Shipping Service Optimization in Pars
Petrochemical Port-Assalouyeh with Using
Queuing Theory and Simulation
Mohsen Parhizgar (Author)
Department of Management
Science and research branch, Islamic Azad University
Shiraz, Fars, Iran
Parhizgar2000@gmail.com
Dr. Mohammad Ali Soukhakian
Department of Management
Science and research branch, Islamic Azad University
Shiraz, Fars, Iran
Dr. Javad Gerami
Department of Management
Science and research branch, Islamic Azad University
Shiraz, Fars, Iran
Abstract
Nowadays, integrity of supply chain processes (suppliers, producers, terminals and etc.) is one of the most important ways of
improving supply chain processes. Meanwhile petrochemical industry due to production and combination of different
materials need a very complex logistics processes.
Therefore creating a balance between different production and service units in the process of production, storage and
distribution is of high importance. Ports with various capacities and various vessel berthing and un-berthing patterns
increase the complexity of petrochemical supply chain and logistics process and subsequently will affect severely the balance
of terminals and vessel.
In this article, ships long standing time, queuing length, ship and terminal costs have been studied in Pars Petrochemical
Terminal which is located in Pars Special Economic Energy Zone. For studying current system and designing new improved
system, with regards to logistics container management process compliancy, a simulation program and queuing theory are
used for this propose. At the end by designing and implementing of model in ARENA software, the results of implemented
simulation by study of alternatives have been analyzed. Finally, current optimization leads to increase efficiency in Pars
Petrochemical Port and petrochemical industries supply chain cost in Assalouyeh.
Keywords: Queuing theory, Simulation, shipping service, transportation, assalouyeh pars petrochemical port
1.
Introduction
The organizations must improve the effectiveness of its quality management system
continuously, through the use of quality Policy, quality objectives, audit results, data
analysis and corrective and preventive actions and also management review [3]. Xu
Jing Jing (2012) in his article entitled “Queuing Models to Improve Port Terminal
Handling Service” writes: The cost covering handling services is composed by fees
covered handling mechanical services and unpaid waiting expenses refer to the
board-to-bank loading and unloading [21].
1.1 Queuing theory: In a simple definition, queuing theory is the science of analysis
and management of the waiting lines or the same Queue [13]. Queuing systems are
ubiquitous in the real world and can play an important role in the design of the
systems, including waiting time and planning for services [7].
1.2 Simulation: The simulation process is applied to the modeling of a process, a
phenomenon, or the actual system [12]. Today, for modeling the production systems,
a set of methods have been developed one of which is simulation. The simulation is a
method that defines, a case which has the probable condition and loss of confidence
in variables and parameters, and then gives a model to determine the characters of the
time variable [15]. the simulation discuss about the models of the complicated
systems which produce acceptable output and can lead to acceptable solutions [9].
1.3 The relevant variables: The following variables can be examined, for the
measurement and analysis of the port data [13].
1.
Average arrival rate of the ships to the port
2.
Average arrival rate of export cargo to the port.
3.
Average rate of servicing to the ships.
4.
Average of the ships stop in the queue (Lq)
5.
Average of the ships stop to receive the services.
6.
Average of waiting time in the queue.
7.
Average of waiting time to receive services.
8.
Length of system (Average number of customers in the systems)
9.
U: Equipment Efficiency Coefficient (average number of hours for the
availability of the equipment)
10. The total cost of the system in this paper consists of four parts, which are: The
costs of the port personnel, ship personnel cost, the ship waiting cost and nonworking costs of port equipment.
Thesis of Mehrdad Najafi (2010), has predicated the number of jetties and gantry
cranes to the reform of the existing container terminal at the port Martyr Rajai for 10
years [14]. Another study performed in 2009, is the thesis of Karim Zandi, entitled
“Optimization of how serving the hospital patients, Using queuing theory and
simulation” in the Shiraz Martyr Motahari hospital. In this paper, on attempt has been
made to use the simulation and its data for the optimization in order to reduce the
waiting time [11]. Also, Saeed khanaze dari*; Omid Bagheri ; Ali. Bayat; Mohammad
hossein Taban and Babak, Salek Mehdi (2011), in their article, have showed: How
the simulation optimizes, the transport way of the dash board to the assembly line by
the fork lift and also how much is the amount of the successes as the result of the
choosing the proper transport way In Pars – Khodro company [5].
