Simulating the line balance to provide an improvement plan for optimal production and costing in petrochemical industries

Document Type : Original Article

Authors

1 Department of Industrial Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran

2 Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

3 Department of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran

4 Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran,

10.22091/jemsc.2024.11189.1198

Abstract

This research examines the influential factors and theoretical principles of queuing in balancing production lines. The required information, including the number of active machines on the production line, the level of station idleness, and the waiting time for parts to receive services, has been quantitatively gathered through on-site observations and interviews with managers and supervisors. Process modeling has been developed using the ARENA simulation software version 13, which can identify the current status of tank and heavy product production in terms of queuing and line balance criteria, and its results are analyzed and described. The findings of this research indicate that the addition of a forklift increases costs from 65,902 monetary units to 80,577, equivalent to a 22% increase. However, this change results in a doubling of production compared to the current state; therefore, the extra expenditure due to increased production is satisfactory for the managers of this organization. The significance of this production increase aligns with enhanced productivity, especially considering the greater emphasis placed on production targets.

Keywords

Main Subjects

References

  1. Abolghasemian, M., & Darabi, H. (2018). Simulation based optimization of haulage system of an open-pit mine: Meta modeling approach. Organizational resources management researchs, 8(2), 1-17. http://dorl.net/dor/20.1001.1.22286977.1397.8.2.2.6
  2. Abolghasemian, M., Ghane Kanafi, A., & Daneshmandmehr, M. (2020). A two-phase simulation-based optimization of hauling system in open-pit mine. Iranian journal of management studies, 13(4), 705-732. https://doi.org/10.22059/ijms.2020.294809.673898
  3. Abolghasemian, M., Kanafi, A. G., & Daneshmand-Mehr, M. (2022). Simulation-based multiobjective optimization of open-pit mine haulage system: a modified-NBI method and Meta modeling approach. Complexity, 2022. https://doi.org/10.1155/2022/3540736
  4. Afolalu, S. A., Ikumapayi, O. M., Abdulkareem, A., Emetere, M. E., & Adejumo, O. (2021). A short review on queuing theory as a deterministic tool in sustainable telecommunication system. Materials Today: Proceedings, 44, 2884-2888.‏ https://doi.org/10.1016/j.matpr.2021.01.092
  5. Ahmed, M. A., Al – Khamis, T. M. (2009). Simulation Optimization for an Emergency Department Healthcare Unit in Kuwait, European Journal of Operational Research, 198, 936-942. https://doi.org/10.1016/j.ejor.2008.10.025
  6. Ala, A., Yazdani, M., Ahmadi, M., Poorianasab, A., & Attari, M. Y. N. (2023). An efficient healthcare chain design for resolving the patient scheduling problem: queuing theory and MILP-ASA optimization approach. Annals of Operations Research, 328(1), 3-33.‏ https://doi.org/10.1007/s10479-023-05287-5
  7. Azizi, S., Shakibi, H., Shokri, A., Chitsaz, A., & Yari, M. (2024). Multi-aspect analysis and RSM-based optimization of a novel dual-source electricity and cooling cogeneration system. Applied Energy, 332, 120487. https://doi.org/10.1016/j.apenergy.2022.120487
  8. Barati, I., Ahari, R. M., & Asadpour, M. (2022). A queuing network and Markov chain approach for balancing assembly line: a case study. International Journal of Advanced Operations Management, 14(1), 56-73. https://doi.org/10.1504/IJAOM.2022.122699
  9. Boulas, K. S., Dounias, G. D., & Papadopoulos, C. T. (2023). A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines. Journal of Intelligent Manufacturing, 1-30.‏ https://doi.org/10.1007/s10845-021-01828-6
  10. Damci, A. (2022). Revisiting the concept of natural rhythm of production in line of balance scheduling. International Journal of Construction Management, 22(12), 2335-2344.‏ https://doi.org/10.1080/15623599.2020.1786764
