either exchanging information between the different actors or the optimization of multi-terminal systems.

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2 Container Handling Process and Operations

In order to write an article on container terminals it is vital to first provide a definition of the shipping container. Containers are relatively uniform boxes whose contents do not have to be unpacked at each point of transfer (Steenken, Voß, & Stahlbock, 2004) and are measured in Twenty-foot Equivalent Units (TEU). A standardized 1 TEU shipping container has a width and height of 8 ft. or 2.44 m and a length of 20 ft. or 6.10 m. Even though this is the standardized measure, 40-45 ft. containers (2 TEU) are also widely used. Specialized containers exist to transport special goods like chemicals or material that needs to be refrigerated, but they all fit in the standardized dimensions allowing for handling at all container terminals.

The main purpose of a container terminal is connecting deep-sea transportation with inland transportation. To serve this purpose containers are moved from deep-sea ships to trains, trucks or inland barges and vice-versa. Therefore, a container terminal can be viewed as an open system with two in/outflows of containers with a stacking operation in between. The first in/outflow is the quayside operation. Here deep-sea vessels arrive to load and unload containers. The second in/outflow is the hinterland operation. Here containers are loaded and unloaded onto trains, trucks or barges, connecting the terminal to the inland. The stacking operation in between consists of three types of areas to temporarily stock container. The first area is known as the stockyard or stack. Here containers are temporarily stored before they are transported further. Containers that are directly moved from the quay to the hinterland or vice versa are an exception. Most containers will be stacked before they are shipped further. The second area is the empty stock, which is a stockyard designated for empty containers. Some terminals have a third area filled with sheds. Here containers can be unpacked to transport their content onwards in smaller segments. The whole operation has been depicted in Figure 1. In the following sections the different parts of the container terminal will be discussed briefly.

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Figure 1: A schematic overview of Terminal Operations (Steenken, Voß, & Stahlbock, 2004)

2.1 Quayside Operation

The main purpose of the quayside operation is to (un)load deep-sea vessels. The unloading of a container-vessel is depicted in Figure 2. Depending on terminal lay-out inland barges can either be loaded at the quayside or have their own designated area. The (un)loading of ships is done with quay cranes. These cranes consist of a steel frame in which a trolley drives back and forth from ship to shore. Attached to this trolley is a standardized device called a spreader, which is able to pick up containers. The purpose of these cranes is to unload a ship as fast as possible. Quay space is usually the bottleneck in a container terminal and therefore a decrease in handling time means an increase in terminal efficiency. Therefore, a special effort is made to increase the efficiency of quay cranes. A modern day spreader is able to pick up to 4 TEU at one go. Efficiency is also increased by two-trolley spreaders and fully automated quay cranes. These developments however are brand new and therefore only seen on the newest container terminals (Chao & Lin, 2011).

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Figure 2: A ship being unloaded at quayside (Mercator Media, 2008)

Figure 3: A typical stockyard (Mercator Media, 2008)

2.2 Stockyard

After the containers are unloaded they usually are placed into the stockyard. A typical stockyard is depicted in Figure 3. Here the containers wait until they are picked up by trucks or trains. Stockyards are typically served by Gantry Cranes. When mounted on a rail this type of crane is known as a Rail Mounted Gantry Crane (RMG), but Rubber Tired Gantry Cranes (RTG) are also very common. Sometimes an Overhead Bridge Crane (OBC) is also used. A single stockyard usually is 8-12 containers in width and will typically be served by two Gantry Cranes. In old set-ups these two gantry cranes are not able to pass each other, but a set-up where a smaller gantry crane moves beneath a larger gantry crane thus allowing for both cranes to serve the entire stack is also available. Apart from these cranes a lot of container movements are also done by reach stackers or other equipment to move a single container. This equipment classified as horizontal transport equipment and will be covered in Section 2.4.

2.3 Hinterland Operation

At the land-side of the terminal, containers are loaded onto trains and trucks to be transported to the customer. The truck-loading operation is depicted in Figure 4. This can either be done by dedicated gantry cranes or by any combination of the self-lifting transport vehicles mentioned in the next section.

