Application of Motorcycle Service Queuing Models and Simulations in Aur Kuning
Keywords:
model, simulation, motorcycleAbstract
Queue simulation is important for analyzing and optimizing service processes in various fields such as bank counter queue services, gas station vehicle queues, vehicle queues in parking lots including motorcycle repair shop service queues. In this case, the research objective is to propose the application of queuing simulation to identify and improve service efficiency in motorcycle maintenance workshops. With the right simulation model, we can analyze various aspects of motorcycle workshop operations, from customer waiting time to efficient resource utilization. This research investigates the use of queuing simulation in motorcycle repair shops, focusing on identifying and addressing potential bottlenecks in the service process. By collecting sample data from 30 incoming vehicles split over three days, we created a representative simulation model for a motorcycle repair shop. With the results of this simulation can help workshop owners in overcoming or getting a solution to the problem of motorcycle queues, this case study will contribute so that there is a change to the effectiveness of motorcycle workshops in the queue by developing the results of this article, keyword model, simulation, queue, motorcycle.
References
Application of Motorcycle Service Queuing Models and Simulations in Aur Kuning
Gerli Wira Yudha 1 ,Latifa Humaira2 ,Liza Efriyanti3 ,Hafiz An Naufal4 ,Gina Hadai Yani Fitri5
,Fadhilah Yuliarizman6
*Corespondence :
Email : [email protected] Abstract
Queue simulation is important for analyzing and optimizing service processes in various fields such as bank counter queue services, gas station vehicle queues, vehicle queues in parking lots including motorcycle repair shop service queues. In this case, the research objective is to propose the application of queuing simulation to identify and improve service efficiency in motorcycle maintenance workshops. With the right simulation model, we can analyze various aspects of motorcycle workshop operations, from customer waiting time to efficient resource utilization. This research investigates the use of queuing simulation in motorcycle repair shops, focusing on identifying and addressing potential bottlenecks in the service process. By collecting sample data from 30 incoming vehicles split over three days, we created a representative simulation model for a motorcycle repair shop. With the results of this simulation can help workshop owners in overcoming or getting a solution to the problem of motorcycle queues, this case study will contribute so that there is a change to the effectiveness of motorcycle workshops in the queue by developing the results of this article, keyword model, simulation, queue, motorcycle.
Abstrak
Simulasi antrian penting untuk menganalisis dan mengoptimalkan proses pelayanan di berbagai bidang seperti layanan antrian loket bank, antrian kendaraan SPBU, antrian kendaraan di tempat parkir termasuk antrian layanan bengkel sepeda motor. Dalam hal ini tujuan penelitian adalah mengusulkan penerapan simulasi antrian untuk mengidentifikasi dan meningkatkan efisiensi pelayanan di bengkel perawatan motor. Dengan model simulasi yang tepat, kita dapat menganalisis berbagai aspek operasional bengkel sepeda motor, mulai dari waktu tunggu pelanggan hingga pemanfaatan sumber daya yang efisien. Penelitian ini menyelidiki penggunaan simulasi antrian di bengkel sepeda motor, dengan fokus untuk mengidentifikasi dan mengatasi potensi kemacetan dalam proses pelayanan. Dengan mengumpulkan data sampel dari kendaraan yang masuk sebanyak 30 yang kami bagi dalam tiga hari, kami membuat model simulasi yang representatif untuk bengkel sepeda motor. Dengan Hasil simulasi ini dapat membantu pemilik bengkel dalam mengatasi atau mendapatkan solusi mengenai permasalahan akan antrian sepeda motor, studi kasus ini akan berkontribusi sehingga terdapat perubahan ke efektivisan bengkel sepeda motor dalam antrian dengan mengembangkan hasil dari artikel ini, kata kunci model, simulasi, antrian ,sepeda motor.
Authors Affiliation:
,2,3,4,5,6 Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi, Indonesia
Article History :
Submission :
Revised :
Accepted :
Published
Keyword : model, simulation, motorcycle
Kata Kunci : model, simulasi, sepeda motor
INTRODUCTION (Tahoma 10, Bold, Line Spacing 1.15)
A good company is a company that always maintains its quality. Company quality can be measured in various ways. Service can be used as a measure of the quality of a company or agency. In the service process there will be a queue. If a company has a poor queuing system, it will cause fewer customers, a bad company image, reduced revenue, and others.
