Analysis of Factors that Influence Automobile Workshop Queue Performance Using Design of Experiments
الموضوعات :
Welly Sugianto
1
(Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, 86400)
Reazul Haq Abdul Haq
2
(Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, 86400)
الکلمات المفتاحية: Queue, Design of Experiments, automobile workshop, categorical, numerical,
ملخص المقالة :
Probability and simulation techniques have been applied to analyze automobile workshop queue performance, but no study has been conducted to identify factors that affect automobile workshop queue performance. It is necessary to identify the factors that influence queue performance to design automobile workshop queue system. This study uses the design of experiments method to investigate the factors that influence queue performance. The number of servers, server area, number of phases, number of workers, and arrival rate are among the numerical factors evaluated. There are two categorical factors to consider: layout type and worker experience. Their effect on queue performance, including queue cost, service time, average customer waiting time, and number of customers, is examined. Additionally, this study seeks to discover appropriate experimental designs. There are three different experimental designs used. The first design is a split plot 2_VI^(7-1) that considers arrival rate as a categorical factor. The second design is a robust design that considers arrival rate as a source of variation. The third design is a full split plot design that considers arrival rate as a numeric factor. According to this study, a full split plot design offers higher accuracy in identifying factors influencing queue performance. The queue performance is significantly affected by the number of servers, phases, workers, arrival rate, and layout. This study paves the way for future studies to determine the optimal point of queue performance.
Aghsami, A., Abazari, S. R., Bakhshi, A., Yazdani, M. A., Jolai, S., & Jolai, F. (2023). A meta-heuristic optimization for a novel mathematical model for minimizing costs and maximizing donor satisfaction in blood supply chains with finite capacity queueing systems. Healthcare Analytics, 3, 100136. https://doi.org/https://doi.org/10.1016/j.health.2023.100136
Almomani, H., & Almutairi, N. (2020). Vehicles Maintenance Workshops Layout and its Management to Reduce Noise Pollution and Improve Maintenance Quality. Journal of Environmental Treatment Techniques, 8(4), 1352–1356.
Anne, M., Sy, P. C., Malabuyoc, F. L. S., Sobrevilla, M. D. M., & Estember, R. D. (2021). A Queuing Theory Approach to Improve Service Quality of Banking Systems : A Case Study of a Bank in Laguna , Philippines. Proceedings of the International Conference on Industrial Engineering and Operations Management, 2703–2714.
Antony, J., Viles, E., Torres, A. F., Paula, T. I. de, Fernandes, M. M., & Cudney, E. A. (2020). Design of experiments in the service industry: a critical literature review and future research directions. The TQM Journal, 32(6), 1159–1175. https://doi.org/10.1108/TQM-02-2020-0026
Asadzadeh, S., Akhavan, B., & Akhavan, B. (2021). Multi-objective optimization of Gas Station performance using response surface methodology. International Journal of Quality and Reliability Management, 38(2), 465–483. https://doi.org/10.1108/IJQRM-06-2019-0181
Ashenafi, A. M., & Geremew, S. (2020). A combined simulation-based taguchi robust design approach for improved parameter design. Industrial Engineering and Management Systems, 19(3), 644–656. https://doi.org/10.7232/iems.2020.19.3.644
Aziziankohan, A., Jolai, F., Khalilzadeh, M., Soltani, R., & Tavakkoli-Moghaddam, R. (2017). Green supply chain management using the queuing theory to handle congestion and reduce energy consumption and emissions from supply chain transportation fleet. Journal of Industrial Engineering and Management, 10(2Special Issue), 213–236. https://doi.org/10.3926/jiem.2170
Bahari, A., Asadi, F., & Moody, B. (2021). Simulation Modeling for Evaluation of the Patients ’ Queue System Performance at Emergency. Research Square, 1(1), 1–22.
Bannikov, D., Sirina, N., & Smolyaninov, A. (2018). Model of The Maintenance And Repair System In Service. Transport Problems, 13(3), 1–10. https://doi.org/10.20858/tp.2018.13.3.1
Blanchet, J. H. (2022). Some open problems in exact simulation of stochastic differential equations. Queueing Systems, 100(3), 509–511. https://doi.org/10.1007/s11134-022-09835-x
Burodo, M. S., Mikailu, I., & Yusuf, G. (2021). An Empirical Analysis of the Queuing Theory and its Application to Customer Satisfaction in a Small and Medium Enterprises ( SMEs ): A Study of Danjalele Enterprise , Funtua , Kastina State. International Journal of Novel Research in Healthcare and Nursing, 8(3), 89–99.