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Figure 4: Trucks being loaded at landside (Associated Press, 2014)

2.4 Horizontal Transportation

To move containers on a terminal a wide range of transport vehicles are available. Within this study, a distinction is made between the transport vehicles that are able to lift containers themselves and the transport vehicles that need to be loaded. The transport machines that need to be loaded are:

 Automated Guided Vehicles or AGVs. AGVs can be deployed on modern terminals. As the name suggests they are computer operated vehicles that can handle a single container.

 Multi-trailer system. A multi trailer is man driven and can handle multiple containers. Self-lifting vehicles are:

 Reach stackers. Reach stackers are man-operated and can be used to transport a single container and stack them usually three or four stories high. Usually, they are deployed to move around empty containers.

 Straddle Carriers can typically lift containers up to 3 or 4 stories high. Typically, they are man-operated, but automatic straddle carriers, sometimes known as Automated Lifting Vehicles (ALV), are also coming available.

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Figure 5: A schematic overview of terminal operations (Steenken, Voß, & Stahlbock, 2004)

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3 Terminal Management

How to properly deploy the equipment described in Section 2 can be referred to as terminal management. As can be expected, the way this equipment is deployed has a huge impact on terminal performance. The objectives of terminal management are:

 Securing a high throughput

 Offering a reliable service

 Cost effectiveness

These objectives are all very closely related. In a perfect world where all machines never break down, the most cost effective terminal is the one where all equipment is utilized 100% of the time. This will maximize throughput and therefore minimize the equipment cost per container handled. However, machines do break down from time to time. In order to keep delivering service when a machine breaks down, excess capacity is needed. This will increase the reliability of the system. The objective is to find an optimum between maximum throughput, maximum reliability and minimal cost.

An aspect that influences the optimum is the employed maintenance routine. The maintenance routine influences all three objectives above. Because this is a problem in almost every industry, a lot of methods to perform maintenance while keeping equipment operational as much as possible at minimal cost exist. Examples of these systems include Reliability Centred Maintenance (RCM) and Total Productive Maintenance (TPM). However, these systems are very elaborate and are applicable to all industries. Therefore they will be considered out of scope in this paper.

This chapter will focus on the different parts of the terminal and how they can be optimized in sense of cost, throughput and reliability. Yan, Liu, & Xi (2008) define four main parts of the terminal, namely the marine side interface, the (horizontal) transfer system, the container storage system and the delivery and receipt system. Each part of the terminal comes with its own decision problems and optimization methods and will be covered below. The chapter will be concluded with some thoughts on terminal optimization.

3.1 Marine Side Interface

According to Bierwirth & Meisel (2010), roughly three classical decision problems can be identified at the marine side interface. The first problem is the assignment of quay space and service time to the different vessels. This problem is known as the Berth Allocation Problem (BAC). The second problem is the assignment of quay cranes to the different vessels. This problem is commonly referred to as the Quay Crane Assignment Problem (QCAP). The last problem is making a work plan for the cranes and

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this is known as the Quay Crane Scheduling Problem (QCSP). All three shall be covered separately below.

Berth Allocation Problem

In order to allocate berth space to the different vessels, some information is required in advance. This is a combination of dimensions like draft, clearance and length as well as projected handing and arrival time. The objective is to allocate berth in a way that an objective function is optimized. This usually means that the handling time must be minimized.

When assigning quay space to vessels the layout of the berth must be determined. This is firstly a combination of the physical constraints of the port and secondly a partitioning of the quay into berths. Three partitioning schemes can be identified (Imai, Sun, Nishimura, & Papadimitriou, 2005):

 Discrete layout. The quay is divided into a number of sections. One vessel per time per berth.

 Continuous layout. No berths exist. The advantage is that quay utilization can increase, but planning will be more difficult in this set-up.

 Hybrid layout. The quay is divided into berths, but larger vessels can take up more than one berth.

As for arrival times of the vessels the following cases can be identified (Imai, Nishimura, & Papadimitriou, 2001) (Stahlbock & Voß, 2008):

 Static Arrival. Vessels can berth immediately when assigned. Either there are no arrival times or they impose a soft constraint enabling the vessel to berth faster at a certain cost.

 Dynamic Arrival. Vessels have fixed arrival times.