In today's world, where mobility has become an integral part of daily life, motorcycle repair services play an important role in ensuring that your vehicle continues to operate in optimal condition. With the increase in electric vehicles, especially motorcycles, motorcycle repair shops are at the forefront of ensuring the smooth operation of vehicles and the safety of road users. One of the biggest challenges that motorcycle repair shops face is managing customer queues. Long waiting times can lead to customer dissatisfaction and affect the operational efficiency of the workplace.
Motorcycle workshop services are one of the companies in the field of repair services that are in great demand today, especially for motorized vehicle users who are engaged in maintaining their motorized vehicles. The motorcycle repair service business is now increasingly competing due to the wider need for motorcycle repair services. Motorcycle repair services for various brands often face challenges in the form of customer complaints regarding the average duration of service by mechanics. This can lead to long queues and is a major concern in maintaining customer satisfaction.
To maintain customer satisfaction, motorcycle service workshops must ensure that they can provide the best service to customers, as well as timely and best service results, the convenience of customers bringing motorbikes to the workshop to picking up motorbikes. According to Saraswati & Hendikawati (in Yanti, Rila Fitri. 2023: 2). Fast service really helps retain customers which of course increases workshop profits in the long run. As stated by Faizal (2005), 'Queues that are very long and too long are of course detrimental to those who need service, because a lot of time is wasted while waiting. In addition, the service provider indirectly also suffers losses, because it will reduce work efficiency, little profit, and will even create an unfavorable image to its customers. One of the reasons that causes long queues in this workshop is the queue that occurs when the customer's motorbike is being serviced. The influx of many customers causes a long queue time and causes the queuing time to be long.
Simulation comes from the word simulate which means to pretend or do as if. As a teaching method, simulation can be interpreted as a way of presenting learning experiences by using artificial situations to understand certain concepts, principles, or skills. Simulation has an important role in modeling and in analyzing activities, because it allows quantitative estimates and can affect the design process on system performance (Mojca Indihar Stemberger, 2001). Simulation is a useful tool for analyzing complex systems where we cannot use standard methods in operational research (Tomas Domonkos, 2010). Simulation is a computer program (soware) that functions to mimic the behavior of certain real systems (reality). The purpose of simulation is training, study of system behavior, entertainment or games. Modeling and simulation is one of the tools often used by management in studying or analyzing the work behavior of a system or process.
Service in the Big Indonesian Dictionary is an activity that helps prepare or take care of what others will need. Service is also an activity or sequence of activities that are invisible or invisible that occur with the relationship between customers and business owners or workers in a company provided by the company in helping or serving customers and solving problems from customer desires.
Queuing is an activity of waiting for your turn to be served. Queuing activities arise because the number of service facilities is less than the number of people who need the service concerned. People are forced to queue to fulfill their needs. Queuing is an activity where several people line up or wait at a service facility then are served, and finally leave the facility after being served to fulfill something they want. Queues occur when a group of people, components, or machines must wait in a certain order to get service. This condition arises when the available service capacity is unable to meet existing service needs. Queuing occurs when the processing time is greater than the time between arrivals. Queuing is also an activity that we always encounter every day in everyday life, the queuing system includes customers who come with a fixed and varied speed in order to get the service needed.
Service is an action or decision to be offered by a party to another party who needs the service, which is basically invisible and does not result in ownership and its production may or may not be associated with a physical product. Queuing systems include customers who come at a constant or variable rate to get service at a service facility. If an incoming customer can enter the service facility, the customer can be served immediately. (Antono, in Aji and boadrastuti).
This article discusses the model and simulation of queues at one of the motorcycle services. Here we explain how the basic concepts of queuing and the benefits of using simulation models in improving the performance of motorcycle vehicle inspection and maintenance services. First we need to know the basic concepts of queuing so that we can minimize the risk of errors that will occur when we process data obtained directly or real-time using the queue simulation method.
The results of this article have the aim of finding alternative solutions to queuing problems at motorcycle workshops, and it is hoped that readers will understand the importance of modeling and simulating motorcycle maintenance queues. With this, it can also improve service management in motorcycle maintenance queues so that it has a significant impact so that customers get satisfaction in visiting this motorcycle workshop.
