A Dynamic and Collaborative Truck Appointment Management

System in Container Terminals

Ahmed Azab

, Ahmed Karam

and Amr Eltawil

Department of Industrial Engineering and Systems Management, Egypt-Japan University of Science and Technology,

POBox 179, New Borg Elrab city, 21934 Alexandria, Egypt

Mechanical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt

Keywords: Container Terminal, Integrated Simulation Optimization, Dynamic Model, Collaboration, Truck Appointment

System.

Abstract: Given the rising growth in containerized trade, Container Terminals (CTs) are facing truck congestion at the

gate and yard. Truck congestion problems not only result in long queues of trucks at the terminal gates and

yards but also leads to long turn times of trucks and environmentally harmful emissions. As a result, many

terminals are seeking to set strategies and develop new approaches to reduce the congestions in various

terminal areas. In this paper, we tackle the truck congestion problem with a new dynamic and collaborative

truck appointment system. The collaboration

provides shared decision making among the trucking companies

and the CT management, while the dynamic features of the proposed system enable both stakeholders to cope

with the dynamic nature of the truck scheduling problem. The new Dynamic Collaboration Truck

Appointment System (DCTAS) is developed using an integrated simulation-optimization approach. The

proposed approach integrates an MIP model with a discrete event simulation model. Results show that the

proposed DCTAS could reduce the terminal congestions and flatten the workload peaks in the terminal.

1 INTRODUCTION

In maritime logistics, one of the most important

performance measures is the delivery time of a

container to a customer. The containerized cargos are

transported through the global supply chain, and each

chain consumes a part of the total delivery time. Due

to that, the decision makers in each phase of the

transhipment operations are trying to reduce the total

transshipment time taking into consideration the

financial, economic, environmental, and even

political barriers.

Container terminals are essential nodes in the

global supply chain due to the tremendous growth of

the containerized cargo trade around the world (figure

1). As a result, the research interests are directed to

tackle the CTs’ problems and develop robust and

reliable solutions for the terminal operators. Figure 2

illustrates the various areas in CTs. Most of CTs can

be divided into three main areas: Seaside, yard area,

and landside. The seaside is the area where the vessels

are berthed, loaded and/or unloaded with the desired

containers using quay cranes. Containers are

transported by internal transport means like manned

trucks or automated guided vehicles to be temporarily

stored in the yard blocks. At the yard, handling

operations are performed using the yard equipment

like yard cranes and straddle carriers. The operations

in each yard block depend on vessel’s operations and

hinterland operations. On the other side of the

terminal, the landside comprises the gates, which are

provided with X-Ray scanners where an import

container is allowed to leave the terminal, and an

export container is allowed to enter the yard area.

CT problems were classified by (Bierwirth and

Meisel 2010) to operational problems and strategic

problems. The operational problems are related to the

scheduling of operations and assignment of the

resources. Operational problems are solved

simultaneously in the short term and solutions and

schedules are updated daily. Examples include berth

allocation and quay crane assignment (Karam and

Eltawil 2015; Karam and Eltawil 2016), and

container handling problems (Mohamed Gheitha et

al. 2014; Gheith et al. 2016). In this paper, more

discussion about landside problems will be

introduced mainly for managing the external trucks

arrival.

Azab A., Karam A. and Eltawil A.

A Dynamic and Collaborative Truck Appointment Management System in Container Terminals.

DOI: 10.5220/0006188100850095

In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems (ICORES 2017), pages 85-95

ISBN: 978-989-758-218-9

Figure 1: Global containerized trade, 1996–2015 (million

TEUs and percentage annual change). (Source: UNCTAD

secretariat).

Export/import containers are delivered/picked up

from the terminal by external trucks. These trucks are

operated by trucking companies to perform the

delivery/pick-up operations in minimal time and cost.

On the other hand, CTs set the appropriate schedules

and rules to reduce the congestion in various terminal

areas. To manage the transaction between the

terminal and the trucking companies, some CTs

adopted a Truck Appointment System (TAS) to

control the arrival of external trucks, while some

other terminals do not follow an appointment system.