Of course vessel handling times need to be projected and the better they are estimated, the better a berthing schedule will be. Vessel handling times are assumed deterministic in most models, but different dependencies exist in literature (Günther & Kim, 2006) (Bierwirth & Meisel, 2010), namely:

 Handling times are fixed

 Handling times depend on positions

 Handling times depend on the number of quay cranes assigned

 Handling times depend on the crane schedule

 Handling times depend on a combination of position, number of quay cranes and crane schedule

As mentioned before, usually the objective of berth assignment is to keep the handling times as low as possible. Further objectives can consist of the minimization of terminal resources and minimal

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vessel rejection rate. These objectives can also be defined in cost in order to add a combination of all factors to the objective function (Bierwirth & Meisel, 2010).

Quay Crane Assignment Problem

After a feasible berth plan has been established, quay cranes have to be assigned to the ship. This can come along with specific requirements (Vis & de Koster, 2003) (Bierwirth & Meisel, 2010):

 Sometimes there is a number of cranes assigned to a ship, sometimes this needs to be a specific set of cranes

 The number of cranes is either time-invariant e.g. will not change throughout the unloading action or will change in some approaches

 A minimum number of cranes needs to be assigned based on contractual agreements

The QCAP is closely related to the BAP because the handling time is hugely dependant on both. Since crane assignment usually is easy to accomplish by rule of thumb, not a lot on this can be found in literature. The QCAP is usually treated as a part of the BAP (Bierwirth & Meisel, 2010).

Quay Crane Scheduling Problem

Whenever a vessel arrives at a container terminal it is loaded according to the stowage or bay plan. Based on this plan a set of cranes has been assigned, which need to unload the vessel in a specific order. What crane needs to unload what part of the vessel is known as the QCSP. Different tasks can be identified for the QCSP based on how the ship is divided into parts (Vis & de Koster, 2003) (Bierwirth & Meisel, 2010):

 Bay areas. A crane needs to (un)load a certain area of the bay

 Bays. A crane needs to (un)load a bay

 Stacks. A crane needs to (un)load a certain set of stacks from the vessel

 Groups. A crane needs to (un)load a group of containers for example having the same destination

 Containers. A crane has a specific set of containers to (un)load

The cranes that have been assigned to the vessel can move freely, but they cannot pass each other. Therefore this constraint needs to be kept into account. By assigning cranes to a certain set of non-overlapping bay areas this difficulty is avoided. However, this comes at the expense of the efficiency of the unloading plan. Therefore, if possible the areas should be established more efficiently. This increases the complexity of the problem.

Apart from the space requirements there are also time requirements. Possible approaches include (Murty, Liu, Wan, & Linn, 2005) (Bierwirth & Meisel, 2010):

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 Ready times. A ready time per crane is used to assign the first move of a crane

 Time windows. A crane is assigned for a certain time-window

 Position. Initial and final positions are prescribed

 Travel times. The speed of crane movement is dependent on the time required to travel between bays

For all approaches, simplifying the problem reduces the optimality of the solution but reduces the complexity of the solution.

3.2 Horizontal Transfer System

After the ship has been unloaded, containers need to be moved through the terminal with AGVs, Straddle Carriers, Multi-trailers or a combination. This is known as horizontal transfer opposed to vertical transfer which is performed by the cranes. How to deploy horizontal transfer equipment is another optimization problem. A difference could be made between man driven and automated vehicles. However, automated vehicles face the same problems as man driven systems complemented by a set of extra problems introduced by the automated system. Therefore, the research on AGVs will be the main topic of this section.

According to literature, some of the objectives of an Automated Guided Vehicle system are (Vis I. , 2006):

 Maximise throughput of the system

 Minimize the time required to complete all jobs

 Minimize vehicle travel times

 Evenly distribute workload over AGVs

 Minimize total cost of movement

 Minimize time job is handled over due time

 Minimize maximum or average throughput times of AGVs to travel to the destination of new jobs

 Minimize expected waiting times of loads

Le-Anh & de Koster (2006) define several decision problems that can be put into three categories, namely number of vehicle estimation, guide path problems and vehicle management. The first category can be seen as the original horizontal transport problem the other two are becoming more and more important with the increase in deployment of AGVs. Le-Anh & de Koster consider the guide path problem as a decision at strategical level because they mainly look at fixed paths, but with the introduction of AGV control algorithms that are more adaptible to their surroundings, this problem

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can also be considered at tactical and operational level. Vis I. (2006) also considers guide paths to be a problem at tactical and operational level, but this is more focussed on collision and deadlock prevention. A topic that will be discussed in the category vehicle management in this paper.