Chen, Y., & Whitt, W. (2022). Applying optimization theory to study extremal GI/GI/1 transient mean waiting times. Queueing Systems, 101(3), 197–220. https://doi.org/10.1007/s11134-021-09725-8
Feitosa, L., Gonçalves, G., Nguyen, T. A., Lee, J. W., & Silva, F. A. (2021). Performance evaluation of message routing strategies in the internet of robotic things using the d/m/c/k/fcfs queuing network. Electronics (Switzerland), 10(21), 1–22. https://doi.org/10.3390/electronics10212626
Franco, C., Herazo-Padilla, N., & Castañeda, J. A. (2022). A queueing Network approach for capacity planning and patient Scheduling: A case study for the COVID-19 vaccination process in Colombia. Vaccine, 40(49), 7073–7086. https://doi.org/https://doi.org/10.1016/j.vaccine.2022.09.079
Galankashi, M. R., Fallahiarezoudar, E., Moazzami, A., Yusof, N. M., & Helmi, S. A. (2016). Performance evaluation of a petrol station queuing system: A simulation-based design of experiments study. Advances in Engineering Software, 92, 15–26. https://doi.org/https://doi.org/10.1016/j.advengsoft.2015.10.004
Glistau, E., Isaias, N., & Machado, C. (2017). Full-Factorial Design of Experiments in Logistics Systems. MultiScience - XXXI. MicroCAD International Multidisciplinary Scientific Conference University of Miskolc, Hungary, April, 1–9. https://doi.org/10.26649/musci.2017.042
Hamzaçebi, C. (2021). Taguchi Method as a Robust Design Tool. In Quality Control - Intelligent Manufacturing, Robust Design and Charts. IntechOpen. https://doi.org/10.5772/intechopen.94908
Huang, J., Mandelbaum, A., & Momčilović, P. (2022). Appointment-driven service systems with many servers. Queueing Systems, 100(3), 529–531. https://doi.org/10.1007/s11134-022-09782-7
Irisbekova, M. (2021). Improving methodology of automobile operating companies activities simulation modeling. E3S Web of Conferences, 05024(05024), 1–8.
Kommula, V. P., Mapfaira, H., Gandure, J., Mashaba, K., Monageng, R., & Samuel, O. (2015). Improving Productivity of a Machine Workshop through Facilities Planning. International Journal of Mining, Metallurgy & Mechanical Engineering (IJMMME) Volume, 3(3), 187–192.
Kondrashova, E. (2021). Optimization of Controlled Queueing Systems : the Case of Car Wash Services TransSiberia 2020 Conference. Transportation Research Procedia, 54(2020), 662–671. https://doi.org/10.1016/j.trpro.2021.02.119
Kothandaraman, D., & Kandaiyan, I. (2023). SS symmetry Analysis of a Heterogeneous Queuing Model with Intermittently Obtainable Servers under a Hybrid. Symmetry, 15, 1–18. https://doi.org/https://doi.org/10.3390/sym15071304
Li, K., Pan, Y., Liu, B., & Cheng, B. (2020). The setting and optimization of quick queue with customer loss. Journal of Industrial and Management Optimization, 16(3), 1539–1553. https://doi.org/10.3934/JIMO.2019016
Litvak, N. (2022). New ways of solving large Markov chains. Queueing Systems, 100(3), 217–219. https://doi.org/10.1007/s11134-022-09821-3
Marit, I. Y., Nursanti, E., & Vitasari, P. (2020). Critical Path Method to Accelerate Automotive Maintenance Duration. International Journal of Scientific & Technology Research, 9(03), 6777–6782. https://doi.org/DOI:10.1088/1757-899X/885/1/012059
Michael K., A., Saheed A., A., & Awaw K., A. (2023). Congestion Problem during Covid-19 in the University College Hospital , Ibadan , Oyo State , Nigeria : An Application of Queuing Theory. International Journal of Mathematics and Statistics Studies, 11(June), 61–66. https://doi.org/https://doi.org/10.37745/ijmss.13/vol11n16166
Moka, S. B., Nazarathy, Y., & Scheinhardt, W. (2023). Diffusion parameters of flows in stable multi-class queueing networks. Queueing Systems, 103(3), 313–346. https://doi.org/10.1007/s11134-022-09869-1
Nguyen, H. Q., & Phung-Duc, T. (2022). Strategic customer behavior and optimal policies in a passenger–taxi double-ended queueing system with multiple access points and nonzero matching times. Queueing Systems, 102(3), 481–508. https://doi.org/10.1007/s11134-022-09786-3
Okaishi, W. Al, Zaarane, A., Slimani, I., & Atouf, I. (2021). A Vehicular Queue Length Measurement System In Real-Time Based On Ssd Network. Sciendo, 22(1), 29–37. https://doi.org/10.2478/ttj-2021-0003
Okonkwo, U. C., Okokpujie, I. P., Odo, B. N., & Fayomi, O. S. I. (2019). Workshop Queue System Modification Through Multi Priority Strategy. Journal of Physics: Conference Series, 1378(2). https://doi.org/10.1088/1742-6596/1378/2/022030
