A Queueing System Study for Refueling Service at Shell Gasoline Station of Mahayag Essay Example

gas tanks. Three people in the gas station attend at the counter. The amount of customers wanting gasoline always depends on the part of the day, its either morning or afternoon. If all gasoline pumps are busy, a customer will wait in the one which has the shortest queue. But, if all pumps have a long waiting line, the arriving customer will refuse to wait and leave. The scope of this study regarding queueing system, arrival and service rate of customers has been gathered by using time and motion study. This arrival and service rate will be the basis for evaluating the waiting time of customer by using simulation process.

The simulation process is use 1000 simulated days to have close possible results generated by random numbers As the result of this study,

1) The arrival rate is 24 customers per hour,

2) The service rate is 47 customers per hour,

3) Utilization is 51 percent,

4) There are 8 customers wait in line at 36 seconds of waiting time, and

5) there are 9 customers served the system after 1. 8 minutes.

The system was servicing customers about 8. 16 hours only with 7. 84 hours as the idle time of the system. Therefore, base on the overall simulation (averaging the queues on 7 days) it reflects that there is no queue formed on the system with 51 % of utilization only.

Keywords:

Based on the Layout of the station, the pump’s 1 and 2 reflects the super premium gasoline, a kind of gasoline that use for tricycle, motor single, auto, and multicab. Most of the time this pumps are the cause of queue for other pumps like diesel, regular, and kerosene pump,

its because there are 3 or 4 customers arrive every minute, Therefore, the researcher conducts a study on the super premium gasoline pumps to determine the waiting tim the.

However, shell management confused if the existing worker of three pump attendants and one Casher are really the best requirements of the station or the optimal number of worker of the station. The main goal of the researcher is to evaluate a result of Monday to Sunday simulation to give an overview or idea to shell management about waiting line of customer. The aim of this research is to study the Queueing System for Refuelling Service at Shell Gasoline Station, Mahayag, Isabel Leyte. Specifically, this study attempts to:

1) Determine the arrival rate of the customers in the system.

2) Determine the service rate of the customers in the system.

3) Evaluate the behavioural characteristics of waiting line through determining the following:

3. 1 The average utilization of the system.

3.2 The average number of customers waiting in line.

3. 3 The average number of customers in the system.

3. 4 The average waiting time in line of customers.

3. 5 The average time in the system.

This will also help the manager to know the waiting time of the customer and use it as baseline in making a decision. Through this study customers will know how much time they have to wait in the system for a service specifically on getting super premium gasoline for there vehicles like tricycle, motor single, auto and multicab. This study help the researcher to identify the systems if line are form or not. Through this study the researcher could learn more about the waiting time of the customers. This

could motivate and challenge the researcher to study and analyze the average arrival and service rate of the customers.

In recent years, queuing theory probably has been applied most to businessindustrial internal service systems, where the customers receiving service are internal to the organization. Examples include materials-handling systems, where materials handling units move loads; maintenance systems, where maintenance crews repair machines; and inspection stations, where quality control inspector inspect items. Employee facilities and departments servicing employees also fit into this category. In addition, machines can be viewed as servers whose customers are the jobs being processed. A related example of great importance is a computer facility, where the computer is viewed as the server.

There is now growing recognition that queuing theory also is applicable to social service systems. For example, a judicial system is a queuing network, where the courts are service facilities, the judges are the servers, and the cases waiting to be tried are the customers. A legislative system is a similar queuing network, where the customers are the bills waiting to be processed. Various health-care systems also are queuing systems. Example a hospital emergency room, but you can also view ambulances, x-ray machines, and hospital beds as servers in their own queuing systems. Similarly, families waiting for low-and moderate-income housing, or other social services, can be viewed as customers in a queuing system.

Queuing theory is used to assess characteristics of the queue such as average number of people in line, average time that person waits, average utilisation of the server system, average time that a person is in the system, etc. with this information, managerial decisions can be made regarding how many servers

to schedule, when to schedule servers based on arrival rates, how to layout the queue system, process improvements and training to reduce service time, etc. When a series of services is performed in sequence where the output rate of one becomes the input rate of the next, they can no longer use the simple formulas. Queuing problems that seem simple on first impression turn out to be extremely difficult or impossible to solve.

The technique best suited to solving this type of problem is computer simulation. Simulation originated during the Chinese war games called weich’i, way back 5,000 years ago and continues through 1780, when the Prussians used the games to help train their army. Since then, all major military powers have used war games to test out military strategies under simulated environments. From military to operational gaming, a new concept, Monte Carlo simulation, was developed as a quantitative technique by the great mathematician John von Neumann during World War II. Monte Carlo simulation is experimentation on chance or probabilistic elements by means of random sampling.

Today, thousand of business, government and service organizations develop simulation models to assist in making decisions concerning inventory control, maintenance scheduling, plant lay-out, investments, sales forecasting and even labor hiring decisions. A simulation is an imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviors of a selected physical or abstract system. Simulation has become an increasingly important quantitative technique for solving problems in operation. Surveys have shown simulation to be one of the techniques most widely applied to real world problems. Evidence of this popularity is the

number of specialized simulation languages that have been developed by computer industry and academia to deal with complex problems areas.