Guide Path Problems

The traditional way of managing AGVs is by setting up a guide path. This means that all AGVs follow a fixed route visiting P/D stations along the way. How to set up these guide paths properly is a classic AGV problem. In literature there exist several approaches to this problem (Le-Anh & de Koster, 2006) (Vis I. , 2006):

 Unidirectional systems. The AGVs move over a fixed path and are able to choose multiple routes. This is the first system. Advantages of this system include flexibility in routing, efficiency and tolerance to system failures, but the control is rather complicated, it is difficult to expand and congestions are likely to occur.

 Bidirectional systems. The same system as a unidirectional system, but with two lanes allowing for multiple directions. This will reduce the congestion problems of the

unidirectional system, but because bidirectional systems require a significant amount of space this option is not heavily researched.

 Single-loop systems. The single-loop system does not allow multiple routes. Therefore, it is much easier to control at the expense of capacity and throughput times.

 Tandem systems. A group of single loops connected by transfer stations. This is easy to control and expand and eliminates the congestion problem. However, transfer buffers are required and when one loop fails, the system fails.

These systems are widely researched and many approaches and solutions exist in literature (Le-Anh & de Koster, 2006)

More modern research focusses on dynamic vehicle routing. For example the Dynamic Evasive Free-Ranging Trajectories (DEFT) model uses the increased computing power and accuracy of AGV sensors to develop free-range routing policies for AGVs. By simulation it can be shown that this approach outperforms the fixed approaches and results in a higher throughput of the AGV system (Duinkerken, Ottjes, & Lodewijks, 2006).

Vehicle Management

Several decision problems related to Vehicle Management exist in literature (Le-Anh & de Koster, 2006).

 Vehicle Scheduling. This can be defined as the decision making process on operational level. In other words, which vehicle does what job.

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 Vehicle Positioning. Sometimes vehicles are jobless. It does not make sense to let each vehicle return to a designated station, so where should they stop? This is another decision problem.

 Battery Management. With battery power AGVs the question when they should be recharged enters the equation.

 Deadlock Resolution. Equipping an AGV with a policy on what to do when it encounters other AGVs along the way.

These problems are all decision problems that are related to the use of AGVs (Le-Anh & de Koster, 2006). Some of them can be applied to human driven vehicles as well.

3.3 Container Storage System

Within the stockyard containers are stacked in rows of several layers high. How many rows and layers depends on multiple factors like the size of the terminal, the layout of the terminal and the stacking policy. The containers are stacked by gantry cranes. When a container has to be removed from the stack and it is not on top the cranes have to perform reshuffling moves (Murty, Liu, Wan, & Linn, 2005). This means removing the top container(s) of a stack and stacking them somewhere else. It needs no explanation that this type of moves needs to be avoided at all times. Therefore stacking policy optimization in container terminals is important.

Park, Choe, Young, & Kwang (2011) identify two types of research that has been done on stacking policies, namely allocation of storage and determination of stacking position. The first type focusses on how to arrange the container stack as a whole. This involves optimizing the factors stack height, space utilization and the number of rehandlings for export containers. This is a multi-objective optimization because in theory using a single layer system would eliminate reshuffling moves but will cost maximum space. Different techniques have been used to approach this problem. The second type focusses on determining a stacking position for a single container in such a way that the amount of reshuffling moves are minimized. Numerous approaches to tackle this problem have been used as well including Genetic Algorithm based methods and dynamic programming models.

The constraints on the stacking policy problem are:

 The degrees of freedom of the gantry cranes. This includes whether the gantry cranes are able to pass each other or if they have to perform handshake moves. Also, the stacks of containers have a maximum height based on the height of the gantry cranes.

 The amount of space allocated to the container stockyard. This is a designated amount of rows and columns and is a decision at strategic level. However, to optimize this must be taken into account.

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3.4 Delivery and Receipt System

On the hinterland side, containers are loaded on and unloaded from trucks and trains. Optimizing the delivery and receipt system mainly means reducing waiting times for truck drivers and train operators. For example, Ng & Mak (2004) propose an algorithm to sequence trucks in a certain way. The aim is to reduce congestion by minimize total truck handling time. A couple of other papers exist, but the research on this part is very limited (Stahlbock & Voß, 2008).

