Discrete Event Simulation for Port Berth Maintenance Planning

Abstract

Industrial and commercial ports, which are one of the three main hubs to the country, require 24/7 operations to maintain the goods export and import flow. Due to the aging and weather factors, berths require regular maintenance, such as replacing old piles, timber finders, marine ladders, rubber fenders, and deck slabs. For efficient berth maintenance, strategies are highly desired to minimize or eliminate any delays in operations during the maintenance. This paper develops a discrete event simulation model using Simphony.NET for berth maintenance processes in Doha Port, Kuwait. The model derives minimum maintenance duration under limited resources and associated uncertainties. The model can be used as a decision support tool to minimize interruption or delays in the port maintenance operations.

Full text

Proceedings of the 2022 Winter Simulation Conference
B. Feng, G. Pedrielli, Y.Peng, S. Shashaani, E. Song, C.G. Corlu, L. H. Lee, E.P. Chew, T. Roeder, and
P. Lendermann, eds.
DISCRETE EVENT SIMULATION FOR PORT BERTH MAINTENANCE PLANNING
Ruqayah Alsayed Ebrahim
Shivanan Singh
Yitong Li
Wenying Ji
Department of Civil, Environmental, and Infrastructure Engineering
George Mason University
4400 University Drive
Fairfax, VA 22030, USA
ABSTRACT
Industrial and commercial ports, which are one of the three main hubs to the country, require 24/7
operations to maintain the goods export and import flow. Due to the aging and weather factors, berths
require regular maintenance, such as replacing old piles, timber finders, marine ladders, rubber fenders, and
deck slabs. For efficient berth maintenance, strategies are highly desired to minimize or eliminate any
delays in operations during the maintenance. This paper develops a discrete event simulation model using
Simphony.NET for berth maintenance processes in Doha Port, Kuwait. The model derives minimum
maintenance duration under limited resources and associated uncertainties. The model can be used as a
decision support tool to minimize interruption or delays in the port maintenance operations.
1 INTRODUCTION
The construction industry is complex and requires detailed analysis for enhanced overall performance.
Berth maintenance/rehabilitation plays an important role in port or harbor projects, and it is deemed as an
infrastructure project involving numerous risks compared to other construction projects. Examples of these
risks include shortage of materials, extreme weather (e.g., dust storms), and equipment breakdowns. To
ensure the efficiency of berth maintenance projects, strategies that consider resource limitations and risks
need to be developed. This research focuses on the berth maintenance project in Doha Port, Kuwait. The
small, shallow, and merchant port is located in the ‘S’ part of Kuwait servicing dhows, barges, and other
coastal vessels operating between ports of the Persian Gulf. The semi-closed basin design, as shown in
Figure 1, handles 7,000 small vessels and approximately 200,000 tons of cargo annually (Shipnext 2022).
Recently, Kuwait Ports Authority has pursued its goal of modernizing and expanding its ports’ facilities to
accommodate larger vessels and reduce discharge times of cargo. Therefore, site investigations and tests
were conducted on the port docking facilities (Figure 1) to investigate the durability of the old concrete
deck slabs, and their capacity to withstand large and heavy loads from present-day cargo.
The tests identified a major problem with the berth—the piles and slab decks were completely damaged.
Due to the damage, the berth was insufficient to receive ships, which have caused delays in the operation
of exporting and importing goods. As such, it was required that significant rehabilitation activities should
be conducted. Precast construction was employed (specifically precast concrete slabs for the slab decks) in
an attempt to accelerate the project duration and minimize further delays.