The popularity of simulation is due in large part of the flexibility it allows in analyzing systems, compared with more confining analytical techniques. In other words, the problem does not have to fit the model; the simulation model can be constructed to fit the problem. Simulation is popular also because it is an excellent experimental technique, enabling systems and problems to be tested within a laboratory setting. However, in spite of its versatility, simulation has limitations and must be used with caution. One limitation is that simulation models are typically unstructured and must be developed for a system or problem that is also unstructured.

As a result, developing simulation models often requires a certain amount of imagination and intuitiveness that is not required by some of the straight forward solution techniques. There are several study of the problem about queueing or waiting line of customers. Like the China Bank. A bank wants to know how many customers are waiting for a drive-in teller, how long they have to wait, the utilization of the teller, and what the service rate would have to be so that 95 percent of the time there will not be more than three cars in the system at any time. China Bank is considering opening a drive-through window for customer service. Management estimates that customers will arrive at the rate of 15 per hour. The teller who will staff the window car service customers at the rate of one every three minutes.

The bank assuming Poisson arrivals and exponential service, find. Using formulas, they

can solve and know the results of each problem. Which utilization is 75%, average number in the waiting line is 2. 25 customers, the average number in the system is 3 customers, the average waiting time in line is 0. 15, or 9 minutes, and the average waiting time in the system is 0. 2 hour, or 12 minutes. The Valdese Machine Tool Company operates an inventory warehouse which issues raw material to supervisors. Currently, two persons are assigned to operate the system. The number of supervisors who use the warehouse is 10.

Beth James a senior member of the operations department believes that the line of supervisors which develops at the warehouse every day is inefficient. She realizes from her knowledge of queuing models that the number of supervisors is probably too small to be represented by an infinite population; she also has come to the conclusion that the distribution of the number of arrivals per unit of time is not Poisson; neither is the distribution of service times exponential. She realizes that with a multi-channel queuing situation which doesn’t meet those conditions, finding an analytical model is almost impossible. Therefore she decides to simulate the system.

* In this study of queueing system for refuelling service at Isabel shell gasoline station is the researcher will show how to simulate part of an operations management system by building a mathematical model that comes as close as possible to represent the validity of the system especially on minimizing queue length.

* Operation Management for Competitive Advantages, Chase, Aquilano, Jacobs; 243.

RESEARCH DESIGN:

The design used in this study is the descriptive method for the reason that it involves the analysis,

interpretation and evaluation of the data gathered.Descriptive Method aim to describe the nature of situation as it is exist at the time of the study and to explore the causes.

RESEARCH INSTRUMENT:

In operations system queuing theory is an important part and a valuable tool for the operations manager. A waiting-line model is useful in service area. Analysis of queues in terms of waiting-line length, average waiting time, and other factors helps us to understand service systems. On this study, the researcher is used Time Study Sheet Form for gathering data of Arrival and Service rate of customers. The Form was designed to generate the simulation model.

On this model random numbers are generated to treat the variables simultaneously. Where it uses 1000 samples or 1000 simulated days to have an accurate results. With these numbers of samples, it cannot be handled by manual calculation alone. It requires a program or application such as Microsoft Excel to have an easy and efficient calculation.

RESEARCH VALIDATION:

This study is authorized by the manager of shell station Engr. Romelo Mappala.

RESEARCH PROCEDURE:

In every study, it is important to establish a research procedure in a scientific way. In which every factors should be considered on a study like, the important data to be gathered and how to treat that data reflecting its primary objectives.

Data gathering procedure is first and foremost before applying the simulation model. These are the data required for the simulation model:  Arrival rate These are the expected number of customers that arrive each period. These data are needed to get the total arrival of customers per hour and until 8 hours. Service rate These are the capacity of a server measured

in number of units that can be processed over a given time period. These data are needed for the computation of customers time spend in a system for a service that will be used for the simulation model. The next procedure is to treat the data with specified tool being used on this study which is the Simulation.

The following are the procedures used in such simulation:

1. Setting up a probability distribution for important variables. And building a cumulative probability distribution for these variables.

2. Establishing an interval of random numbers for each variable.

Simulation Result of Monday The table 3 shows that in every hour there are 25 customers arrive at the station to refuel their vehicles. The service rate is 59 customers served the system in every hour. Therefore, there is no queues form on Monday because the arrival rate is smaller than to service rate. The system was servicing customers only about 6. 88 hours out of 16 hour operation. The remaining 9. 12 hours is the idle time of the system. This idle time is utilized to other systems. There is 1 customer wait in line at 0. 01 or 36 seconds of waiting line. And there is only 1 customer served the system after 0. 04 hour or 2. 4 minutes. The table shows the results of the simulation based on 1000 simulated days.

This day reflects the busy day because the system was servicing customers over 100 percent of the time, which is 17. 44 hour. There are 6 customer leave in line at 11 minutes. And there are 4 customers not served the system after 9 minutes. The table shows the results

of the simulation based on 1000 simulated days. Table 9: Wednesday Simulation Results Averages Arrival rate Service rate Utilization Number of customers waitin.