3.5 Summary

Summarizing, the decisions that are made daily at a container terminal consist of four parts. The first part is the quay operation and consists of (un-)loading a vessel as fast as possible by assigning an optimal birth and an optimal amount of quay cranes. The second part is the horizontal transfer system. The throughput and costs of this system can be optimized by optimizing the routes. Reliability can be achieved by proper vehicle management. The third part is the stacking system. Here an optimal policy minimizes the amount of reshuffling moves to keep handling times as low as possible. The last part is the delivery and receipt system. Not much research is done on optimizing this system.

When looking at the optimization options, one thing that stands out is that all solutions focus on their own sub area. For example, when optimizing the quay operation, the horizontal transfer system is not taken into account. This creates an inefficiency in the boundary areas between the different subsystems. An inefficiency that can significantly reduce the throughput and cost effectiveness of the terminal as a whole. In order to tackle this problem it would be better if the different optimization schemes take each other into account. Either to optimize the terminal as a whole rather than the different subsystems or to increase the efficiency of the terminal by

communication between these subsystems. In order to achieve this integrated optimization data is needed. The next chapter will look at how data is gathered at the terminal and how this can be used.

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4 Information Flow

To go from several separate optimization problems to the integrated problem, it is necessary to gain a sense of the information flow across terminals. By studying the information flow, a sense on how integration of information should be approached can be gained. Two types of flow can be identified. The first type is the information exchange between different actors on the terminal. This consists of all information exchanged by the terminal, the shipping companies, the rail operators and the road transport companies. The second type is the information flow needed for terminal operations. This includes all data that is needed to properly stack and retrieve containers and to guide terminal personnel and AGVs. Both types will be discussed separately below.

4.1 Information exchange between different actors

Looking at the flow of information worldwide, two big flows can be defined. The first flow consists of the different container terminals exchanging information with each other on how containers are loaded into the ship. The second flow consists of an information package per container that is exchanged with the rail and road transport companies.

The information on containers and how they are loaded into the ship is known as the bay plan. In the old days this plan was transferred over the phone or via a fax machine. Somewhere around 1995 the first global communication system via computers was implemented under the name Electronic Data Interchange (EDI) (Garstone, 1995). This made it possible to send the bay plan from terminal to terminal via an international standard. EDI is still used today to communicate bay plans between the different terminals and shipping lines.

Almotairi, Flodén, Stefansson, & Woxenius (2011) have written an elaborate paper on the information flows between different actors in terminal operations in Sweden. They have researched the coöperation between deep-sea terminals and rail-transport companies. Because their research consists of two terminals, namely a deep-sea terminal connecting container ships to rail transport and a second terminal to connect rail-transport to road-transport, this research covers almost all modalities of a full deep-sea container teminal. Therefore a great deal of knowledge about information exchange can be taken from their paper. This information is used to construct Table 1. In this table it is possible to see all information exchanges between the differen actors on a terminal. As an example, the journey of a group of containers from a container vessel on the open sea to a factory inland will be considered using the information provided by table 1. When the container vessel is still in the ocean, the loading plan has already been communicated to the terminal. This plan contains the expected arrival time and how the containers are loaded onto the vessel. The required

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containers are unloaded to the harbour. Here the containers are stacked. When they are retrieved, the containers designated for rail are loaded onto the train and a list of containers with information about their destination is sent by email to the inland train terminal. Information about containers meant for road transport are sent container per container by mail to the designated road transport companies.

Table 1: The data flow between different actors on a container terminal (Almotairi, Flodén, Stefansson, & Woxenius, 2011) Interesting to note is that apart from the bayplan, which is transferred through the EDI system, most information is still being exchanged via telephone, fax or e-mail correspondence. Because Sweden is among the more technologically advanced countries in the world, it can be assumed that most terminals will not exchange their data in a more sophisticated manner. This means that almost all couplings between different IT-systems are still performed by hand. The level of sophistication is low.

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4.2 Information flow in terminal operations

A modern container terminal is not able to function anymore without Information Technology. For example, in an eight-berth terminal where eight ships are berthed for loading/unloading some 6000 containers simultaneously, a highly sophisticated information technology is required to provide reliable and timely information for hundreds of people within the port/transport community (Shi, Tao, & Voß, 2011). In order to properly manage a terminal and the data collected and generated in this process an IT-system called the Terminal Operation System (TOS) was developed. Because no clear standard for TOS has been defined, Terminal Operation Systems come in a wide variety of flavours. They are either developed in house or by a third party and have to be adapted to fit a particular container terminal (Zhao, Liang, & Han, 2012). However, Terminal Operation Systems are of vital importance because through these systems it is possible to track the information flow through the terminal.