Ebrahim, Singh, Li, and Ji
Figure 1: Kuwait Ports Authority, Doha Port Layout, Kuwait (Scale 1:5,000).
The construction process involves 12 phases (shown in Figure 2): removal works, reinforcement,
pouring concrete, structure pile installation, fender pile installation, reinforcement of precast concrete slabs,
precast concrete slab installation, front deep beam reinforcement, front deep beam concrete pouring, timber
fender installation, and rubber fender installation and painting.
Figure 2: Phases of the berth maintenance project.
In detail, “removal work” starts with the removal of the existing 23,000 𝑚2 concrete deck slabs and the
removal of the outer concrete layer of the concrete beam until they reach the reinforcement layer. If the
reinforcement is not completely damaged, then it will be treated with Epoxy, otherwise, it will be replaced
by new reinforcement. Then, the treatment of pile caps starts followed by “concrete beam reinforcement”
and “beam concrete pouring.” Prestressed “structural pile installation” starts where they dug around 60
structural piles (4 piles/day) using a 3 ton hammer and a crane. The structural pile is 36 cm (about 1.18 ft)
× 36 cm (about 1.18 ft) and 16 meters long (size 2.07𝑚3). Then, prestressed “fender pile installation” starts
where they dug around 108 fender piles (5 piles/day). The fender pile is 36 cm × 36 cm and 17.5 meters

Ebrahim, Singh, Li, and Ji
long. At an offsite facility, “precast concrete slab reinforcement” is installed in a suitable steel mold, and
concrete is poured. Once the concrete has acquired an adequate compressive strength, slabs are then
transported to site where 23,500 𝑚2 of “precast concrete slab installation” is performed with a crane
followed by “front deep beam reinforcement”, “front deep beam concrete pouring”, “timber fender
installation”, “rubber fender installation” and “painting”, respectively. Table 1 shows the relationship
between resources (materials, labor, and equipment) and project activities.
The objective of this research is to develop a simulation model to mimic the berth maintenance process
in Doha Port, Kuwait, using the construction simulation platform Simphony.NET (AbouRizk et. al. 2016).
This platform was chosen because it was specifically developed for the construction industry and the variety
of modeling elements can realistically mimic what occurs on construction sites. In detail, the proposed
discrete event simulation model incorporates the process of the berth maintenance project as well as
resource limitations and uncertainties associated with the process. The model can be used to inform owners,
project managers, and contractors with adequate resource allocation and optimum scheduling performance.
Furthermore, the client can use this model to realistically criticize construction proposals submitted by
contractors during the bidding process.
Table 1: Project resources associated with their tasks.
Resources
Tasks
Crane
Loading and unloading the following materials: concrete
formwork, piles, precast concrete slabs, timber fenders
and rubber fenders
Excavator
Excavation
Jackhammer
Old concrete deck slabs removal
Pile hammer
Installing piles
Dump Trucks
Hauling excavated material
Concrete Pump
Pouring concrete
Concrete Trucks
Loading concrete to the site
Forklift
Loading and unloading timber and rubber fenders
Piling Crew
Pile head cap treatment, inner piling and outer piling
Concrete Crew
Reinforcement installation, concrete pouring and
concrete curing
Laborers
Old concrete deck slab removal, epoxy treatment, timber
and rubber fender installation and painting
2 LITERATURE REVIEW
Berth maintenance/rehabilitation process depends on the tests and site investigations results, and the size
of the damage (e.g., minor or major damage). In cases of minor damage, the maintenance/rehabilitation
process begins with the repair of areas with spalling and exposed reinforcement. Then, concrete pavement
repair work begins by sealing cracks followed by installing new bollards, fenders, life ladders, and
manholes. In cases of major damage, the process includes deck slabs repair, pile cap repairs, demolition
and removal of existing infrastructure, dredging and dumping of material, reclamation of land, construction
of embankments, construction of reverted slopes, provision of infrastructure utilities (water, electricity,
etc.), erection of reefer stacks, and construction of drainage systems (KPA 2022).
Seaborne trade development is affected by demand trade patterns and competition. The difference in
demand could create an uncertain environment to develop a strategic decision-making process. “Strategic
and tactical decisions tend to be capital intensive and must be flexible to adapt and expand in terms of
infrastructure and technological changes in the long run” (Stopord 2008). Tools that have been used to
identify and visualize the process of berthing and port operations are Business Process Modeling Notation
(BPMN) (BPMI 2004) and Discrete Event Simulation Modeling (DES) (Caceres et. al. 2015). Another tool