METHODS
This research was conducted at one of the motorcycle service workshops located in Aur Kuning. The object of the research is workshop customers who queue to get motorcycle service services, which greatly affects customer satisfaction and the quality of service provided by the workshop.
Data collection is done by making observations to the workshop in groups. The data we take or obtain is the time the customer is served, the service time of the service and the time the customer is served, and the equipment used is a cellphone to calculate the length of the queue and documentation.
The subjects observed were customers of the workshop who were queuing for motorcycle services. The data sample taken in this study amounted to 30 motorcycles. Then the data is processed manually using a queue simulation model that is done in groups.
Limitations and assumptions in this study:
The data obtained is motorcycle maintenance queue data
Observations were made for 3 days due to time constraints
Assumption that no customers cancel the queue
RESULT AND DISCUSSION
RESULT
The queue data in table 1.1 is obtained on Friday, May 17, 2024 based on recording the time distance of the arrival of the first and second motorcycles, then the second and third motorcycles and so on until the arrival of the 11th motorcycle.
Table 1.1 Queuing data (Friday)
Motor ke Plate Arrival Time Arrival Distance
BA 5489 LA 14.13 - 14.35 13 menit
BA 2348 ND 14.18 - 14.25 5 menit
BM 3125 DAB 14.24 - 14.39 6 menit
BA 3628 LG 14.30 - 15.00 6 menit
BA 4781 LP 14.35 - 15.55 5 menit
BA 4765 LI 14.39 – 16.16 4 menit
BA 4469 CA 14.40 - 14.54 1 menit
BA 2267 EL 14.41 - 15.19 1 menit
D 3397 TB 14.45 - 15.03 4 menit
BA 3447 XA 14.50- 15.06 5 menit
BA 3391 LI 15.06-15.38 16 menit
By summing up the total arrival distance on Friday, the result is 66 which is then divided by the number of samples, which results in 6 which is the formula for finding the IAT value by entering into the formula -x ln (Ri), namely -6 ln (Ri).
The queue data in table 1.2 is obtained on Saturday, May 18, 2024 based on recording the time distance of the arrival of the first and second motorbikes, then the second and third motorbikes and so on until the arrival of the 10th motorbike.
Table 1.2 Queuing data (Saturday)
Motor ke Plate Arrival Time Arrival Distance
BA 2869 LO 13.30-16.03 30
BA 3128 XC 13.36-15.16 6
BA 2874 LE 13.38-14.30 2
BA 6402 BJ 14.12-15.05 25
BA 3700 LE 14.20-15.34 8
BA 4778 LAB 14.46-15.43 26
BA 3212 LU 15.07-15.26 23
BA 6504 TZ 15.17-16.41 10
BA 6147 AAT 15.31-15.39 19
BA 2959 XA 15.42-16.46 18
By summing up the total arrival distance on Friday, the result is 167 which is then divided by the number of samples, which results in 16.7 which is the formula for finding the IAT value by entering into the formula -x ln (Ri), namely -16.7 ln (Ri).
The queue data in table 1.3 is obtained on Thursday, May 23, 2024 based on recording the time distance of the arrival of the first and second motorbikes, then the second and third motorbikes and so on until the arrival of the 9th motorbike.
Table 1.3 Queuing data (Thursday)
Motor ke Plate Arrival Time Arrival Distance
Ba 3081 lw 11.05 - 12.24 5
Ba 2901 HB 11.07- 12.20 2
Ba 6002 AV 11.15- 12.45 8
Ba 2564 LA 11.27- 11.49 12
Ba 5505 LN 11.30- 12.32 3
Ba 3248 TU 11.32-12.45 2
Ba 2459 LJ 11.35- 12.25 3
Bm 4231 YN 11.50- 13.10 15
Ba 6026 NA 12.30- 13.45 40
By summing up the total arrival distance on Friday, the result is 90 which is then divided by the number of samples, which results in 10 which is the formula for finding the IAT value by entering into the formula -x ln (Ri), namely -10 ln (Ri).
Service data in table 2.1 is obtained on Friday, May 17, 2024 through the length of time waiting for the service time contained in the queue table, how to calculate it is by subtracting from the time the motorcycle arrives until the motorcycle leaves.