Throughout the terminal processes can be made more efficient if the data is properly managed. For example, with a proper stacking policy that takes into account which containers are needed first, expensive reshuffling moves can be avoided. Therefore, at multiple positions on the terminal information is required on where the container needs to go, at what time and where it is currently in the system. How this information is gathered depends on the terminal, but multiple methods exist (Shi, Tao, & Voß, 2011):

 Barcode scanners. This is the most common method in any system to retrieve information on a product. With a barcode scanner it is possible to retrieve information in any well-lit area by scanning a visual pattern on the container.

 Microwave technology. This technology consists of a readable tag which is connected to a container. Recent developments allow the tag to be read on a train moving with 110 km/h.

 Radio Frequency Identification (RFID). RFID consists of a tag that can be attached to containers, AGVs, equipment and personnel. Each tag has a unique code related to the object it is attached to. Tags can be read-only or rewritable. The tags can be read with an antenna embedded in the concrete of the terminal.

 Voice Recognition. Containers can also be identified by recognizing a voice pattern of the operator. This allows for a data-signal as well as a recorder voice message for other handling parties.

With information on position, destination and other relevant data becomes available real-time. This can all be integrated to optimize terminal operations further. This will be the subject of the next chapter.

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5 Information Integration

With the information on a terminal more centralized within the Terminal Operation System, further optimizations become available. With all the data centralized in the system it is possible to research more integrated optimizations of a container terminal than when all subsystems are viewed as different entities. Roughly two approaches are found in literature. One approach focusses mainly on optimizing the terminal as a whole, the other looks into optimizing the communication between the different actors within the system. In this review, the former shall be called the centralized approach, the latter shall be called the decentralized approach. Both research angles will be discussed in this chapter.

5.1 Centralized Approach

This section is focussed on integrating terminal operations and solving the system as a whole. An optimization strategy that is well researched for this application are Genetic Algorithms (Skinner, et al., 2013) (Lau & Zhao, 2008).

Genetic Algorithms (GA) is an optimization strategy based on evolution theory and the survival of the fitness. With the GA approach a set of solutions to the problem is generated. This set is then used to develop a new set according to certain rules. The fitness of this pool of solutions is evaluated and solutions that do not meet a certain fitness criterion are eliminated. The “fit” solutions are then used to generate new solutions. This cycle continues until the optimal solution is found. Advantages of this method are that it is possible to find good solutions to the most complex problems with a lot of variables. However, the optimal solution is not guaranteed within this approach. Another major disadvantage of this approach is the increase in computation power that is needed to generate the solutions when the amount of variables in the problem increases.

Lau & Zhao (2008) use this approach to optimize an automated container terminal. They have researched the use of two types of genetic algorithms, namely a Multi-Layer Genetic Algorithm (MLGA) and a Genetic Algorithm Plus Maximum Matching (GAPM), to optimize container terminal operations. The integrated problem is modeled as a mixed-integer problem which contains all the events like Quay Crane, Yard Crane and AGV moves. The Multi-Layer Genetic Algorithm (MLGA) is depicted in Figure 6 and is based on a sequence of events. The first layer optimizes the total sequence of events, while the second layer optimizes the AGV events only. Within the Genetic Algoritm Plus Maximum Matching (GAPM) algorithm the event sequence is also optimized by Genetic Algorithms, but the AGV assignment is done by modeling this problem as a matching problem.

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The GAPM performs much better than the MLGA but both methods are having computation time problems for instances larger than 1000 containers.