Ebrahim, Singh, Li, and Ji
called STAADPRO (Research Engineers international 1997) has been used to model the design of a
proposed marine berthing structure using induced load distribution (Vivek and Prasad 2016). No research
has been found about simulation models of berth maintenance/rehabilitation processes involving
risks/uncertainties that answers the question “what if?”
Simphony.NET construction simulation appeared to be the most feasible as it can model complex
processes involving uncertainties. As such, understanding of such processes can be communicated and
validated by experts and then can be analyzed by us to look for alternatives that can be easily translated
into execution language by non-technical users (Hajjar and AbouRizk 1999). Simulation modeling of berth
maintenance/rehabilitation process is a digital prototype of a real-world problem to predict the performance
by presenting materials, equipment, and labor as resources, operations as tasks, and equipment breakdowns
or severe weather conditions as sub models. The simulation model presented in this paper analyzes the
construction process of rehabilitating the berth taking into consideration of uncertain events that can affect
the project schedule and its outcome. The results of such a model can help the owner/contractor/project
manager control and monitor the project progress, schedule, and cost.
Time-cost tradeoff projects or problems in project management can involve some crashing. Crashing
in construction projects include activity crashing due to materials, labor, and equipment crashing, which
means adding costs to meet the project schedule (Pena 2009). To minimize the total project cost and time,
the crashing of an activity answers the question “what is the maximum number of time units that an activity
can have during crashing and use it to reduce the activity time?” (Pena 2009). By identifying and providing
z input parameters such as the number of activities, the probability distribution of completion of each task
or activity, the maximum completion time, and the maximum crashing time, a simulation model can be
constructed to determine the average project cost/time resulting from crashing.
3 METHODOLOGY
The presented methodology consists of one main component of a simulation model using the construction
simulation platform, Simphony.NET. The rehabilitation of a berth follows a particular sequence of steps
determined by engineers to produce an effective, structurally sound final product. In this scenario, the old
berth’s concrete components needed to be partially demolished and then repaired, followed by new
construction. The model developed by Simphony.NET follows the exact sequencing of events in a
controlled environment. The entire model can be simplified to smaller activities where various resources
were determined and configured to be captured or released as required. These resources move from one
activity to another, however, are not shared between them simultaneously. Due to site space constraints
(being located next to the ocean), access is restricted, thereby discouraging concurrent construction
activities.
Understanding previous berth construction provided a basis in which we can develop the model, and
further change resource elements to improve production. As such, the model was developed in a way to
closely mimic what occurs on site. Entities are not flowing freely from the beginning to the end of the
model, but rather grouping intermittently to finish a task, and then moving to other tasks at similar times.
This modelling behavior was purposely adopted since on-site activities occur the same way. For example,
all exposed steel must be completely treated (grouped) with epoxy before new reinforcement installation
can occur. Similarly, all concrete for the deck needs to be poured together (completely grouped) before
moving onto the curing phase. With this modelling configuration, the client or engineer can more accurately
simulate construction events, thereby allowing them to choose the most suitable resource allocation
strategy.
4 CASE STUDY AND RESULTS
4.1 Data Preparation and Model Inputs
On November 10, 2019, rehabilitation of the first berth commenced and lasted 189 days. Compared to the
contractor’s bid schedule of 156 days, there was a delay of 33 days which ultimately increased project costs.

Ebrahim, Singh, Li, and Ji
This also delayed docking of container ships which resulted in a penalty fee that the port authority ultimately
had to pay. The actual completion times of each activity for one berth (100m in length) are given in Table
2.
Table 2: Duration of rehabilitation project tasks.
Project
Task
No.
Project Task
Time
(days)
Project
Task No.
Project Task
Time
(days)
1
Jackhammering Concrete
30
11
Piling (Inside Berth
15
2
Excavation
10
12
Piling (Outside Berth)
22
3
Hauling Material Offsite
4
13
Install Precast Slabs
2
4
Applying Epoxy to Steel
3.25
14
Install Deck Reinforcement
8
5
Treating Pile Heads
30
15
Pour Deck Concrete
2
6
Install New Reinforcement
24
16
Cure Deck Concrete
3
7
Install Formwork
15
17
Install Timber Fenders
8
8
Pour Beam Concrete
3
18
Install Rubber Fenders
10
9
Cure Beam Concrete
3
19
Painting
2
10
Remove Formwork
10
4.2 Model and Assumptions
The model was built to closely mimic the activities of construction of a previous berth. An event must be
started and finished in order to move on to the other event, and so on. The end of the model would signify
the completed construction of one 100m long berth. The resources linked to the main model are shown in
Figure 3. The main model schematic is illustrated in Figure 4. Explanations of the model elements are given
in Table 3.
Table 3: Model elements’ legend.
Element
Description
Element
Description
Represents a resource
with a specified number
of servers
A file in which entities
wait for resources
Creates entities
Batches a group of
entities
Allows an entity to take
one of two paths
depending on a specific
condition
Delays an entity for a
specified amount
A modeling element
that generates clones of
an entity and sends them
out at a separate output
point
A modeling element
that consolidates a
specified number of
entities before releasing
one