Table 2.1 Service duration data (Friday)
Motor ke Plate Duration of Service (minutes)
BA 5489 LA 22 menit
BA 2348 ND 7 menit
BM 3125 DAB 15 menit
BA 3628 LG 30 menit
BA 4781 LP 90 menit
BA 4765 LI 95 menit
BA 4469 CA 14 menit
BA 2267 EL 40 menit
D 3397 TB 18 menit
BA 3447 XA 16 menit
BA 3391 LI 27 menit
By summing up the total arrival distance on Friday, the result is 374 which is then divided by the number of samples, which results in 34 which is the formula for finding the IAT value by entering into the formula -x ln(Ri), namely -34 ln(Ri).
Service data in table 2.2 is obtained on Saturday, May 18, 2024 through the length of time waiting for the service time contained in the queue table, how to calculate it is by subtracting from the time the motorcycle arrives until the motorcycle leaves.
Table 2.2 Service duration data (Saturday)
Motor ke Plate Duration of Service (minutes)
BA 2869 LO 183 menit
BA 3128 XC 122 menit
BA 2874 LE 68 menit
BA 6402 BJ 57 menit
BA 3700 LE 74 menit
BA 4778 LAB 59 menit
BA 3212 LU 19 menit
BA 6504 TZ 78 menit
BA 6147 AAT 8 menit
BA 2959 XA 63 menit
By summing up the total arrival distance on Friday, the result is 731 which is then divided by the number of samples, which results in 73.1 which is the formula for finding the IAT value by entering into the formula -x ln(Ri), namely -73.1 ln(Ri).
This Service data was obtained on Thursday, May 23, 2024 through the length of time waiting for the service time contained in the queue table, how to calculate it is by subtracting from the time the motorcycle arrives until the motorcycle leaves.
Table 2.3 Service duration data (Thursday)
Motor Ke Plat Duration of Service (minutes)
Ba 3081 lw 89 menit
Ba 2901 HB 87 menit
Ba 6002 AV 90 menit
Ba 2564 LA 31 menit
Ba 5505 LN 62 menit
Ba 3248 TU 77 menit
Ba 2459 LJ 50 menit
Bm 4231 YN 60 menit
Ba 6026 NA 75 menit
By summing up the total arrival distance on Friday, the result is 621 which is then divided by the number of samples, which results in 69 which is the formula for finding the IAT value by entering into the formula -x ln(Ri), namely -69 ln(Ri).
Based on the queue data table and the service length table, we can use it to find a calculation table using a random number obtained using the multiplicative RNG formula with the equation z0 = 12357 a = 43 and modulo m = 1237 which results in an RNG value according to table 3.1.
Tabel 3.1 Random Number Generator
NO Ri
R1 = 0,5481
R2 = 0,5683
R3 = 0,4373
R4 = 0,8059
R5 = 0,6572
R6 = 0,2611
R7 = 0,2279
R8 = 0,8027
R9 = 0,5181
R10 = 0,2821
R11 = 0,1317
R12 = 0,6661
R13 = 0,6434
R14 = 0,6741
R15 = 0,8172
R16 = 0,1438
R17 = 0,1875
R18 = 0,0646
R19 = 0,7809
R20 = 0,5796
R21 = 0,9240
R22 = 0,7324
R23 = 0,4939
R24 = 0,2392
R25 = 0,2894
R26 = 0,4446
R27 = 0,1188
R28 = 0,1095
R29 = 0,7275
R30 = 0,2853
Here the author will explain the parts contained in the data calculation table or table 3 as follows:
Intern Arival Time (IAT)
Is the time between the arrival of the first motor with the second and so on. To get the IAT results we need the value of ln (Ri) which is in the queue data table with the random number that has been obtained.
Arival Time (AT)
In the queue simulation model refers to the time at which each entity arrives or enters the queue. By calculating the sum between the initial arrival and the next one until completion.
Service Time (ST)
Is the duration of time required to serve or respond to a vehicle from the start time to the finish time. By calculating the ln(Ri) value in the service length data table with the random number that has been obtained.
Intro Time Service (ITS)
In the simulation model the queue is the time interval between the arrival of two consecutive customers. In finding the ITS value using the method, for the first ITS value the first row AT value plus the first row ST value. For the second ITS value the first ITS value plus the second line ST value and so on.