The appointment systems can be used to increase the

service quality in CTs for all transshipment means;

trucks, train, barges and vessels (Zehendner and

Feillet 2014). Many terminals have developed Truck

Appointment Systems (TAS) to make balance in

truck arrivals to alleviate the terminal rush hours. The

benefits of the TAS have been reported in literature

as will be shown later. In this paper, we propose a

dynamic and collaborative appointment management

solution to support decision makers in the terminals

gain more benefits from applying the appointment

systems.

Figure 2: Operation areas of a seaport container terminal

and flow of transports (Steenken et al. 2005).

The remaining of the paper is organized as

follows. Section 2 discusses related literature. The

proposed system is explained in section 3. Section 4

presents the numerical experiment. Section 5 shows

the results, and section 6 illustrates the conclusion.

2 PREVIOUS WORK

Landside operations affect the whole terminal

performance and therefore, decision problems related

to landside operations received an increasing interest

in literature. Scheduling the arrival of external trucks

is considered one of the most important landside

problems addressed in the literature. One of the

earliest case studies is conducted by Murty et al.

(2005) at HongKong International Terminal (HIT) ,

which resulted in the reduction of terminal congestion

using the truck appointment system. Authors

developed a decision support system based on an

information system to help in making the terminal

operational decisions efficiently. A comprehensive

study by Morais and Lord (2006) is developed to

review the appointment system implemented in

terminals across North America. They adopted

various strategies to reduce the idling of truck,

congestion at gates and emissions related to CT

drayage operations. Namboothiri and Erera, (2008)

used a planning strategy for pickup and delivery

operations in CTs based on an integer programming

heuristic. The sequence of the drayage operations is

determined by minimizing the transportation cost. An

improvement in productivity and capacity utilization

is obtained with some sensitivity to poor selection of

the appointment time.

Huynh and Walton (2008) and Huynh (2009)

investigated limiting the arrivals and individual

appointments versus the block appointments. In

addition, they introduced combined mathematical

model and DES model. Guan and Liu (2009) stated

that the TAS is one of the most viable strategies to

avoid the terminal congestion and improve the system

efficiency. To achieve that, authors formulated a

nonlinear optimization model and applied a multi-

server queuing model. Chen and Yang (2010) studied

the export container’s drayage operations in Chinese

CT. They proposed an integer programming model in

order to reduce the transportation cost through time

window management. They indicated that the peak

arrivals are smoothed by solving the problem using a

genetic algorithm (GA). Zhao and Goodchild (2010)

studied the impact of using the arrival information of

external trucks on the yard operations. They

concluded that prior knowledge about the arrival time

ICORES 2017 - 6th International Conference on Operations Research and Enterprise Systems

of external trucks reduces the queue lengths at gates

and re-handling frequency at the yard. Chen et al.

(2011) introduced a stationary time-dependent

queueing model providing a supporting tool to

improve demand management at CTs.

Simulation was used in many studies for

developing and testing truck appointment systems.

Sharif et al., (2011) developed an agent-based

simulation model to achieve a steady arrival of

external trucks at container terminals. The results

showed that the congestion at CTs can be minimized

by using gate congestion information and estimating

the truck idling times. Karafa (2012) conducted a case

study using a dynamic traffic simulation model to

investigate the congestions and related emissions.

They concluded that extending the gate working

hours increases the terminal productivity and reduces

the emissions especially at peak hours. Based on a

previous work, Van Asperen et al., (2013) used a DES

model to investigate the effect of truck announcement

system on the yard operations performance, and a

significant reduction in yard crane moves is obtained

using the proposed algorithms.

Zhang et al., (2013) developed an optimization

approach for truck appointments to reduce the heavy

truck congestions in CTs. A method based on Genetic

Algorithms (GA) and Point Wise Stationary Fluid

Flow Approximation (PSFFA) was designed to solve

the problem that resulted in reducing truck turn times.

In a series of papers, Chen, Govindan, Yang, et al.

(2013), Chen, Govindan and Yang (2013) and Chen,

Govindan and Golias (2013) studied various

strategies and approaches to optimize the

appointments of external trucks in the terminal.