Ebrahim, Singh, Li, and Ji
Generates or
consolidates entities
Counts the number of
entities passing through
the element
Allows an entity to
request servers of one or
more resources
Allows an entity to
release servers of one or
more resources
Allows an entity to
preempt a single server
of a resource
Allows entities to pass
or block them
depending on a state
variable
Allows an entity to take
one of multiple paths
depending on specified
probabilities
Allows an entity to
change the state of a
valve
Receive entities
Send entities
Executes a formula
when an entity passes
through
Destroys entities
Figure 3: Resources used for modeling the project using Simphony.NET.
Concrete Plant
Concrete Plant Q
General Labor Crew Q
General Labor Crew
Crane
Crane Q
Excavator
Excavator Q
Concrete Pump Q
Concrete Pump
Forklift
Forklift Q
Excavator
Jackhammer
Concrete Crew
Concrete Crew Q
Pile Driving
Crew
Pile Driving
Crew Q
Pile Crew
Pile Crew
Pile Crew Q
Excavator
Jackhammer Q

Ebrahim, Singh, Li, and Ji
Figure 4: Simulation base model of the project using Simphony.NET.
Create 100m of
Berth
Create Dump Trucks
Jackhammer
Concrete
Batch 10m Sections
Batch
100m
Convert to
100m Sections
Consolidate 1
No. of loads
Haul and Return
Excavate
Material
Generate2
Bath 10m
Section
Generate4
Apply Epoxy to
Exposed Steel
Consolidate4
Treat Pile
Heads
Unbatch 10m
Batch 100
Install Beam
Reinforcement
Capture Crane
Install Formwork
Install
Formwork
Release Crane
Install Formwork
Unbatch 100
Cure Beam
Concrete
Batch 100
Generate1
Release Concrete
Pump
Pour Beam
Concrete
Capture Concrete
Pump
Consolidate2
Capture
Crane
Remove
Formwork
Release
Crane
Convert to
5m Sections
Fetching
Activator
Capture
Crane Piling
Capture Pile
Driving Crane
Release
Crane 1
Install
Reinforcement
Install Precast
Slabs
Capture
Crane 1
Batch
100_2
Release Piling
Equipment
Piling (Outside
Berth)
Piling (Inside
Berth)
Convert to
1m Sections
Consolidate3
Pour Deck
Concrete
Release Concrete
Pump 1
Generate3
Batch 100m
Release Fender
Install Equipment
Install Rubber
Fenders
Install Timber
Fenders
Capture
Forklift
Capture Crane
Fender Install
Cure Deck
Concrete
Painting
Production
Destroy
Capture Concrete
Pump 1