Queueing Time(QT)
Refers to the period of time spent by customers in the queue before being served. To get the QT value for the first value is 0 because the customer is served first, for the second QT value by means of the first row ITS value minus the second row AT value.
SP Idle Time (IT)
Refers to the period of time when workers have free time because there is no demand to be served. The IT value is searched by looking at QT if the value contains it means that the IT time is 0 while the QT time is 0 then IT is worth the value of the first line AT.
System Time (St)
Refers to the simulation time that continues to run and is used to organize various events in the simulation. The St value is searched by the first row ITS value minus the first row AT value resulting in the first St value, the second row ITS value minus the second row AT value resulting in the second St value and so on.
For the calculation value of the queuing system, the author will explain the following parts:
Avarage Queueing Time = Rata- rata waktu dalam antrean (AQT)
Average Queueing Time (AQT) is a value that indicates how long, on average, a data or item spends in the queue.
Average Sistem Process Time = Rata- rata waktu proses (Ws)
Average time in system is a value that shows how long, on average, a data or item spends in the system, including time in queue and service time.
Average Queue Length = Rata- rata Panjang antrian
Average Queue Length is a value that indicates how many items or data are, on average, in the queue at any given time.
Average Number in The System = Rata- rata jumlah unit dalam sistem
Average number of units in a system is a value that indicates how many units or items are, on average, in the system at any given time.
Service Point Idle Time = Lama istirahat
Rest time or idle time refers to the period during which a service is inactive or unused. To calculate the percentage of this idle time, it is used.
DISCUSSION
Here we show the results of the calculation of queue data that we have processed the previous data in the table below.
Table 4.1 Queuing data calculation (Friday)
arrival no intern arrival time arrival time service time intro time service queueing time SP idle time system time
3.6077 3.6077 20.4441 24.0518 0 3.6077 20.4441
3.3906 6.9983 19.2135 43.2653 17.0535 0 36.267
4.9655 11.9638 28.1226 71.3879 31.3015 0 59.4241
1.2947 13.2585 7.337 78.7249 58.1294 0 65.4664
2.5186 15.7771 14.272 92.9969 62.9478 0 77.2198
8.0571 23.8342 45.6569 138.6538 69.1627 0 114.8196
8.873 32.7072 50.2808 188.9346 105.9466 0 156.2274
1.3186 34.0258 7.4723 196.4069 154.9088 0 162.3811
3.9455 37.9713 22.3579 218.7648 158.4356 0 180.7935
7.5929 45.5642 43.0267 261.7915 173.2006 0 216.2273
12.1633 57.7275 68.9257 330.7172 204.064 0 272.9897
The calculation of this queuing system will be estimated with the following results:
a. Average Queueing Time = Rata- rata waktu dalam antrean (AQT)
AQT = (Quenty Time) / (Banyak Data) = 1035.15 / (11) = 94,1046 minute
b. Average Sistem Process Time = Rata- rata waktu proses (Ws)
Ws = (Total Sytem Time) / (Banyak Data) = (1362.26) / (11) = 123,842 minute
c. Average Queue Length = Rata- rata Panjang antrian
Lq = (Total Queueing Time) / (Total Time) = (1035.35) / (330.717) = 3,13002 minute
d. Average Number in The System = Rata- rata jumlah unit dalam sistem
Lq = (Sytem Time) / (Total Time) = (1362.26) / (330.717) = 4,11911 minute
e. Service Point Idle Time = Lama istirahat
R.I.T = (Total SP Idle) / (Total Time) = (3,6077) / (330.717) = 0,01091 minute
From the results of the above calculations on Friday, it is obtained that the average time in the queue is 94.10 minutes or about ± 1.5 hours and the process time is around 123.8 or about ± 2 hours and a break time of 0.01 minutes.