Majid Azadi khah* ; Mehdi Jamalzadeh ; Omid heidary; Hamed Sohrabi and Sajad
Shokhouhyar (2011) in their article, have reached the optimal number of paint color
buffer in the Pars – Khodro company with using simulation ARENA software [2].
Amir Azaroun, and Farhad kianfar (2001) in their article, have studied using on
algorithm to select the shortest path in networks of queue in steady state [1].
A. Nabi. Rahimi Fard, in his master thesis, with using queuing theory, has examined
Tehran traffic and has told some suggestions to optimize it [6]. Chen, Laih (2012) in
his article, has studied the connection between the pricing of the terminal services
and the queue length regarding the queue cost for the ship owners and also for the
good owners [16].Senay, Solak; John , paul, B. Clarke, and Ellis L. Johnson (2012) In
their paper with the simulation of the entire airport, could increase the airport
efficiency and could help the managers to make decisions about the development of
old terminal or the construction of the new one [20].David. Lovell, in his article
shows a structure to compute continuum approximation of a queuing system, such as
airports, high ways and … in a national system [17].Also, Joseph I. Daniel (2008) in
his article, using the simulation has studied the delays in the major international
airline companies in 27 different airports. He could optimize the selling price
regarding the delay, with the help of simulation and its output [18].
2. Definition of the Case
Due to the increasing growth in containerized imports and exports, in the west of asia
and Iran, this type of transport is very important after extensive study, a conceptual
model of the terminal was built, to make clear the aim and limit of the subject.
This model is visible in Figure 1.
Figure 1 (conceptual model of the case)
According to Figure 1, when the ships arrive in the assalouyeh off shore area, they
stop in the dock in coordination with Iranian PMO. Then the jetties become empty
and ship is put beside of the berth. After unloading and loading completion, the ship
will leave the port. On the other hand, the trucks move empty container to the
containerized storage and the full container to the port yard.
The process during 24 hours a day, two channels and one step service system, queue
as FIFO, none group inputs and infinitely waiting lines are the assumptions.
3. Research methodology
To use this model, The information about the arrival and departure of the ships and
also other data were collected from the first quarter of the year 2011.then by using
data analysis & EXCEL software, The required parameters were estimated by using
this information and their distribution function and also with the help of input
analyzer software, The required parameters of the queuing theory were studied.
After ensuring about the accuracy of the generated model, by the use of Arena
simulator software, Various Scenarios were defined and also each of them were
studied.
3.1 Types of Scenarios:
Scenarios with 2 jetties:
1: The amount of arrival of the trucks and ships in normal condition
2: 4 fold increase in the rate of the trucks arrival and 5 fold increase in the rate of the ships arrival.
3: 2 fold increase in the rate of the trucks arrival and 5 fold increase in the rate of the ships arrival.
4: 5 fold increase in the rate of the trucks arrival and 5 fold increase in the rate of the ships arrival.
5: 7fold increase in the rate of the tucks arrival and 5 fold increase in the rate of the ships arrival.
6: 8 fold increase in the rate of the trucks arrival and 5 fold increase in the rate of the ships arrival.
7: 9fold increase in the rate of the trucks arrival and 5 fold increase in the rate of the ships arrival.
8: 6 fold increase in the rate of the trucks arrival and 5 fold increase in the rate of the ships arrival.
9: 3 fold increase in the rate of the trucks arrival and 5 fold increase in the rate of the ships arrival.
10: 10 fold increase in the rate of the trucks arrival and 5 increase in the rate of the ships arrival.
Scenarios with 3 jetties:
11: the amount of arrival of the trucks and the ships in the normal condition.
12: 4 fold increase in the trucks arrival rate and 5 fold increase in the ships arrival rate.
13: 2fold increase in the trucks arrival rate and 5 fold increase in the ships arrival rate.
14: 6fold increase in the trucks arrival rate and 5 fold increase in the ships arrival rate.
15: 3fold increase in the trucks arrival rate and 5 fold increase in the ships arrival rate.