Various performance measures and objective are

examined such as transportation cost, fuel

consumption, shifted arrivals, and truck waiting

times. A new concept of chassis exchange introduced

by Dekker et al., (2013) to reduce the CT congestion

using simulation as a calculation tool. Zhao and

Goodchild (2013) used a hybrid approach of

simulation and queuing models to examine the impact

of the TAS on the performance of yard crane

operations. The results showed a significant

improvement in system performance and efficiency.

Zehendner and Feillet (2014) formulated a mixed

integer programming model to get the optimum

number of appointments considering the CT

workload. Results are validated using DES to ensure

the improvements of service quality for both the

trucks and also for all terminal resources.

Azab and Eltawil (2016) studied the effect various

arrival patterns of external trucks on truck turn times

in CTs through a simulation-based study. Their

results show that arrival patterns have a significant

effect on the terminal performance in such a way that

makes it important to consider the arrival pattern

effects during the design of the truck appointment

system. Li et al., (2016) proposed some response

strategies that help in solving the problem of truck

arrivals’ deviation from its appointments. Results

showed that the greenness of operations is

significantly affected by the use of truck

appointments. Chen and Jiang (2016) introduced

some strategies to manage the truck arrivals within

the time windows based on truck-vessel service

relationship to reduce the terminal congestion.

To sum up, an increasing attention is paid to the

TAS in literature. However, only two studies (Phan

and Kim (2015) and Phan and Kim (2016))

investigated the TAS with considering the

collaboration among trucking companies and the

container terminal. In these two papers, an iterative

approach is used to model the collaboration among

trucking companies and the terminal operator. The

iterative approach consists of two levels which are

interconnected by a feedback loop. The first level is a

mathematical model which includes a sub-problem

for each trucking company to minimize the total

waiting cost of trucks at the yard. On the other hand,

the second level is a procedure to estimate the

expected times at the yard of trucks based on the

solution of first level. This iterative approach enables

the collaboration process.

By careful investigation of the approaches

proposed in Phan and Kim (2015) and Phan and Kim

(2016), we notice three gaps that are needed to be

covered to improve the existing approaches. The first

gap is related to the second level where a simple

procedure is typically used to estimate the truck turn

times. This simple procedure lacks real world aspects

such as the waiting times of trucks at gate. The second

gap is that the existing approach did not consider the

randomness of the terminal operations. The third gap

is related to the number of times the trucking

companies and terminal operator send their decisions

to each other. According to Phan and Kim (2016) ,

their iterative approach needs about nine iterations on

average to terminate and produce the final solution.

In contrast, the proposed system in this paper requires

only 2 iterations between trucking companies and the

container terminal. From a practical point of view,

large number of iterations may cause some of

trucking companies not to submit their appointment

applications for some reasons such as not having time

to reschedule their truck operation or forgetting to

resubmit their applications. In this case, the quality of

the solution may be impaired.

A Dynamic and Collaborative Truck Appointment Management System in Container Terminals

Based on the above understandings, we propose a

new approach for dynamic and collaborative truck

appointment scheduling in container terminals. The

proposed approach considers the collaboration

among trucking companies and terminal operators by

a pre-processing integration of a mixed integer

programming model and a discrete event simulation

(DES) model. The contributions of the proposed

approach are as follows:

1) The turn times of trucks are estimated based on a

simulation model which enables capturing several

real world aspects as well as the stochastic nature

of the terminal operations.

2) By employing the pre-processing integration, the

trucking companies send their rescheduled

appointments to the terminal two times only.

Thus, this improves the applicability of the

proposed new appointment system.

3 THE PROPOSED DYNAMIC

AND COLLABORATIVE

TRUCK APPOINTMENT

SYSTEM

In this section, the proposed Dynamic Collaborative

Truck Appointment System is introduced (DCTAS)

based on the collaboration concepts. The paper

introduces an integrated simulation optimization

approach to achieve the collaboration goal

considering both the dynamic and stochastic nature of

the problem. The proposed DCTAS (figure 3) can be

illustrated in five operational steps as follows:

Step (1): each trucking company submits an

arrival proposal to the terminal. This proposal

contains the preferable arrival time of their external

trucks based on some factors such as; ship arrival,

container dwell time, ship departure, available trucks,

etc.