Ebrahim, Singh, Li, and Ji
It was assumed that the General Labor Crew resource can be utilized for several activities throughout
the project. However, specialized activities like concreting and pile driving required a separate specialized
crew. Also, due to site space restrictions and safety, numerous types of work cannot occur simultaneously,
as such resources must remain idle until their respective activity is ready to be conducted.
Three sub-models, shown in Figure 5, were created to complement the functions of the main model,
but at offsite locations. They include the following: (1) concrete for beam; (2) concrete for deck; (3) fetching
piles.
Figure 5: Simulation sub-models.
Furthermore, three other sub-models shown in Figure 6 were created to mimic weather complications
and equipment breakdown. They include the following: (1) bad weather; (2) crane breakdown; (3)
jackhammer breakdown.
Figure 6: Other simulation sub-models.
CONCRETE FOR BEAM
Create Concrete
Trucks
Load
Truck
Concrete Trucks
Counter
Return to Plant
Create Concrete
Trucks
Load
Truck 1
CONCRETE FOR DECK
Concrete Trucks
Counter 1
Return to Plant 1
FETCHING PILES
Fetch Piles
Offsite
Fetching
Valve
Fetching &
Return
Preempt
Crane 1
Destroy6
No. of Deliveries
Release Crane
Offload Piles
Create Bad
Weather
Bad Weather
Probability
Preempt
Concrete Plant
BAD WEATHER
Delay
Destroy3
No. of Weather
Delays
Release
Concrete Plot
0.3
0.7
Create Crane
Breakdown
Preempt
Crane
Repair
Crane
Destroy2
No. of Crane
Breakdowns
Release
Repaired Crane
CRANE BREAKDOWN
Create Minor
Breakdown
Minor
Probability
Execute2
Destroy4
No. of
Jackhammer
Breakdowns
Release Repaired Jackhammer
Repair
Jackhammer
Execute1
Major Probability
Destroy5
Create Major
Breakdown
JACKHAMMER BREAKDOWN
0.1
0.9
0.8
0.2

Ebrahim, Singh, Li, and Ji
4.3 Results
The Simphony.NET model was run following the sequencing of actual events as recorded by project
management. The equipment and labor resources were allocated to each activity based on the contractor’s
proposal. The activity time was specified in the model with reference to actual performance, and not the
proposed schedule. Furthermore, different breakdowns and uncertainties were introduced to determine how
the project cycle was affected. The results acquired are summarized in Table 4.
Table 4: Simphony.NET simulation results with uncertainties.
Scenario
(Cumulative)
Production Rate
Total Production Time
Comments
Ideal Conditions
0.52 m/day
193.38 days
All tasks can be conducted
without interruption
Bad Weather
0.51 m/day
195.38 days
30% chance that there is bad
weather every 10 days
Crane Breakdown
0.47 m/day
215.00 days
Every 5 days the crane breaks
down and needs repair
Jackhammer
Breakdown
0.43 m/day
233.46 days
Jackhammer experiences
minor and major breakdowns
The simulated result for the ‘Ideal Conditions’ scenario was similar to actual construction performance
in Kuwait. As shown in Table 4, whenever uncertainty regarding weather conditions or equipment
breakdown was introduced in the model, the subsequent delays experienced caused decreases in production
rate from 0.52 m/day to 0.43 m/day and increases in total production time from 193.38 days to 233.48 days.
These uncertain events are commonly seen in construction projects. However, they may not be easily
incorporated into a bidding schedule even though they are certainly realistic. To mitigate these
uncertainties, modifications of available resources can be conducted to offset the effects of delays. As such,
resources in the model were tweaked to offer viable solutions whilst being kept within a reasonable project
time frame. The results acquired are summarized in Table 5.
Table 5: Simphony.NET simulation results with all uncertainties and resource modification.
Scenario
(Cumulative)
Production Rate
Total Production Time
Comments
With All
Uncertainties
0.43 m/day
233.46 days
All breakdowns and bad
weather
Additional
Concrete Trucks
0.47 m/day
211.21 days
Trucks increases from 2 to 8
trucks
Additional Jack
Hammers
0.52 m/day
191.46 days
Jackhammers increased from
1 to 3
Additional
General Labour
0.59 m/day
170.00 days
General labor crew increased
from 1 to 2
Additional
Concrete Crew
0.60 m/day
166.71 days
Concrete crew increased from
1 to 2
Additional
Concrete Pump
0.62 m/day
161.71 days
Concrete pump increased
from 1 to 2
Table 5 shows enhanced production due to improved resources. With additional concrete trucks, jack
hammers, general labor and concrete crews, and an additional concrete pump, completion of the berth can
be reduced from 233.46 days to 161.71 days, which is similar to the project schedule bid by the contractor.
Consequently, the production rate increased from 0.43m/day to 0.62 m/day.

