Integrating buffer “stock” and buffer loops in an assembly line with conveyors: An automotive case study
Claudio CASTIGLIONE, Erica PASTORE, Arianna ALFIERI
Abstract. The degree of automation in manufacturing systems, along with the characteristics of job handling and transport between workstations, and the product cycle time, determine the most suitable job handling system for each line. Conveyor systems allow for the management of re-entrant flows at the same workstations and more flexible system architecture through carousels, which also serve as a buffer loop to accommodate jobs awaiting machine availability. This paper explores the role of buffer stocks and blocking after service mechanisms, task allocation within the system, and operational characteristics to tackle the assembly line balancing problem in a fully automated assembly line interconnected by conveyors through discrete event simulation. Scenario analysis is employed to assess the impacts of constraining job flows through the conveyors on: fluctuations in throughput from shift to shift, its variability, and the critical WIP threshold for the CONWIP logic.
Keywords
System Architecture, Assembly Lines, Discrete Event Simulation
Published online 9/10/2025, 9 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Claudio CASTIGLIONE, Erica PASTORE, Arianna ALFIERI, Integrating buffer “stock” and buffer loops in an assembly line with conveyors: An automotive case study, Materials Research Proceedings, Vol. 57, pp 512-520, 2025
DOI: https://doi.org/10.21741/9781644903735-60
The article was published as article 60 of the book Italian Manufacturing Association Conference
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] Zhou, L., Jiang, Z., Geng, N., Niu, Y., Cui, F., Liu, K., & Qi, N. (2022). Production and operations management for intelligent manufacturing: A systematic literature review. International Journal of Production Research, 60(2), 808-846 https://doi.org/10.1080/00207543.2021.2017055
[2] Dolgui, A., Sgarbossa, F., & Simonetto, M. (2022). Design and management of assembly systems 4.0: systematic literature review and research agenda. International Journal of Production Research, 60(1), 184-210. https://doi.org/10.1080/00207543.2021.1990433
[3] Wang, H., Lv, L., Li, X., Li, H., Leng, J., Zhang, Y., … & Luo, G. (2023). A safety management approach for Industry 5.0′ s human-centered manufacturing based on digital twin. Journal of Manufacturing Systems, 66, 1-12. https://doi.org/10.1016/j.jmsy.2022.11.013
[4] Cañas, H., Mula, J., Díaz-Madroñero, M., & Campuzano-Bolarín, F. (2021). Implementing industry 4.0 principles. Computers & industrial engineering, 158, 107379. https://doi.org/10.1016/j.cie.2021.107379
[5] Romero-Silva, R., & Shaaban, S. (2019). Influence of unbalanced operation time means and uneven buffer allocation on unreliable merging assembly line efficiency. International Journal of Production Research, 57(6), 1645-1666. https://doi.org/10.1080/00207543.2018.1495344
[6] Boysen, N., Schulze, P., & Scholl, A. Assembly line balancing: What happened in the last fifteen years?. European Journal of Operational Research, 301(3) (2022), 797-814. https://doi.org/10.1016/j.ejor.2021.11.043
[7] Fragapane, G., Ivanov, D., Peron, M., Sgarbossa, F., & Strandhagen, J. O. Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics. Annals of operations research, 308(1) (2022), 125-143. https://doi.org/10.1007/s10479-020-03526-7
[8] Rahman, H. F., Janardhanan, M. N., & Nielsen, P. (2020). An integrated approach for line balancing and AGV scheduling towards smart assembly systems. Assembly Automation, 40(2), 219-234. https://doi.org/10.1108/AA-03-2019-0057
[9] Tiacci, L. (2015). Simultaneous balancing and buffer allocation decisions for the design of mixed-model assembly lines with parallel workstations and stochastic task times. International Journal of Production Economics, 162, 201-215. https://doi.org/10.1016/j.ijpe.2015.01.022
[10] Romero-Silva, R., Santos, J., & Hurtado-Hernández, M. (2024). A conceptual framework of the applicability of production scheduling from a contingency theory approach: addressing the theory-practice gap. Production Planning & Control, 35(3), 262-282. https://doi.org/10.1080/09537287.2022.2076627
[11] Tiacci, L. (2024). Combining balancing, sequencing and buffer allocation decisions to improve the efficiency of mixed-model asynchronous assembly lines. Computers & Industrial Engineering, 194, 110357. https://doi.org/10.1016/j.cie.2024.110357
[12] Konstantinov S, Ahmad M, Ananthanarayan K, Harrison R. The cyber-physical e-machine manufacturing system: Virtual engineering for complete lifecycle support. Procedia CIRP. 2017 Jan 1;63:119-24. https://doi.org/10.1016/j.procir.2017.02.035
[13] Santos CH, De Queiroz JA, Leal F, Montevechi JA. Use of simulation in the industry 4.0 context: Creation of a Digital Twin to optimise decision making on non-automated process. Journal of Simulation. 2022 May 4;16(3):284-97. https://doi.org/10.1080/17477778.2020.1811172
[14] Leng, J., et al. Digital twins-based smart manufacturing system design in Industry 4.0: A review. Journal of manufacturing systems. 2021, 60: 119-137. https://doi.org/10.1016/j.jmsy.2021.05.011