Step (2): once the terminal operators receive the

submitted proposal, the terminal working load is

updated and the performance measures are

determined. To do this, a DES model of the terminal

is introduced to help the terminal operator to estimate

the total truck turn time for the trucking company and

evaluate the terminal congestion at each yard block

(YB) during each working hour (time window). It is

assumed that the workload of the CT contains the set

of confirmed appointments that are already reserved

before the terminal appointment application's

deadline for each time window (Tw) and the ship

tasks assigned to each yard block.

Step (3): The terminal operators publishes the

schedule information online with the expected turn

times for all submitted requests. Each trucking

company is then capable of knowing how much time

they are supposed to spend in the terminal (turn time)

to achieve their delivery/pick up tasks.

Step (4): To avoid going to the terminal in

congestion times, the trucking company will use the

mixed integer programming (MIP) model available as

a scheduling tool for their trucks. The MIP model is

solved to reduce the transportation cost in the CT

considering the previous preferable arrival time (step

1) and the terminal performance measures (step 2),

and a new arrival request will be issued.

Step (5): the new schedule will be submitted as a

confirmed appointment request and the terminal

workload will be updated waiting the new requests to

be submitted and confirmed.

Figure 3: The operational steps of the proposed DCTAS.

As illustrated, the DCTAS provides an interactive

management strategy between the stakeholders to

cope with the dynamic nature of the appointment

process in CTs. Interacting communication among

stakeholders can be implemented easily using an

online collaboration platform. In a previous work,

(Azab et al. 2016) adopted a design thinking strategy

to design and synthesize an online information system

for transportation logistics. Whenever a trucking

company is ready to submit the preferable arrival

times, the system receives the appointments and deals

with the workload updates and changes hourly.

ICORES 2017 - 6th International Conference on Operations Research and Enterprise Systems

Moreover, using the DES model is expected to

enhance the solution and to accommodate the

system’s actual variability and randomness. This

randomness results from the stochastic operations and

events such as the gate service rate, inter-terminal

traveling times, yard crane handling rates, quay crane

handling rate, and the failure of equipment. The

proposed simulation optimization approach integrates

the MIP model with the DES model in a pre-

processing way (Bierwirth and Meisel 2015) in which

the problem under particular circumstances is solved

to produce the input data for the other problem. The

DES model provide the input to the MIP model. After

solving the MIP model, the optimum truck

appointment schedule is evaluated using the

simulation model to get the turn time of trucks after

optimization.

3.1 The DES Model

The DES model is built using “Flexsim CT

”

package, which is a special software for simulating

container terminal operations. The basic elements of

the model are shown in figure 4. The 3D DES model

includes five yard blocks, five yard cranes, four gates,

and a single shared gate queue. When the external

truck arrives at the gate according to the

predetermined schedule (Tables 2-3), the truck joins

a single queue shared among the four gates. Trucks

will leave the gate queue to the first available gate and

will be processed according to an Erlang distribution

(0.65,4) (Guan and Liu 2009). Once the truck

completes processing at the gate, the trucks are

directed to the yard block that contains a container to

be picked up or to the location of the container where

it will be dropped off. Yard cranes are the equipment

that handles the container within the blocks to/from

the external trucks. The external trucks leave the

terminal after finishing the pickup/drop off operation.

On the seaside of the terminal, the arriving vessels are

berthed, and there is a truck gang that serves each

quay crane assigned to the vessel. The internal trucks

deliver the containers between the seaside and yard

area. At the yard block, the highest priority is given

to shipside operations, next to gate side operations

and lastly to internal yard operations.

There are some assumptions that are used in this

simulation model. At the gates, it is assumed that all

arriving trucks will share the same queue before

going to the first available gate, and external trucks

travel time within the terminal is neglected. As a

result, the truck turn time will be the sum of the gate

queue waiting time, the gate service time, the yard

waiting time, and the yard service time. To obtain

more accurate results, each time window is divided

into four time intervals, and the average truck turn

time is calculated per each time interval. Moreover,

the collision of trucks traveling through the internal

transportation network of the terminal is not

considered. Because the problem is regarded as a

design problem for a new appointment system, the

input parameters are driven from literature and based

on some experience. Berth and yard cranes service

rates are represented by the average net moves/hr

calculated form the busy time and truck throughput

for each crane. Table 1 illustrates the input parameters

to the DES model.