Figure 6: The Multi-Layer Genetic Algorithm (Lau & Zhao, 2008)

Another approach is the approach based on Model Predictive Control (MPC). Model Predictive Control is a control method that uses a dynamic model of the process and optimizes the current state over a finite-time horizon (Alessandri, Cervellera, & Gaggero, 2013). This means that, oposed to traditional control, this type of control is able to anticipate future events. However, the amount of parameters in a general container terminal is very high, causing this method to be very computationally heavy. Alessandri, Cervellera, & Gaggero (2013) propose to tackle this problem by combining this approach with a Neural Network (NN) algorithm. Neural Networks are networks of variables with weights that can be trained. By feeding the Neural Network with a set of example data, it can adjust the weights in

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such a way that it can approximate non-linear functions. The application in this paper consists of training a Neural Network offline and then using it online to reduce the computational load of the MPC calculations in what Alessandri et Al. call a Neural Network Algorithm Model Predictive Controller (NNAMPC). The results gained in this way generate higher cost than using the MPC alone. However, the computational load is reduced significantly, making this a useful approach for an operational decision support system (Alessandri, Cervellera, & Gaggero, 2013). However, the problem of the centralized approach remains the required computational power.

5.2 Distributed Approach

Computation power is a problem in general when looking at the centralized approach. Therefore, a Multi-Agent System (MAS) seems like a more promising approach to integrate terminal operation optimizations. Within a MAS all the different subsystems are governed by their own controller, called an agent. The difference between this approach and the traditional approach is that within a MAS, the different agents are able to exchange information in order to not only optimize their own subsystem, but also take the system as a whole into account. The advantage is that in this decentralized approach, the computing power is divided over the different agents allowing for more complex optimizations. Zhao & Cheng (2009) use the MAS approach to integratively optimize the Berth Assignment Problem and the Quay Crane Assignment problem. Both agents are controlled by a management agent to further optimize operations. They are able to achieve a linger time reduction of 3 – 8% by using MAS.

Figure 7: The architecture of a MAS as defined by Yan, Liu, & Xi (Yan, Liu, & Xi, 2008)

Yan, Liu, & Xi (2008) go a step further with the Multi-Agent approach and define a full system as depicted in Figure 7. The traditional decision problems are covered by their own controllers. These

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controllers are the Yard Agent, Transfer Agent and Quay Agent. Furthermore, they define a user agent which defines tasks for the users of the system, for example container X needs to go on truck Y. The Core Agent manages that statusses of the different agents and the Integrating Agent is thare to further optimize and coordinate the actions of the different agents as a whole. The method has not been verified yet by an actual modelling experiment, but is expected to result in better solutions than the integrated approach.

5.3 Outlook on the future

Up until now the further optimization of container terminals based on data integration on terminal level was discussed. However, when the arrival distribution of ships, trucks and trains is viewed as an external unknown parameter approximated by a probability density function, the optimization of the terminal will always be sub-optimal. Incorporating external information is necessary to further optimize terminal operations. Present research is looking into this direction.

Challenges in this field can be attributed to the competitiveness among terminals rather than the state of technology. With the internet, information exchange between the different actors is easy. The main questions that terminal owners face are “what do we share?” and “how do we keep the competitive edge?” Therefore, the information exchange problem is more about business decisions than technical ones but is important nonetheless and taken into account by most research done in this field.

Figure 8: From a bilateral system to the Port Community System (Jürgens, Grig, Elbert, & Straube, 2011)

Jürgens et al. are at the center of this argument. They argue that although different actors already exchange information through EDI this is a very rough and bilateral approach. They propose a Port Community System (PCS) which is centered at the container terminal. This system enables all parties, like trucking companies, train operators and shippers, to exchange information about the destination, Estimated Time of Departure (ETD) and Estimated Time of Arrival (ETA) of containers, ships, trucks and trains.

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A pilot with the Port Community System was performed at the port of Hamburg by Hamburger Hafen Logistik AG (HHLA). Using this information exchange system the following improvements were achieved:

 Parties involved in the transportation beneﬁted from the regularly updated sail- ing lists

 The need for special trains could be recognized promptly and planned in a better way

 The reliability of the process improved if data of appropriate quality were reported in time

 Customs could commence prompt veriﬁcation of the orders

 The restacking rate at the terminal decreased by 20-39%

 Train handling times decreased by approximately 30%

Overall all parties benefitted from this information exchange which paid off in better efficiency and reduced handling times (Jürgens, Grig, Elbert, & Straube, 2011).

While this approach is focussed around a single terminal also multiple terminal systems exist, for example in the port of Rotterdam. Here the port is a system of multiple terminals, all connected at the hinterland sides by roads and train tracks. Nabais, Negenborn, Benítez, & Botto (2013) view the different terminals as multiple actors within the port system. This allows them to control the full system with Model Predictive Control and the MAS approach.