Ebrahim, Singh, Li, and Ji
The total project cost was $773,422 for the maintenance of 1 berth ($17,633,191.329 for 7 berths)
including the delays with no additional equipment or labor. Additional equipment and labor (including crew
and engineers) will increase the cost to $971,872 - $1,368,772. An example of control can be observed
when the jackhammer experiences a major breakdown, thereby requiring 7 days for repair/replacement. In
such an event, project managers can determine from the simulation model that having other jackhammers
working simultaneously can reduce the length of delay caused by a 7-day absence of the broken
jackhammer. Therefore, the downtime and resources are being controlled to mitigate against project
schedule growth. Another example of control relates to increased placement rate of concrete with additional
pumps and trucks, thereby saving on the entire construction schedule. In the end, project managers would
have to make the final decision to allow the operation of additional resources. Depending on the sensitivity
of port operations during construction, it may be more feasible to bolster project finances to save on time.
As in all ports, Doha Port’s financial sustainability revolves around the number of vessels docking and the
amount of cargo being processed. In essence, the greater the downtime of operations, the greater the loss
of profits. Project managers must carefully weigh the scenario of completing the project faster, with
additional resources, versus completing the project slower, with lesser resources. The former allows for a
quicker turnaround time of port operations, thereby leading to improved business and profits, however with
the cost of extra equipment. On the other hand, the latter allows for savings in construction costs, but with
a slower turnaround time, penalty fees associated with delayed ship docking and loss in business and profits.
5 CONCLUSION
This paper developed a simulation model to mimic the berth maintenance process at Doha Port, Kuwait.
The model can be used to compute minimum maintenance project duration by considering limited resources
and uncertainties (e.g., equipment breakdowns and bad weather) associated with the maintenance process.
The simulated production rate has been shown to be similar to the actual construction performance in
Kuwait, which demonstrates the capability of the model to capture main features of berth maintenance.
Uncertainty in the model tends to create project delays, thereby negatively affecting production rate and
total production time. As such, adjusting resources can mitigate against possessing an unfavorable project
time frame. The results of the simulation model showed that by delimiting resources and utilizing them
effectively, improved construction performance can be accomplished by a contractor. Furthermore, by
using the simulation model, a contractor or an owner, or a project manager can control and monitor the
process of the berth maintenance project to execute the work in a minimum time as this project is a
governmental project with a time limit.
In this presented case, to improve the accuracy of the model and make it more reliable, laborers/crews’
working shifts timing can be included to determine the status of each worker whether they are On/Off duty.
In addition, resource costs can be incorporated into the model to investigate the total cost of adopting extra
equipment. This can then be compared to the costs associated with delaying ships out at sea for determining
the strategy’s financial feasibility. Last but not least, the tradeoff between the costs associated with adding
additional resources, the saved operation downtimes and possible improvement in business and profits can
be further investigated by professionals or scholars who are interested in the logistics associated with berth
maintenance.
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AUTHOR BIOGRAPHIES
RUQAYAH ALSAYED EBRAHIM is a Master’s student in the department of Civil, Environmental and Infrastructure
Engineering, George Mason University. Ruqayah’s current concentration is construction engineering and management. Her e-mail
address is rebrahim@gmu.edu.
SHIVANAN SINGH is a Master’s student in the department of Civil, Environmental and Infrastructure Engineering, George
Mason University. Shivanan’s current concentration is construction engineering and management. His e-mail address is
ssingh83@gmu.edu.
YITONG LI is a Ph.D. candidate in the Department of Civil, Environmental & Infrastructure Engineering, George Mason
University. Yitong’s current research area fosuces on dynamic modeling of infrastructure restoration progress and construction
simulation input modeling. Her e-mail address is yli63@gmu.edu.
WENYING JI is an assistant professor in the Department of Civil, Environmental & Infrastructure Engineering, George Mason
University. Dr. Ji received his PhD in Construction Engineering and Management from the University of Alberta. Dr. Ji is an
interdisciplinary scholar focused on the integration of advanced data analytics and complex system modeling to enhance the overall
performance of infrastructure systems. His e-mail address is wji2@gmu.edu.