Figure 4: 3D discrete event simulation model.

A Dynamic and Collaborative Truck Appointment Management System in Container Terminals

Table 1: the input parameters to DES model.

General parameters

Working hours (Tws) 8:00 am- 12 pm

Truck speed (max) 300 m/min (18 km/hour)

Container dwell time Exponential(0.3) [days]

Gate Parameters

Process time (min) Erlang (0.65, 4)

Gate capacity 1 truck/one gate

Yard parameters

Crane speed (max) 90 m/min (empty/loaded)

Block capacity (max) 24 containers

Crane net moves 27.7 move/hr (average)

Quayside parameters

crane speed (max) 120 m/min (empty/loaded)

Crane net moves 12.3 move/hr (average)

3.2 The Scheduling Problem: MIP

Model

In most container Terminals, the arrival of external

trucks from the hinterland is a random process that is

affected by the preferable arrival times of trucking

companies. These preferable arrival times are not

known by the terminal operators to be considered in

planning and scheduling operations. As a result, a

truck may arrive during a congestion time where the

waiting time is costly and the emissions are high. On

the other hand, if these trucks are forced to come at

certain times that are specified by the terminal

operators, it may be inconvenient for some trucking

companies due to the trucks availability and other

operations outside the terminal. To tackle this

problem, the following mathematical model

considers both, the convenience of trucking

companies to arrive at their preferable times and the

total time spent in the terminal which is influenced by

the terminal congestion.

Based on the mathematical models formulated by

(Phan and Kim 2015), we modified the model to

consider the truck turn time (TT

) of trucks which is

derived from the DES model. The proposed DCTAS

assumes that each trucking company develops its

preferable schedule considering the available number

of trucks at each time window (s

kτ

). The trucking

company’s operator defines all tasks to be performed,

which represents a pick up or a delivery operation for

one container using one truck. Tasks that are assigned

in the same preferable arrival hour (time window) are

grouped together in one task group. For a certain task

group, Containers can be delivered or picked up from

the same yard block or from several yard blocks (table

2). The used parameters and indices in MIP model are

defined as follows:

i index for a task group

j index for a yard block

k index for a trucking company

τ index of a time window

t index of a time interval. Note that multiple

time intervals exist in a time window

earliest possible (lower) bound of the time

window for task group i

latest possible (upper) bound of the time

window for task group i

number of tasks to be done for task group i

kτ

number of available trucks of company k

during time window τ

most preferable time window at which

containers of task group i to be stored or

retrieved

σ number of time intervals per each time window

maximum number of containers of task i that

can be allocated to yard block j

cost of late arrival by a unit time compared

with the preferable time window of task i

cost of early arrival by a unit time compared

with the preferable time window of task i

truck waiting cost in the terminal of truck

company k per time interval

P congestion penalty in $, a strategic parameter

determined by the terminal manager.

average truck turn time for a truck arriving at

yard block j at time interval t derived variables

from the DES model

Sets

I set of task groups.

K set of trucking companies.

T set of time intervals.

J set of yard blocks j

W set of time windows.

Decision variables:

ijτ

number of trucks for task group i which are

deployed to yard block j at time window τ

Derived variables from the MIP model:

ijt

average arrival rate of trucks for task group i at

yard block j at time interval t

ICORES 2017 - 6th International Conference on Operations Research and Enterprise Systems

Minimize:

(1)

Subjected to:

(2)

(3)

(4)

(5)

(6)

(7)

The objective function (1) is to minimize the cost

of shifting (delaying or advancing) the appointment

and the truck turn time (TT

) cost within the terminal.

The total number of scheduled trucks must satisfy the

number of containers to be delivered or picked up (2).

Constraint (3) states that the number of trucks to be

assigned to task i cannot be larger than the resource

level of the trucking company. The capacity

constraint of each yard block is described in (4) to

ensure that the number of containers for each task

group have to be smaller than or equal to the available

spaces in yard blocks. There is an earliest and latest

feasible time window for each container (5). To

calculate the arrival rate for each task group,

constraint (6) is used. Constraint (7) illustrates the

domain of each variable in the problem.