  11. Dengiz, B. (2009). Redesign of PCB Production Line with Simulation and Taguchi Design. Proceedings of the Winter Simulation Conference, 2197 – 2204. https://doi.org/10.1109/WSC.2009.5429646
  12. Dengiz, B., & Akbay, K. S. (2000). Computer Simulation of a PCB Production Line: Meta – Modeling Approach. International Journal Production Economy. 63, 195 – 205. https://doi.org/10.1016/S0925-5273(99)00013-4
  13. Dengiz, B., Bektas, T., Ultanir, A. E. (2006). Simulation Optimization Based DSS Application: A Diamond Tool Production Line in Industry, Simulation Model Practice Theory, 14(3), 296 – 312. https://doi.org/10.1016/j.simpat.2005.07.001
  14. Dengiz, B., İç, Y. T., & Belgin, O. (2016). A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry. Mathematics and Computers in Simulation, 120, 120-128. https://doi.org/10.1016/j.matcom.2015.07.005
  15. Hurrion, R. (1997). An Example of Simulation Optimisation Using a Neural Network Metamodels: Finding the optimum number of Kanbans in a Manufacturing System, Journal of the Operational Research Society, 48(11), 1105 – 1112. https://doi.org/10.1057/palgrave.jors.2600468
  16. Jahangiri, S., Abolghasemian, M., Ghasemi, P., & Chobar, A. P. (2023). Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions. International journal of industrial and systems engineering, 43(1), 1-19. https://doi.org/10.1504/IJISE.2023.128399
  17. Kleijnen, J. P. C., & Sargent, R. G. (2000). A Methodology for Fitting and Validating Metamodels in Simulation. European Journal of Operatioanal Research, 120, 14 – 29. https://doi.org/10.1016/S0377-2217(98)00392-0
  18. Liu, Y., Shen, W., Zhang, C., & Sun, X. (2023). Agent-based simulation and optimization of hybrid flow shop considering multi-skilled workers and fatigue factors. Robotics and Computer-Integrated Manufacturing, 80, 102478. https://doi.org/10.1016/j.rcim.2022.102478
  19. Mas, L., Vilaplana, J., Mateo, J., & Solsona, F. (2022). A queuing theory model for fog computing. The Journal of Supercomputing, 78(8), 11138-11155.‏ https://doi.org/10.1007/s11227-022-04328-3
  20. Meckesheimer, M., Barton, R. R., Simpson, T. W., & Booker, A. J. (2001, September). Computationally inexpensive metamodel assessment strategies. In International design engineering technical conferences and computers and information in engineering conference (Vol. 80227, pp. 191-201). American Society of Mechanical Engineers. https://doi.org/10.1115/DETC2001/DAC-21028
  21. Piermarini, C., & Roma, M. (2024). A Simulation--Based Optimization approach for analyzing the ambulance diversion phenomenon in an Emergency-Department network. arXiv preprint arXiv:2309.00643. https://doi.org/10.48550/arXiv.2309.00643
  22. Samouylov, K., Gaidamaka, Y., & Zaripova, E. (2016, September). Analysis of business process execution time with queueing theory models. In International Conference on Information Technologies and Mathematical Modelling (pp. 315-326). Springer, Cham. https://doi.org/10.1007/978-3-319-44615-8_28
  23. Urban, T. L., & Chiang, W. C. (2016). Designing energy-efficient serial production lines: The unpaced synchronous line-balancing problem. European Journal of Operational Research, 248(3), 789-801. https://doi.org/10.1016/j.ejor.2015.07.015
  24. Yalçınkaya, Ö., & Bayhan, G. M. (2009). Modelling and optimization of average travel time for a metro line by simulation and response surface methodology, European journal of operational research, 196(1), 225-233. https://doi.org/10.1016/j.ejor.2008.03.010
  25. Yang, Q., & Liu, W. (2022). Production line balance optimization design of S company. Manufacturing and Service Operations Management, 3(1), 57-71.‏ https://dx.doi.org/10.23977/msom.2022.030109
  26. Zeinali F., Mahootchi M., Sepehri M M. (2015). Resource Planning in the Emergency Department: A Simulation – Based Meta Modeling Approach, Simulation Modeling Practice and Theory, 53(2), 123-148. https://doi.org/10.1016/j.simpat.2015.02.002
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