Figure 9: Terminals as part of a Muti-Actor System (Nabais, Negenborn, Benítez, & Botto, 2013)

Figure 10: The Multi-Agent System approach (Nabais, Negenborn, Benítez, & Botto, 2013)

The advantage of this method is that this MPC approach allows for minimal information exchange. The main idea is to define a fine for not being able to handle the load. The MPC control algorithm will aim to minimize this cost by allocating newly arriving ships to a different terminal. When a terminal can no longer handle the load, information will be sent by the terminal control agent to the coordinator agent. The coordinator agent will than allocate ships to terminals that have excess capacity. In this way, the overall efficiency of the system will increase. A major strength of this approach is that no sensitive data needs to be shared. Only the marginal costs for the overload of a terminal are needed. Simulation shows that this approach as well as a centralized approach in which

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the terminals would share all information with the Port Authority outperforms the so-called “selfish” approach (Nabais, Negenborn, Benítez, & Botto, 2013).

Both papers point in the direction of inter-terminal information exchange to some extent. It makes sense to look further into this in the future. There definitely is risk involved in sharing information with peers, but much can be gained from this strategy. This is why a lot of companies now exist who are offering Port Community Systems to the different shipping ports. They seek to play the connecting role between all the stakeholders in the system and are united within the International Port Community Systems Association (IPCSA) (International Port Community Systems Association, 2014). Companies that offer these type of services in europe are Portbase (Rotterdam), DAKOSY (Hamburg), MPC plc (Felixstowe), Portic (Barcelona), dbh (Bremen) and SOGET (Le Havre). The association also has a lot of non-European members. All these companies offer community systems which allow data interchange between the different stakeholders of terminal operations.

The system Nextlogic in the Port of Rotterdam even takes this a step further (Nextlogic, 2014). This system entails an adaption to the original Portbase PCS and uses a neutral agent not unlike the system described by Nabais, Negenborn, Benítez, & Botto (2013). This neutral agent uses the information from the PCS to allocate barge operators to terminals in order to better allign the connection between deep-sea and inland shipping. Since this pilot, if succesful, yields considerable benefit for all parties involved, more initiatives like this can be expected in the future.

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6 Conclusion

In this literature review, the development of data integration at modern container terminals was covered. After a brief introduction in the different elements of the container terminal system the traditional decision problems on a container terminal were discussed. Among them were berth allocation, quay crane scheduling, stacking policy optimization and vehicle deployment optimization. After an outline of these decision problems, the data flow and integration were discussed. Concluding with an outlook on the future.

Concerning data integration, roughly two types of approaches can be identified, namely the centralized and the decentralized approach. The centralized approach focusses on optimizing the terminal as a whole. State of the art methods to do this include optimization based on genetic algorithms, neural network programming and dynamic programming methods. Some research in this field succeeds in improving terminal operation by solving this problem. However, there are two problems with this approach. The first problem is that these optimizations are so computationally heavy that it is not possible to perform them online. This means that the optimization will always be performed on some (simplified) model instead of the real-time situation. The second problem is that because a model is optimized, the differences with the real situation could cause the optimization to be sub-optimal and rather rigid. Automated Container Terminals exist, but most terminals involve a lot of human actors. These calculations leave no room for human error and differentiations from the modelled system. Therefore, this does not seem a very promising road.

The decentralized approach in this sense seems a lot more promising. Here the different parts of the terminal are different optimizing bodies that exchange information in order to not only optimize their own operation, but also take the terminal optimization into account. An operation that could be implemented via the Terminal Operation System that is already used in some form in most terminals and definitely looks promising in most automated terminals. In the future more research on this approach can be expected in the field of applying the latest developments on swarm intelligence and machine learning to the multi-agent container terminal.

The multi-agent approach can also be implemented one layer higher allowing for a network of container terminals in order to optimize the full supply chain. The technical possibility is already available, but most terminals are not yet equipped with an infrastructure to share information. Also, fear to lose the competitive edge by sharing information between terminals exist, making the barriers to implement a system at this level more political than technical. Regardless, research already has been done in this field and Port Community Systems are available. These type of developments are

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expected to grow further in the future, since the possible benefits of inter terminal cooperation clearly outweigh the costs.

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