Pang, G., Sarantsev, A., & Suhov, Y. (2022). Birth and death processes in interactive random environments. Queueing Systems, 102(1), 269–307. https://doi.org/10.1007/s11134-022-09855-7
Panta, A. P., Ghimire, R. P., Panthi, D., & Pant, S. R. (2021). Optimization of M / M / s / N Queueing Model with Reneging in a Fuzzy Environment. American Journal of Operations Research, 11, 121–140. https://doi.org/10.4236/ajor.2021.113008
Premono, A., Victor, M., & Sutrisno, H. H. (2020). An experimental study of a car maintenance workshop layout optimization. AIP Conference Proceedings, May, 1–8. https://doi.org/10.1063/5.0000585
Raghuwanshi, P., & Goyal, A. (2015). Evaluation and Improvement of Plant Inventory and Layout Design in Automobile Service Industries. International Journal of Innovative Research in Science, Engineering and Technology, 4(9), 8178–8185. https://doi.org/10.15680/IJIRSET.2015.0409023
Ravner, L., & Sakuma, Y. (2021). Strategic arrivals to a queue with service rate uncertainty. Queueing Systems, 97(3), 303–341. https://doi.org/10.1007/s11134-020-09683-7
Ravner, L., & Wang, J. (2023). Estimating customer delay and tardiness sensitivity from periodic queue length observations. Queueing Systems, 103(3), 241–274. https://doi.org/10.1007/s11134-022-09867-3
Revina, I. V, & Trifonova, E. N. (2021). Car Service Optimization Based on Simulation. Journal of Physics: Conference Series, 1791, 1–11. https://doi.org/10.1088/1742-6596/1791/1/012084
Siregar, N. (2020). Queue System in Automobile Service Sectors. International Journal of Advanced Science and Technology, 29(5), 13365–13378.
Srivastava, S. (2015). Queuing theory in workshop. International Journal of Science, Technology & Management, 04(01), 88–95.
State, K., Adeniran, A., & Burodo, M. S. (2022). Application of Queuing Theory and Management of Waiting Time Using Multiple Server Model : Empirical Evidence From Ahmadu Bello. International Journal of Scientific and Management Research, 5(4), 159–174. https://doi.org/http://doi.org/10.37502/IJSMR.2022.5412
Umair, M., Farooq, M. U., Raza, R. H., Chen, Q., & Abdulhai, B. (2021). Efficient Video-based Vehicle Queue Length Estimation using Computer Vision and Deep Learning for an Urban Traffic Scenario. MDPI, 9, 1–19.
Van Ommeren, J.-K., Baer, N., Mishra, N., & Roy, D. (2020). Batch service systems with heterogeneous servers. Queueing Systems, 95(3), 251–269. https://doi.org/10.1007/s11134-020-09654-y
Vasanthi, J., & Santhi, S. (2022). Application of Queueing Theory for the Improvement of Railway Services. International Journal of Mechanical Engineering, 7(2), 1479–1483.
Vijay Prasad, S., Donthi, R., & Challa, M. K. (2020). The sensitivity analysis of service and waiting costs of a multi server queuing model. IOP Conference Series: Materials Science and Engineering, 993(1). https://doi.org/10.1088/1757-899X/993/1/012107
Wahid, Z., Ming, T. T., & Ahmad, K. (2020). Use of Taguchi Robust Design to Optimize Rubber Glove Process. Journal of Physics: Conference Series, 1489(1). https://doi.org/10.1088/1742-6596/1489/1/012034
Walraevens, J., Van Giel, T., De Vuyst, S., & Wittevrongel, S. (2022). Asymptotics of waiting time distributions in the accumulating priority queue. Queueing Systems, 101(3), 221–244. https://doi.org/10.1007/s11134-022-09839-7
Windarto, Y., Hersant, R., & Putro, E. (2021). Developing Home Service System; Business Process Reengineering for Motorcycle Workshop. Indonesian Journal of Information Systems, 3(2), 94–104. https://doi.org/10.24002/ijis.v3i2.4144
Xiao, L., Xu, S. H., Yao, D. D., & Zhang, H. (2022). Optimal staffing for ticket queues. Queueing Systems, 102(1), 309–351. https://doi.org/10.1007/s11134-022-09854-8
Yaduvanshi, D., Sharma, A., & More, P. V. (2019). Application of Queuing Theory to Optimize Waiting- Time in Hospital Operations. Operation and Supply Chain Management, 12(3), 165–174.
Yom-Tov, G. B., & Chan, C. W. (2021). Balancing admission control, speedup, and waiting in service systems. Queueing Systems, 97(1), 163–219. https://doi.org/10.1007/s11134-021-09685-z
Zychlinski, N. (2023). Applications of fluid models in service operations management. Queueing Systems, 103(1), 161–185. https://doi.org/10.1007/s11134-022-09868-2