4 NUMERICAL EXPERIMENTS

In this section, a numerical example is solved to

illustrate the operational scenario and performance of

the proposed DCTAS. Table 2 shows a proposed

appointment application for 4 trucking companies.

Each trucking company is assumed to have a specific

number of containers (d

) in the terminal. The task

group is a set of tasks that will be submitted by the

same trucking company at the same preferred arrival

time (p

). It is assumed also that each trucking

company knows which yard block (j) holds its

containers. To create a workload in the terminal, the

externally confirmed applications and inter-terminal

tasks are developed in order to investigate the

response of the proposed system to the heavy-loaded

time windows. The proposed system (DCTAS) is

expected to shift the proposed arrival appointments to

the time windows where the turn time cost will be

minimized with consideration of the preferred arrival

times. Table 3 illustrates the tasks that are assumed to

be already reserved and confirmed.

To start working with the DCTAS, all tasks are

loaded to the simulation model input. Each task has a

corresponding arrival time, the number of containers,

and yard block location. By running the DES model,

the external trucks arrive to the terminal model

according to the predetermined scheduled times and

released out of the system as the task is completed.

The average truck turn times at each yard block are

recorded for each time window to be used in the MIP

model input. Other performance measures can be

derived from the simulation model such as the queue

length at gates, waiting times at gates and yard,

service rate at gates and yard, cranes’ utilization, etc.

Table 2: Proposed appointment applications for four

trucking companies.

Truck.

Company

Task

group

di Pi j

TC1

1 5 2 1

2 3 4 2

2 2

TC2

4 3 4 4

5 4 3 2

6 4 1 3

TC3

2 3

3 4

TC4

9 3 2 4

5 5

A Dynamic and Collaborative Truck Appointment Management System in Container Terminals

Table 3: the reserved tasks in the CT.

Confirmed

tasks

Di Tw j

11 30 2 1

12 30 3 2

13 30 2 3

14 30 2 4

15 30 3 5

16 10 (to ship) 3-4 1

To get statistically reliable results, the simulation

model is run for 35 replications which are used to

determine the 95% confidence intervals of the

targeted mean performance measures. After obtaining

the results from the simulation model, the derived

variables are sent to the MIP model. The MIP model

is solved using a personal computer with Intel

Core

i7 CPU and 4 GB RAM. IBM Ilog CPLEX

Optimization Studio version 12.2 is used to code the

problem and get the optimum solution. The cost

parameters in the objective function are assumed to

be $1, $4, $5, and $2 per each time for

, w

, and P respectively. Table 4 shows the

available number of trucks (s

kτ

) for each trucking

company per each time window. In Constraint 3, the

number of available trucks is used to guarantee that

the new assigned tasks do not exceed the trucking

company’s available trucks per each time window.

5 RESULTS AND ANALYSIS

Table 5 shows the MIP model optimum solution of

the provided instance. In Table 4, di represents the

number of containers submitted before solving the

problem. After solving the DCTAS problem, the X

ijt

describes the new scheduled tasks proposed for the

trucking company to reduce the total cost of

delivering a container to the terminal. There are three

possibilities noticed from the results to occur after the

solution to the input schedule of the DCTAS. The first

possibility, there will be no change in the schedule

such as task group 8. The second possibility, the task

group preferred time window will be advanced or

delayed resulting in an advancing and/or delaying

cost without any change in the number of containers

per task. For example, the arrival time of task group

5 is shifted from Tw3 to Tw2. This seems reasonable

because, at yard block 2, the workload in Tw3 was the

highest among the other three time windows in the

same block before the solution. The third possibility

is that the task group will be decomposed to smaller

mini-task groups. It is evident that the second and

third possibility may occur together like in task

groups 1, 2, 7, and 10.

Table 4: the available number (S

kτ

) of trucks for each

trucking company per each time widow.

Truck.

Company

Tw1 Tw2 Tw3 Tw4

TC1 3 5 6 4

TC2 7 4 1 5

TC3 6 1 2 4

TC4 3 4 3 4

Table 5: The DCTAS solution.

Truck.