Arrival no IAT arrival time service time intro time service queueing time SP idle time system time
6.7854 6.7854 29.7016 36.487 0 6.7854 29.7016
7.3645 14.1499 32.2362 68.7232 22.3371 0 54.5733
6.586 20.7359 28.2889 97.0121 47.9873 0 76.2762
3.3714 24.1073 14.7568 111.7689 72.9048 0 87.6616
32.3868 56.4941 141.7651 253.534 55.2748 0 197.0399
27.9554 84.4495 122.3676 375.9016 169.0845 0 291.4521
45.7503 130.1998 200.2604 576.162 245.7018 0 445.9622
4.13 134.3298 18.0782 594.2402 441.8322 0 459.9104
9.1084 143.4382 39.8699 634.1101 450.802 0 490.6719
1.32 144.7582 5.778 639.8881 489.3519 0 495.1299
Table 4.2 Queuing data calculation (Saturday)
The calculation of this queuing system will be estimated with the following results:
a. Average Queueing Time = Rata- rata waktu dalam antrean (AQT)
AQT = (Quenty Time) / (Banyak Data) = (1995.28) / (10) = 199,528 minute
b. Average Sistem Process Time = Rata- rata waktu proses (Ws)
Ws = (Total Sytem Time) / (Banyak Data) = (2628.38) / (10) = 262,838 minute
c. Average Queue Length = Rata- rata Panjang antrian
Lq = (Total Queueing Time) / (Total Time) = (1995.28) / (639.888) = 3,11816 minute
d. Average Number in The System = Rata- rata jumlah unit dalam sistem
Lq = (Sytem Time) / (Total Time) = (2628,38) / (639.888) = 4,10756 minute
e. Service Point Idle Time = Lama istirahat
R.I.T = (Total SP Idle) / (Total Time) = (6.7854) / (639.888) = 0,0106 minute
Based on the above calculations, it is concluded that on Saturdays the average length of time in the queue is 199.53 minutes or about ± 3.3 hours and the average length of process time is 262.84 minutes ± or about 4.3 hours and the rest time is 0.01 minutes.
Table 4.3 P Queuing data calculation (Thursday)
arrival no IAT arrival time service time intro time service queueing time SP idle time system time
3.1142 3.1142 21.4885 24.6027 0 3.1142 21.4885
7.0542 10.1684 48.6741 73.2768 14.4343 0 63.1084
14.3045 24.4729 98.7014 171.9782 48.8039 0 147.5053
12.3994 36.8723 85.5562 257.5344 135.1059 0 220.6621
8.1058 44.9781 55.93 313.4644 212.5563 0 268.4863
21.3031 66.2812 146.9916 460.456 247.1832 0 394.1748
22.1183 88.3995 152.6163 613.0723 372.0565 0 524.6728
3.1814 91.5809 21.9517 635.024 521.4914 0 543.4431
12.5421 104.123 86.5407 721.5647 530.901 0 617.4417
The calculation of this queuing system will be estimated with the following results:
a. Average Queueing Time = Rata- rata waktu dalam antrean (AQT)
AQT = (Quenty Time) / (Banyak Data) = (2082.53) / (9) = 231,393 minute
b. Average Sistem Process Time = Rata- rata waktu proses (Ws)
Ws = (Total Sytem Time) / (Banyak Data) = (2800.98) / (9) = 311,22 minute
c. Average Queue Length = Rata- rata Panjang antrian
Lq = (Total Queueing Time) / (Total Time) = (2082.53) / (721.565) = 2,88613 minute
d. Average Number in The System = Rata- rata jumlah unit dalam system
Lq = (Sytem Time) / (Total Time) = (2800.98) / (721.565) = 3,88182 minute
e. Service Point Idle Time = Lama istirahat
R.I.T = (Total SP Idle) / (Total Time) = (5) / (626) = 0,00432 minute
Based on the above calculations, it is concluded that on Saturday the average length of time in the queue is 231.39 minutes or about ± 3.8 hours and the average length of process time is 311.22 minutes ± or about 5.1 hours and the rest time is 0.004 minutes.
KESIMPULAN
The results of the analysis from using this queue simulation can be seen the length of the queue in motorcycle service, our suggestions for improvement might be done by adding mechanics in the service or imposing queue limits in a day of work. In this simulation we made observations to one of the motorcycle services in Aur kuning to get sample data. We also took documentation as evidence of direct observation of the field which we started on 17-18 and continued on May 23. In completing this research we used the queue data table, we manipulated and calculated to get the results of the calculation table.
Based on the author's calculations in observations made in three days at different times and the number of different vehicle samples, the longest time in this motor vehicle service activity was on Thursday, May 23, 2024 with a queue time of about ± 3.8 hours and a service process time of about ± 5.1 hours in one sampling observation.
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