16: 5fold increase in the trucks arrival rate and 5fold increase in the ships arrival rate.
17: 7fold increase in the trucks arrival rate and 5 fold increase in the ships arrival rate.
18: 8fold increase in the trucks arrival rate and 5 fold increase in the ships arrival rate.
19: 9fold increase in the trucks arrival rate and 5 fold increase in the ships arrival rate.
20: 10fold increase in the trucks arrival rate and 5 fold increase in the ships arrival rate.
3.2 Modeling and Simulation in the Arena software
In the present case, the entry and output rate at random times, and their impact on the
queue length and also the amount of effectiveness and the total cost, make it
completely dynamic, so that its effective quantitative analysis only happens with the
help of the simulation. For this reason, the creation of a simulation model to evaluate
different solutions for optimizing the number of berth and the amount of the entrance
and exit, and the analysis of their effects are inevitable. With the purpose of the
modeling the case in this paper, the ARENA software has been used. ARENA is one
of the software, which is used in the simulation of the discrete systems and uses the
SIMAN language [19]. There are the following main sections in the designed model
3.2.1 The section of ships arrival and departure
Logic - Arrival of Ships
Time and
conditions for
Entrance
A rrival of ships
0
Awaiting a
position in the
terminal port
Organize
arriving ships
Mark E ntrance
Time
Go to the pier that
is empty
0
N R( Terminal Port) <2 .AN D . ( CalH our( TN OW ) >0 .AN D . CalH our( TN OW ) <24)
Figure 2: The designed model In ARENA.
3.2.2 The section determining the way of servicing to the ships
Figure 3: the designed model in ARENA
Figure 4: the designed model in ARENA
3.2.3 Section of the trucks arrival and departure
Logic - Moving Trucks
Moving trucks
Entry
U ploading
C ontainer
0
0
Route 2
Departure
D ispose 1
0
Figure 5: the designed model in ARENA
3.3 The data collection
According to the designed model, the required data were collected from the process
of the arrival an exit of the ships and the trucks. These data were collected for a
period of 90 days. For each of the required data, the appropriate distribution function
is used with input analyzer software– ARENA software. The statistical analysis was
performed on data, in the time between the ship arrival and also the trucks.
The distribution function chosen for the time between the arrival of the ships into the
anchorage area is WEIB (30.9, 1.34) and the distribution of the time of the servicing
to the ships is TRIA (0,8.75, 21) and also the distribution function of the total
expected time in the system is LOGN (19.7, 28.5)
3.4 Model implementation and results:
According to preliminary estimates, the cost is calculated as follows:
The cost of a lost opportunity for ships (IRR/hr) A=27000000 (1)
The cost of a lost opportunity for
B = 63200000
(2)
Ship personnel cost (IRR/hr)
C = 1750000
(3)
Terminal personnel cost (IRR / hr)
D=1250000
(4)
The total lost opportunity cost for the ship = A * Lq
(5)
The total lost opportunity cost for the terminal = B * U
(6)
The lost cost of the system = C + D + (B + U) + (A + Lq)
(7)
One of the Arena’s simulated model outputs is the coefficient of efficiency and the
average waiting time in the queue. Based on the results of the designed model and the
results of the implementation of the simulation model, the amount of the total cost
different scenarios, is shown in Figure 6.
Total Cost (IRR/hr)
700,000,000
600,000,000
500,000,000
400,000,000
300,000,000
200,000,000
100,000,000
18 11 3 1 19 8 10 15 7 14 20 17 16 4 5 12 6 2 9 13
Scenario No.
Figure 6 Comparison of the system total cost for different scenarios.
The results shown in Figure 6, tell that, the total cost in scenario 18 with 3 jetties has
reached the minimum cost, so the total cost has declined 17%.
4. Conclusion
In this paper, the process of loading and unloading of the ships, in the South Pars
petrochemical terminal has been studied. The aim of this study is to determine the
maximum efficiency of terminal equipment’s and also the average of the ships
waiting time in scenarios in order to minimized the cost for this purpose, the
simulation has been used to study this system regarding the complexities of the
terminal processes. Finally, after designing and the implantation in the ARENA, the
results of the simulation have been analyzed with the studies of the improve
alternatives.
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