Company

Obj.

value

($)

Task

group

di Xijt Tw j

TC1 137.8

1 5

2 1

3 4

2 3

1 1

2 2

1 1 4 1

2 2 2 2

TC2

114.7

4 3

4 4

5 4

2 2

1 3

TC3

101.75

4 4 4 1

1 2

1 3

2 2 1 2

3 3 1 4

TC4 130.31

9 3 3 4 4

1 1 4 3

4 2

1 3

To investigate the solution performance, the

simulation model is used to test the performance of

the output schedule from the MIP model and compare

it with simulation results before solving the MIP

model. In other words, we need to see how the

proposed schedule differs from the optimum schedule

after applying the DCTAS. The average truck turn

times at each block j per each time window τ (TT

jτ

)

are recorded for the proposed (preferred)

appointments and the optimum appointments. Figures

(5-9) show a comparison between the TT

jτ

values for

the proposed (preferred) appointments by the

trucking companies versus the optimum

appointments after applying the DCTAS.

ICORES 2017 - 6th International Conference on Operations Research and Enterprise Systems

Figure 5: Average TT

1τ.

Figure 6: Average TT

2τ.

Figure 7: Average TT

3τ.

Figure 8: Average TT

4τ.

Figure 9: Average TT

5τ.

Results show that there is a difference between the

jτ

values before and after applying the proposed

appointment management system. To confirm this

difference, a t-test is conducted for TT

jτ

values with a

95% confidence interval using Minitab 17 statistical

software to test the 35 samples (replications) of TT

jτ

The statistical results show that there is a significant

difference between the average TT

jτ

values before and

after solution for most points such as Tw3 at YB1,

Tw4 at YB2, Tw4 at YB3, Tw3 at YB4, and Tw4 at

YB. While, some points did not depict significant

differences in average TT

jτ

such as Tw1 at YB2, Tw2

at YB3, Tw2 at YB4, Tw4 at YB4. It is noticed that

the number of proposed tasks within some task

groups increased after solution because some task

groups are decomposed to two or three tasks.

However, this reduces the turn time cost for the

external trucks, some trucking companies may be

inconvenient due to shifting their preferable arrival

times. For the CT, distributing the arrival

appointments over the terminal working hours is good

to avoid congestion in certain times windows. From

another side, reducing congestion and decreasing

waiting time will result in less emissions and less fuel

cost as well increased efficiency for the trucking

companies. The results showed also that the average

queue length at gates is reduced by 21% and the

average truck turn time is reduced by 22.6% after

applying the proposed system.

6 CONCLUSIONS

This paper proposed an integrated appointment

system by which both the CT and trucking companies

collaborate in determining the arrival schedule of

external trucks. The proposed Dynamic Collaboration

Truck Appointment System (DCTAS) integrates a

discrete event simulation model with an MIP model

A Dynamic and Collaborative Truck Appointment Management System in Container Terminals

using pre-processing integration. In the proposed

DCTAS, the terminal operator firstly uses the

simulation model to evaluate the turn times of the

trucks considering their preferred arrival times. Then,

the trucking companies solve the MIP model to

reduce the total stay cost in the terminal. Finally, the

terminal operator uses the rescheduled appointments

of the trucking companies as inputs to the simulation

model to produce the final appointment times and

container schedule.

The results showed that the DCTAS could reduce

truck congestion at the time windows where the

terminal workloads are high. Moreover, the DCTAS

could smooth the terminal workload and balance the

arrival processes of external trucks. Thus, both

stakeholders can benefit from applying the proposed

appointment strategy. In addition, the rescheduling

frequency is reduced compared to the existing

literature approaches.

For future work, the proposed system will be

implemented to a real case study and the effect of

applying the proposed DCTAS on landside

operations, yard operations and seaside operations

will be investigated. Also, it is important to examine

the emissions from trucks and terminal equipment

after applying the DCTAS. One more issue that is

expected to increase the appointment system

performance is to consider truck sharing and

collaboration between the trucking companies to

reduce the empty truck trips. For instance, a trucking

company may have a truck with an empty trip during

a pickup task, which can be utilized by another

trucking company to deliver a container to the

terminal. This truck sharing process can be

considered also in the appointment process.

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